A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes

PubMed Central

Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-01-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed‐batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647–1661, 2017 PMID:28786215

Robust PBPK/PD-Based Model Predictive Control of Blood Glucose.

PubMed

Schaller, Stephan; Lippert, Jorg; Schaupp, Lukas; Pieber, Thomas R; Schuppert, Andreas; Eissing, Thomas

2016-07-01

Automated glucose control (AGC) has not yet reached the point where it can be applied clinically [3]. Challenges are accuracy of subcutaneous (SC) glucose sensors, physiological lag times, and both inter- and intraindividual variability. To address above issues, we developed a novel scheme for MPC that can be applied to AGC. An individualizable generic whole-body physiology-based pharmacokinetic and dynamics (PBPK/PD) model of the glucose, insulin, and glucagon metabolism has been used as the predictive kernel. The high level of mechanistic detail represented by the model takes full advantage of the potential of MPC and may make long-term prediction possible as it captures at least some relevant sources of variability [4]. Robustness against uncertainties was increased by a control cascade relying on proportional-integrative derivative-based offset control. The performance of this AGC scheme was evaluated in silico and retrospectively using data from clinical trials. This analysis revealed that our approach handles sensor noise with a MARD of 10%-14%, and model uncertainties and disturbances. The results suggest that PBPK/PD models are well suited for MPC in a glucose control setting, and that their predictive power in combination with the integrated database-driven (a priori individualizable) model framework will help overcome current challenges in the development of AGC systems. This study provides a new, generic, and robust mechanistic approach to AGC using a PBPK platform with extensive a priori (database) knowledge for individualization.

Robust predictive control with optimal load tracking for critical applications. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tse, J.; Bentsman, J.; Miller, N.

1994-09-01

This report derives a multi-input multi-output (MIMO) version of a two-degree-of-freedom receding-horizon control law based on mixed H{sub 2}/H{infinity} minimization. First, the integrand in the frequency domain representation of the MIMO performance criterion is decomposed into disturbance and reference spectra. Then the controller is derived which minimizes the peak of the disturbance spectrum and the integral of the reference spectrum on the unit circle. The resulting two-degree-of-freedom MIMO control strategy, referred to as the minimax predictive multivariable control (MPC), is shown to have worst-case-disturbance-rejection and robust-stability properties superior to those of purely H{sub 2}-optimal controllers, such as Generalized Predictive Controlmore » (GPC), for identical horizons. An attractive feature of the receding horizon structure of MPC is that it can, in ways similar to GPC, directly incorporate input constraints and pre-programmed reference inputs, which are nontrivial tasks in the standard H{infinity} design.« less

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

PubMed

Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-11-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.

Design and analysis of a model predictive controller for active queue management.

PubMed

Wang, Ping; Chen, Hong; Yang, Xiaoping; Ma, Yan

2012-01-01

Model predictive (MP) control as a novel active queue management (AQM) algorithm in dynamic computer networks is proposed. According to the predicted future queue length in the data buffer, early packets at the router are dropped reasonably by the MPAQM controller so that the queue length reaches the desired value with minimal tracking error. The drop probability is obtained by optimizing the network performance. Further, randomized algorithms are applied to analyze the robustness of MPAQM successfully, and also to provide the stability domain of systems with uncertain network parameters. The performances of MPAQM are evaluated through a series of simulations in NS2. The simulation results show that the MPAQM algorithm outperforms RED, PI, and REM algorithms in terms of stability, disturbance rejection, and robustness. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Robustness and cognition in stabilization problem of dynamical systems based on asymptotic methods

NASA Astrophysics Data System (ADS)

Dubovik, S. A.; Kabanov, A. A.

2017-01-01

The problem of synthesis of stabilizing systems based on principles of cognitive (logical-dynamic) control for mobile objects used under uncertain conditions is considered. This direction in control theory is based on the principles of guaranteeing robust synthesis focused on worst-case scenarios of the controlled process. The guaranteeing approach is able to provide functioning of the system with the required quality and reliability only at sufficiently low disturbances and in the absence of large deviations from some regular features of the controlled process. The main tool for the analysis of large deviations and prediction of critical states here is the action functional. After the forecast is built, the choice of anti-crisis control is the supervisory control problem that optimizes the control system in a normal mode and prevents escape of the controlled process in critical states. An essential aspect of the approach presented here is the presence of a two-level (logical-dynamic) control: the input data are used not only for generating of synthesized feedback (local robust synthesis) in advance (off-line), but also to make decisions about the current (on-line) quality of stabilization in the global sense. An example of using the presented approach for the problem of development of the ship tilting prediction system is considered.

Circadian Phase Resetting via Single and Multiple Control Targets

PubMed Central

Bagheri, Neda; Stelling, Jörg; Doyle, Francis J.

2008-01-01

Circadian entrainment is necessary for rhythmic physiological functions to be appropriately timed over the 24-hour day. Disruption of circadian rhythms has been associated with sleep and neuro-behavioral impairments as well as cancer. To date, light is widely accepted to be the most powerful circadian synchronizer, motivating its use as a key control input for phase resetting. Through sensitivity analysis, we identify additional control targets whose individual and simultaneous manipulation (via a model predictive control algorithm) out-perform the open-loop light-based phase recovery dynamics by nearly 3-fold. We further demonstrate the robustness of phase resetting by synchronizing short- and long-period mutant phenotypes to the 24-hour environment; the control algorithm is robust in the presence of model mismatch. These studies prove the efficacy and immediate application of model predictive control in experimental studies and medicine. In particular, maintaining proper circadian regulation may significantly decrease the chance of acquiring chronic illness. PMID:18795146

Control of Uncertain Systems under Constraints: Switching Horizon Predictive Control of Persistently Disturbed Input-Saturated Plants

DTIC Science & Technology

2006-12-01

on at any time from a family of candidate feedback-gains so as to control a discrete- time input-saturated LTI system possibly subject to persistent... times robustness Mosca, E. (2006) Control of Uncertain Systems under Constraints: Switching Horizon Predictive Control of Persistently Disturbed...feedback controls u = f(x̂) (3) so as to ensure, under suitable conditions, stability in the noiseless case as well as finite l∞-induced gain of the

A Framework for Information Theoretic Cooperative Sensing and Predictive Control

DTIC Science & Technology

2012-09-11

Miroslav Barić and Francesco Borelli , Decentralized Robust Control Invariance for a Network of Integrators, Proceeding of American Control...from http: //www.mpc.berkeley.edu. P4 Miroslav Barić and Francesco Borelli , Distributed Averaging with Flow Constraints, Proceeding of American Control

RSRE: RNA structural robustness evaluator

PubMed Central

Shu, Wenjie; Zheng, Zhiqiang; Wang, Shengqi

2007-01-01

Biological robustness, defined as the ability to maintain stable functioning in the face of various perturbations, is an important and fundamental topic in current biology, and has become a focus of numerous studies in recent years. Although structural robustness has been explored in several types of RNA molecules, the origins of robustness are still controversial. Computational analysis results are needed to make up for the lack of evidence of robustness in natural biological systems. The RNA structural robustness evaluator (RSRE) web server presented here provides a freely available online tool to quantitatively evaluate the structural robustness of RNA based on the widely accepted definition of neutrality. Several classical structure comparison methods are employed; five randomization methods are implemented to generate control sequences; sub-optimal predicted structures can be optionally utilized to mitigate the uncertainty of secondary structure prediction. With a user-friendly interface, the web application is easy to use. Intuitive illustrations are provided along with the original computational results to facilitate analysis. The RSRE will be helpful in the wide exploration of RNA structural robustness and will catalyze our understanding of RNA evolution. The RSRE web server is freely available at http://biosrv1.bmi.ac.cn/RSRE/ or http://biotech.bmi.ac.cn/RSRE/. PMID:17567615

Robust model predictive control for multi-step short range spacecraft rendezvous

NASA Astrophysics Data System (ADS)

Zhu, Shuyi; Sun, Ran; Wang, Jiaolong; Wang, Jihe; Shao, Xiaowei

2018-07-01

This work presents a robust model predictive control (MPC) approach for the multi-step short range spacecraft rendezvous problem. During the specific short range phase concerned, the chaser is supposed to be initially outside the line-of-sight (LOS) cone. Therefore, the rendezvous process naturally includes two steps: the first step is to transfer the chaser into the LOS cone and the second step is to transfer the chaser into the aimed region with its motion confined within the LOS cone. A novel MPC framework named after Mixed MPC (M-MPC) is proposed, which is the combination of the Variable-Horizon MPC (VH-MPC) framework and the Fixed-Instant MPC (FI-MPC) framework. The M-MPC framework enables the optimization for the two steps to be implemented jointly rather than to be separated factitiously, and its computation workload is acceptable for the usually low-power processors onboard spacecraft. Then considering that disturbances including modeling error, sensor noise and thrust uncertainty may induce undesired constraint violations, a robust technique is developed and it is attached to the above M-MPC framework to form a robust M-MPC approach. The robust technique is based on the chance-constrained idea, which ensures that constraints can be satisfied with a prescribed probability. It improves the robust technique proposed by Gavilan et al., because it eliminates the unnecessary conservativeness by explicitly incorporating known statistical properties of the navigation uncertainty. The efficacy of the robust M-MPC approach is shown in a simulation study.

A Reconfiguration Scheme for Accommodating Actuator Failures in Multi-Input, Multi-Output Flight Control Systems

NASA Technical Reports Server (NTRS)

Siwakosit, W.; Hess, R. A.; Bacon, Bart (Technical Monitor); Burken, John (Technical Monitor)

2000-01-01

A multi-input, multi-output reconfigurable flight control system design utilizing a robust controller and an adaptive filter is presented. The robust control design consists of a reduced-order, linear dynamic inversion controller with an outer-loop compensation matrix derived from Quantitative Feedback Theory (QFT). A principle feature of the scheme is placement of the adaptive filter in series with the QFT compensator thus exploiting the inherent robustness of the nominal flight control system in the presence of plant uncertainties. An example of the scheme is presented in a pilot-in-the-loop computer simulation using a simplified model of the lateral-directional dynamics of the NASA F18 High Angle of Attack Research Vehicle (HARV) that included nonlinear anti-wind up logic and actuator limitations. Prediction of handling qualities and pilot-induced oscillation tendencies in the presence of these nonlinearities is included in the example.

Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems

DOE PAGES

Alamaniotis, Miltiadis; Agarwal, Vivek

2014-04-01

Anticipatory control systems are a class of systems whose decisions are based on predictions for the future state of the system under monitoring. Anticipation denotes intelligence and is an inherent property of humans that make decisions by projecting in future. Likewise, artificially intelligent systems equipped with predictive functions may be utilized for anticipating future states of complex systems, and therefore facilitate automated control decisions. Anticipatory control of complex energy systems is paramount to their normal and safe operation. In this paper a new intelligent methodology integrating fuzzy inference with support vector regression is introduced. Our proposed methodology implements an anticipatorymore » system aiming at controlling energy systems in a robust way. Initially a set of support vector regressors is adopted for making predictions over critical system parameters. Furthermore, the predicted values are fed into a two stage fuzzy inference system that makes decisions regarding the state of the energy system. The inference system integrates the individual predictions into a single one at its first stage, and outputs a decision together with a certainty factor computed at its second stage. The certainty factor is an index of the significance of the decision. The proposed anticipatory control system is tested on a real world set of data obtained from a complex energy system, describing the degradation of a turbine. Results exhibit the robustness of the proposed system in controlling complex energy systems.« less

Model of brain activation predicts the neural collective influence map of the brain

PubMed Central

Morone, Flaviano; Roth, Kevin; Min, Byungjoon; Makse, Hernán A.

2017-01-01

Efficient complex systems have a modular structure, but modularity does not guarantee robustness, because efficiency also requires an ingenious interplay of the interacting modular components. The human brain is the elemental paradigm of an efficient robust modular system interconnected as a network of networks (NoN). Understanding the emergence of robustness in such modular architectures from the interconnections of its parts is a longstanding challenge that has concerned many scientists. Current models of dependencies in NoN inspired by the power grid express interactions among modules with fragile couplings that amplify even small shocks, thus preventing functionality. Therefore, we introduce a model of NoN to shape the pattern of brain activations to form a modular environment that is robust. The model predicts the map of neural collective influencers (NCIs) in the brain, through the optimization of the influence of the minimal set of essential nodes responsible for broadcasting information to the whole-brain NoN. Our results suggest intervention protocols to control brain activity by targeting influential neural nodes predicted by network theory. PMID:28351973

Multi-model predictive control based on LMI: from the adaptation of the state-space model to the analytic description of the control law

NASA Astrophysics Data System (ADS)

Falugi, P.; Olaru, S.; Dumur, D.

2010-08-01

This article proposes an explicit robust predictive control solution based on linear matrix inequalities (LMIs). The considered predictive control strategy uses different local descriptions of the system dynamics and uncertainties and thus allows the handling of less conservative input constraints. The computed control law guarantees constraint satisfaction and asymptotic stability. The technique is effective for a class of nonlinear systems embedded into polytopic models. A detailed discussion of the procedures which adapt the partition of the state space is presented. For the practical implementation the construction of suitable (explicit) descriptions of the control law are described upon concrete algorithms.

Absolute Stability Analysis of a Phase Plane Controlled Spacecraft

NASA Technical Reports Server (NTRS)

Jang, Jiann-Woei; Plummer, Michael; Bedrossian, Nazareth; Hall, Charles; Jackson, Mark; Spanos, Pol

2010-01-01

Many aerospace attitude control systems utilize phase plane control schemes that include nonlinear elements such as dead zone and ideal relay. To evaluate phase plane control robustness, stability margin prediction methods must be developed. Absolute stability is extended to predict stability margins and to define an abort condition. A constrained optimization approach is also used to design flex filters for roll control. The design goal is to optimize vehicle tracking performance while maintaining adequate stability margins. Absolute stability is shown to provide satisfactory stability constraints for the optimization.

Hybrid robust predictive optimization method of power system dispatch

DOEpatents

Chandra, Ramu Sharat [Niskayuna, NY; Liu, Yan [Ballston Lake, NY; Bose, Sumit [Niskayuna, NY; de Bedout, Juan Manuel [West Glenville, NY

2011-08-02

A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

SU-F-R-51: Radiomics in CT Perfusion Maps of Head and Neck Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nesteruk, M; Riesterer, O; Veit-Haibach, P

2016-06-15

Purpose: The aim of this study was to test the predictive value of radiomics features of CT perfusion (CTP) for tumor control, based on a preselection of radiomics features in a robustness study. Methods: 11 patients with head and neck cancer (HNC) and 11 patients with lung cancer were included in the robustness study to preselect stable radiomics parameters. Data from 36 HNC patients treated with definitive radiochemotherapy (median follow-up 30 months) was used to build a predictive model based on these parameters. All patients underwent pre-treatment CTP. 315 texture parameters were computed for three perfusion maps: blood volume, bloodmore » flow and mean transit time. The variability of texture parameters was tested with respect to non-standardizable perfusion computation factors (noise level and artery contouring) using intraclass correlation coefficients (ICC). The parameter with the highest ICC in the correlated group of parameters (inter-parameter Spearman correlations) was tested for its predictive value. The final model to predict tumor control was built using multivariate Cox regression analysis with backward selection of the variables. For comparison, a predictive model based on tumor volume was created. Results: Ten parameters were found to be stable in both HNC and lung cancer regarding potentially non-standardizable factors after the correction for inter-parameter correlations. In the multivariate backward selection of the variables, blood flow entropy showed a highly significant impact on tumor control (p=0.03) with concordance index (CI) of 0.76. Blood flow entropy was significantly lower in the patient group with controlled tumors at 18 months (p<0.1). The new model showed a higher concordance index compared to the tumor volume model (CI=0.68). Conclusion: The preselection of variables in the robustness study allowed building a predictive radiomics-based model of tumor control in HNC despite a small patient cohort. This model was found to be superior to the volume-based model. The project was supported by the KFSP Tumor Oxygenation of the University of Zurich, by a grant of the Center for Clinical Research, University and University Hospital Zurich and by a research grant from Merck (Schweiz) AG.« less

Using spatial information about recurrence risk for robust optimization of dose-painting prescription functions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bender, Edward T.

Purpose: To develop a robust method for deriving dose-painting prescription functions using spatial information about the risk for disease recurrence. Methods: Spatial distributions of radiobiological model parameters are derived from distributions of recurrence risk after uniform irradiation. These model parameters are then used to derive optimal dose-painting prescription functions given a constant mean biologically effective dose. Results: An estimate for the optimal dose distribution can be derived based on spatial information about recurrence risk. Dose painting based on imaging markers that are moderately or poorly correlated with recurrence risk are predicted to potentially result in inferior disease control when comparedmore » the same mean biologically effective dose delivered uniformly. A robust optimization approach may partially mitigate this issue. Conclusions: The methods described here can be used to derive an estimate for a robust, patient-specific prescription function for use in dose painting. Two approximate scaling relationships were observed: First, the optimal choice for the maximum dose differential when using either a linear or two-compartment prescription function is proportional to R, where R is the Pearson correlation coefficient between a given imaging marker and recurrence risk after uniform irradiation. Second, the predicted maximum possible gain in tumor control probability for any robust optimization technique is nearly proportional to the square of R.« less

Modeling and sliding mode predictive control of the ultra-supercritical boiler-turbine system with uncertainties and input constraints.

PubMed

Tian, Zhen; Yuan, Jingqi; Zhang, Xiang; Kong, Lei; Wang, Jingcheng

2018-05-01

The coordinated control system (CCS) serves as an important role in load regulation, efficiency optimization and pollutant reduction for coal-fired power plants. The CCS faces with tough challenges, such as the wide-range load variation, various uncertainties and constraints. This paper aims to improve the load tacking ability and robustness for boiler-turbine units under wide-range operation. To capture the key dynamics of the ultra-supercritical boiler-turbine system, a nonlinear control-oriented model is developed based on mechanism analysis and model reduction techniques, which is validated with the history operation data of a real 1000 MW unit. To simultaneously address the issues of uncertainties and input constraints, a discrete-time sliding mode predictive controller (SMPC) is designed with the dual-mode control law. Moreover, the input-to-state stability and robustness of the closed-loop system are proved. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves good tracking performance, disturbance rejection ability and compatibility to input constraints. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Performance and robustness of hybrid model predictive control for controllable dampers in building models

NASA Astrophysics Data System (ADS)

Johnson, Erik A.; Elhaddad, Wael M.; Wojtkiewicz, Steven F.

2016-04-01

A variety of strategies have been developed over the past few decades to determine controllable damping device forces to mitigate the response of structures and mechanical systems to natural hazards and other excitations. These "smart" damping devices produce forces through passive means but have properties that can be controlled in real time, based on sensor measurements of response across the structure, to dramatically reduce structural motion by exploiting more than the local "information" that is available to purely passive devices. A common strategy is to design optimal damping forces using active control approaches and then try to reproduce those forces with the smart damper. However, these design forces, for some structures and performance objectives, may achieve high performance by selectively adding energy, which cannot be replicated by a controllable damping device, causing the smart damper performance to fall far short of what an active system would provide. The authors have recently demonstrated that a model predictive control strategy using hybrid system models, which utilize both continuous and binary states (the latter to capture the switching behavior between dissipative and non-dissipative forces), can provide reductions in structural response on the order of 50% relative to the conventional clipped-optimal design strategy. This paper explores the robustness of this newly proposed control strategy through evaluating controllable damper performance when the structure model differs from the nominal one used to design the damping strategy. Results from the application to a two-degree-of-freedom structure model confirms the robustness of the proposed strategy.

Mixed H2/H∞ distributed robust model predictive control for polytopic uncertain systems subject to actuator saturation and missing measurements

NASA Astrophysics Data System (ADS)

Song, Yan; Fang, Xiaosheng; Diao, Qingda

2016-03-01

In this paper, we discuss the mixed H2/H∞ distributed robust model predictive control problem for polytopic uncertain systems subject to randomly occurring actuator saturation and packet loss. The global system is decomposed into several subsystems, and all the subsystems are connected by a fixed topology network, which is the definition for the packet loss among the subsystems. To better use the successfully transmitted information via Internet, both the phenomena of actuator saturation and packet loss resulting from the limitation of the communication bandwidth are taken into consideration. A novel distributed controller model is established to account for the actuator saturation and packet loss in a unified representation by using two sets of Bernoulli distributed white sequences with known conditional probabilities. With the nonlinear feedback control law represented by the convex hull of a group of linear feedback laws, the distributed controllers for subsystems are obtained by solving an linear matrix inequality (LMI) optimisation problem. Finally, numerical studies demonstrate the effectiveness of the proposed techniques.

Robust predictive cruise control for commercial vehicles

NASA Astrophysics Data System (ADS)

Junell, Jaime; Tumer, Kagan

2013-10-01

In this paper we explore learning-based predictive cruise control and the impact of this technology on increasing fuel efficiency for commercial trucks. Traditional cruise control is wasteful when maintaining a constant velocity over rolling hills. Predictive cruise control (PCC) is able to look ahead at future road conditions and solve for a cost-effective course of action. Model- based controllers have been implemented in this field but cannot accommodate many complexities of a dynamic environment which includes changing road and vehicle conditions. In this work, we focus on incorporating a learner into an already successful model- based predictive cruise controller in order to improve its performance. We explore back propagating neural networks to predict future errors then take actions to prevent said errors from occurring. The results show that this approach improves the model based PCC by up to 60% under certain conditions. In addition, we explore the benefits of classifier ensembles to further improve the gains due to intelligent cruise control.

Active member control of a precision structure with an H(infinity) performance objective

NASA Technical Reports Server (NTRS)

Fanson, J. L.; Chu, C.-C.; Smith, R. S.; Anderson, E. H.

1990-01-01

This paper addresses the noncollocated control of active structures using active structural elements. A top level architecture for active structures is presented, and issues pertaining to robust control of structures are discussed. Controllers optimized for an H sub inf performance specification are implemented on a test structure and the results are compared with analytical predictions. Directions for further research are identified.

Aeroservoelastic Uncertainty Model Identification from Flight Data

NASA Technical Reports Server (NTRS)

Brenner, Martin J.

2001-01-01

Uncertainty modeling is a critical element in the estimation of robust stability margins for stability boundary prediction and robust flight control system development. There has been a serious deficiency to date in aeroservoelastic data analysis with attention to uncertainty modeling. Uncertainty can be estimated from flight data using both parametric and nonparametric identification techniques. The model validation problem addressed in this paper is to identify aeroservoelastic models with associated uncertainty structures from a limited amount of controlled excitation inputs over an extensive flight envelope. The challenge to this problem is to update analytical models from flight data estimates while also deriving non-conservative uncertainty descriptions consistent with the flight data. Multisine control surface command inputs and control system feedbacks are used as signals in a wavelet-based modal parameter estimation procedure for model updates. Transfer function estimates are incorporated in a robust minimax estimation scheme to get input-output parameters and error bounds consistent with the data and model structure. Uncertainty estimates derived from the data in this manner provide an appropriate and relevant representation for model development and robust stability analysis. This model-plus-uncertainty identification procedure is applied to aeroservoelastic flight data from the NASA Dryden Flight Research Center F-18 Systems Research Aircraft.

Sensitivity of Space Station alpha joint robust controller to structural modal parameter variations

NASA Technical Reports Server (NTRS)

Kumar, Renjith R.; Cooper, Paul A.; Lim, Tae W.

1991-01-01

The photovoltaic array sun tracking control system of Space Station Freedom is described. A synthesis procedure for determining optimized values of the design variables of the control system is developed using a constrained optimization technique. The synthesis is performed to provide a given level of stability margin, to achieve the most responsive tracking performance, and to meet other design requirements. Performance of the baseline design, which is synthesized using predicted structural characteristics, is discussed and the sensitivity of the stability margin is examined for variations of the frequencies, mode shapes and damping ratios of dominant structural modes. The design provides enough robustness to tolerate a sizeable error in the predicted modal parameters. A study was made of the sensitivity of performance indicators as the modal parameters of the dominant modes vary. The design variables are resynthesized for varying modal parameters in order to achieve the most responsive tracking performance while satisfying the design requirements. This procedure of reoptimization design parameters would be useful in improving the control system performance if accurate model data are provided.

Predictive functional control for active queue management in congested TCP/IP networks.

PubMed

Bigdeli, N; Haeri, M

2009-01-01

Predictive functional control (PFC) as a new active queue management (AQM) method in dynamic TCP networks supporting explicit congestion notification (ECN) is proposed. The ability of the controller in handling system delay along with its simplicity and low computational load makes PFC a privileged AQM method in the high speed networks. Besides, considering the disturbance term (which represents model/process mismatches, external disturbances, and existing noise) in the control formulation adds some level of robustness into the PFC-AQM controller. This is an important and desired property in the control of dynamically-varying computer networks. In this paper, the controller is designed based on a small signal linearized fluid-flow model of the TCP/AQM networks. Then, closed-loop transfer function representation of the system is derived to analyze the robustness with respect to the network and controller parameters. The analytical as well as the packet-level ns-2 simulation results show the out-performance of the developed controller for both queue regulation and resource utilization. Fast response, low queue fluctuations (and consequently low delay jitter), high link utilization, good disturbance rejection, scalability, and low packet marking probability are other features of the developed method with respect to other well-known AQM methods such as RED, PI, and REM which are also simulated for comparison.

Two challenges in embedded systems design: predictability and robustness.

PubMed

Henzinger, Thomas A

2008-10-28

I discuss two main challenges in embedded systems design: the challenge to build predictable systems, and that to build robust systems. I suggest how predictability can be formalized as a form of determinism, and robustness as a form of continuity.

Comparisons of Robustness and Sensitivity between Cancer and Normal Cells by Microarray Data

PubMed Central

Chu, Liang-Hui; Chen, Bor-Sen

2008-01-01

Robustness is defined as the ability to uphold performance in face of perturbations and uncertainties, and sensitivity is a measure of the system deviations generated by perturbations to the system. While cancer appears as a robust but fragile system, few computational and quantitative evidences demonstrate robustness tradeoffs in cancer. Microarrays have been widely applied to decipher gene expression signatures in human cancer research, and quantification of global gene expression profiles facilitates precise prediction and modeling of cancer in systems biology. We provide several efficient computational methods based on system and control theory to compare robustness and sensitivity between cancer and normal cells by microarray data. Measurement of robustness and sensitivity by linear stochastic model is introduced in this study, which shows oscillations in feedback loops of p53 and demonstrates robustness tradeoffs that cancer is a robust system with some extreme fragilities. In addition, we measure sensitivity of gene expression to perturbations in other gene expression and kinetic parameters, discuss nonlinear effects in feedback loops of p53 and extend our method to robustness-based cancer drug design. PMID:19259409

Robust shrinking ellipsoid model predictive control for linear parameter varying system

PubMed Central

Yan, Yan

2017-01-01

In this paper, a new off-line model predictive control strategy is presented for a kind of linear parameter varying system with polytopic uncertainty. A nest of shrinking ellipsoids is constructed by solving linear matrix inequality. By splitting the objective function into two parts, the proposed strategy moves most computations off-line. The on-line computation is only calculating the current control to assure the system shrinking into the smaller ellipsoid. With the proposed formulation, the stability of the closed system is proved, followed with two numerical examples to demonstrate the proposed method’s effectiveness in the end. PMID:28575028

Transgression as addiction: religiosity and moral disapproval as predictors of perceived addiction to pornography.

PubMed

Grubbs, Joshua B; Exline, Julie J; Pargament, Kenneth I; Hook, Joshua N; Carlisle, Robert D

2015-01-01

Perceived addiction to Internet pornography is increasingly a focus of empirical attention. The present study examined the role that religious belief and moral disapproval of pornography use play in the experience of perceived addiction to Internet pornography. Results from two studies in undergraduate samples (Study 1, N = 331; Study 2, N = 97) indicated that there was a robust positive relationship between religiosity and perceived addiction to pornography and that this relationship was mediated by moral disapproval of pornography use. These results persisted even when actual use of pornography was controlled. Furthermore, although religiosity was negatively predictive of acknowledging any pornography use, among pornography users, religiosity was unrelated to actual levels of use. A structural equation model from a web-based sample of adults (Study 3, N = 208) revealed similar results. Specifically, religiosity was robustly predictive of perceived addiction, even when relevant covariates (e.g., trait self-control, socially desirable responding, neuroticism, use of pornography) were held constant. In sum, the present study indicated that religiosity and moral disapproval of pornography use were robust predictors of perceived addiction to Internet pornography while being unrelated to actual levels of use among pornography consumers.

Set-membership fault detection under noisy environment with application to the detection of abnormal aircraft control surface positions

NASA Astrophysics Data System (ADS)

El Houda Thabet, Rihab; Combastel, Christophe; Raïssi, Tarek; Zolghadri, Ali

2015-09-01

The paper develops a set membership detection methodology which is applied to the detection of abnormal positions of aircraft control surfaces. Robust and early detection of such abnormal positions is an important issue for early system reconfiguration and overall optimisation of aircraft design. In order to improve fault sensitivity while ensuring a high level of robustness, the method combines a data-driven characterisation of noise and a model-driven approach based on interval prediction. The efficiency of the proposed methodology is illustrated through simulation results obtained based on data recorded in several flight scenarios of a highly representative aircraft benchmark.

Active control strategy for the running attitude of high-speed train under strong crosswind condition

NASA Astrophysics Data System (ADS)

Li, Decang; Meng, Jianjun; Bai, Huan; Xu, Ruxun

2018-07-01

This paper focuses on the safety of high-speed trains under strong crosswind conditions. A new active control strategy is proposed based on the adaptive predictive control theory. The new control strategy aims at adjusting the attitudes of a train by controlling the new-type intelligent giant magnetostrictive actuator (GMA). It combined adaptive control with dynamic matrix control; parameters of predictive controller was real-time adjusted by online distinguishing to enhance the robustness of the control algorithm. On this basis, a correction control algorithm is also designed to regulate the parameters of predictive controller based on the step response of a controlled objective. Finally, the simulation results show that the proposed control strategy can adjust the running attitudes of high-speed trains under strong crosswind conditions; they also indicate that the new active control strategy is effective and applicable in improving the safety performance of a train based on a host-target computer technology provided by Matlab/Simulink.

Effortful Control in "Hot" and "Cool" Tasks Differentially Predicts Children's Behavior Problems and Academic Performance

ERIC Educational Resources Information Center

Kim, Sanghag; Nordling, Jamie Koenig; Yoon, Jeung Eun; Boldt, Lea J.; Kochanska, Grazyna

2013-01-01

Effortful control (EC), the capacity to deliberately suppress a dominant response and perform a subdominant response, rapidly developing in toddler and preschool age, has been shown to be a robust predictor of children's adjustment. Not settled, however, is whether a view of EC as a heterogeneous rather than unidimensional construct may offer…

Robust Feedback Control of Flow Induced Structural Radiation of Sound

NASA Technical Reports Server (NTRS)

Heatwole, Craig M.; Bernhard, Robert J.; Franchek, Matthew A.

1997-01-01

A significant component of the interior noise of aircraft and automobiles is a result of turbulent boundary layer excitation of the vehicular structure. In this work, active robust feedback control of the noise due to this non-predictable excitation is investigated. Both an analytical model and experimental investigations are used to determine the characteristics of the flow induced structural sound radiation problem. The problem is shown to be broadband in nature with large system uncertainties associated with the various operating conditions. Furthermore the delay associated with sound propagation is shown to restrict the use of microphone feedback. The state of the art control methodologies, IL synthesis and adaptive feedback control, are evaluated and shown to have limited success for solving this problem. A robust frequency domain controller design methodology is developed for the problem of sound radiated from turbulent flow driven plates. The control design methodology uses frequency domain sequential loop shaping techniques. System uncertainty, sound pressure level reduction performance, and actuator constraints are included in the design process. Using this design method, phase lag was added using non-minimum phase zeros such that the beneficial plant dynamics could be used. This general control approach has application to lightly damped vibration and sound radiation problems where there are high bandwidth control objectives requiring a low controller DC gain and controller order.

Predictive control strategies for wind turbine system based on permanent magnet synchronous generator.

PubMed

Maaoui-Ben Hassine, Ikram; Naouar, Mohamed Wissem; Mrabet-Bellaaj, Najiba

2016-05-01

In this paper, Model Predictive Control and Dead-beat predictive control strategies are proposed for the control of a PMSG based wind energy system. The proposed MPC considers the model of the converter-based system to forecast the possible future behavior of the controlled variables. It allows selecting the voltage vector to be applied that leads to a minimum error by minimizing a predefined cost function. The main features of the MPC are low current THD and robustness against parameters variations. The Dead-beat predictive control is based on the system model to compute the optimum voltage vector that ensures zero-steady state error. The optimum voltage vector is then applied through Space Vector Modulation (SVM) technique. The main advantages of the Dead-beat predictive control are low current THD and constant switching frequency. The proposed control techniques are presented and detailed for the control of back-to-back converter in a wind turbine system based on PMSG. Simulation results (under Matlab-Simulink software environment tool) and experimental results (under developed prototyping platform) are presented in order to show the performances of the considered control strategies. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Experimental Validation of L1 Adaptive Control: Rohrs' Counterexample in Flight

NASA Technical Reports Server (NTRS)

Xargay, Enric; Hovakimyan, Naira; Dobrokhodov, Vladimir; Kaminer, Issac; Kitsios, Ioannis; Cao, Chengyu; Gregory, Irene M.; Valavani, Lena

2010-01-01

The paper presents new results on the verification and in-flight validation of an L1 adaptive flight control system, and proposes a general methodology for verification and validation of adaptive flight control algorithms. The proposed framework is based on Rohrs counterexample, a benchmark problem presented in the early 80s to show the limitations of adaptive controllers developed at that time. In this paper, the framework is used to evaluate the performance and robustness characteristics of an L1 adaptive control augmentation loop implemented onboard a small unmanned aerial vehicle. Hardware-in-the-loop simulations and flight test results confirm the ability of the L1 adaptive controller to maintain stability and predictable performance of the closed loop adaptive system in the presence of general (artificially injected) unmodeled dynamics. The results demonstrate the advantages of L1 adaptive control as a verifiable robust adaptive control architecture with the potential of reducing flight control design costs and facilitating the transition of adaptive control into advanced flight control systems.

Real time simulation of nonlinear generalized predictive control for wind energy conversion system with nonlinear observer.

PubMed

Ouari, Kamel; Rekioua, Toufik; Ouhrouche, Mohand

2014-01-01

In order to make a wind power generation truly cost-effective and reliable, an advanced control techniques must be used. In this paper, we develop a new control strategy, using nonlinear generalized predictive control (NGPC) approach, for DFIG-based wind turbine. The proposed control law is based on two points: NGPC-based torque-current control loop generating the rotor reference voltage and NGPC-based speed control loop that provides the torque reference. In order to enhance the robustness of the controller, a disturbance observer is designed to estimate the aerodynamic torque which is considered as an unknown perturbation. Finally, a real-time simulation is carried out to illustrate the performance of the proposed controller. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Predicting effects of structural stress in a genome-reduced model bacterial metabolism

NASA Astrophysics Data System (ADS)

Güell, Oriol; Sagués, Francesc; Serrano, M. Ángeles

2012-08-01

Mycoplasma pneumoniae is a human pathogen recently proposed as a genome-reduced model for bacterial systems biology. Here, we study the response of its metabolic network to different forms of structural stress, including removal of individual and pairs of reactions and knockout of genes and clusters of co-expressed genes. Our results reveal a network architecture as robust as that of other model bacteria regarding multiple failures, although less robust against individual reaction inactivation. Interestingly, metabolite motifs associated to reactions can predict the propagation of inactivation cascades and damage amplification effects arising in double knockouts. We also detect a significant correlation between gene essentiality and damages produced by single gene knockouts, and find that genes controlling high-damage reactions tend to be expressed independently of each other, a functional switch mechanism that, simultaneously, acts as a genetic firewall to protect metabolism. Prediction of failure propagation is crucial for metabolic engineering or disease treatment.

Robust coordinated control of a dual-arm space robot

NASA Astrophysics Data System (ADS)

Shi, Lingling; Kayastha, Sharmila; Katupitiya, Jay

2017-09-01

Dual-arm space robots are more capable of implementing complex space tasks compared with single arm space robots. However, the dynamic coupling between the arms and the base will have a serious impact on the spacecraft attitude and the hand motion of each arm. Instead of considering one arm as the mission arm and the other as the balance arm, in this work two arms of the space robot perform as mission arms aimed at accomplishing secure capture of a floating target. The paper investigates coordinated control of the base's attitude and the arms' motion in the task space in the presence of system uncertainties. Two types of controllers, i.e. a Sliding Mode Controller (SMC) and a nonlinear Model Predictive Controller (MPC) are verified and compared with a conventional Computed-Torque Controller (CTC) through numerical simulations in terms of control accuracy and system robustness. Both controllers eliminate the need to linearly parameterize the dynamic equations. The MPC has been shown to achieve performance with higher accuracy than CTC and SMC in the absence of system uncertainties under the condition that they consume comparable energy. When the system uncertainties are included, SMC and CTC present advantageous robustness than MPC. Specifically, in a case where system inertia increases, SMC delivers higher accuracy than CTC and costs the least amount of energy.

Predictive IP controller for robust position control of linear servo system.

PubMed

Lu, Shaowu; Zhou, Fengxing; Ma, Yajie; Tang, Xiaoqi

2016-07-01

Position control is a typical application of linear servo system. In this paper, to reduce the system overshoot, an integral plus proportional (IP) controller is used in the position control implementation. To further improve the control performance, a gain-tuning IP controller based on a generalized predictive control (GPC) law is proposed. Firstly, to represent the dynamics of the position loop, a second-order linear model is used and its model parameters are estimated on-line by using a recursive least squares method. Secondly, based on the GPC law, an optimal control sequence is obtained by using receding horizon, then directly supplies the IP controller with the corresponding control parameters in the real operations. Finally, simulation and experimental results are presented to show the efficiency of proposed scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Are greenhouse gas emissions and cognitive skills related? Cross-country evidence.

PubMed

Omanbayev, Bekhzod; Salahodjaev, Raufhon; Lynn, Richard

2018-01-01

Are greenhouse gas emissions (GHG) and cognitive skills (CS) related? We attempt to answer this question by exploring this relationship, using cross-country data for 150 countries, for the period 1997-2012. After controlling for the level of economic development, quality of political regimes, population size and a number of other controls, we document that CS robustly predict GHG. In particular, when CS at a national level increase by one standard deviation, the average annual rate of air pollution changes by nearly 1.7% (slightly less than one half of a standard deviation). This significance holds for a number of robustness checks. Copyright © 2017 Elsevier Inc. All rights reserved.

The 32nd CDC: System identification using interval dynamic models

NASA Technical Reports Server (NTRS)

Keel, L. H.; Lew, J. S.; Bhattacharyya, S. P.

1992-01-01

Motivated by the recent explosive development of results in the area of parametric robust control, a new technique to identify a family of uncertain systems is identified. The new technique takes the frequency domain input and output data obtained from experimental test signals and produces an 'interval transfer function' that contains the complete frequency domain behavior with respect to the test signals. This interval transfer function is one of the key concepts in the parametric robust control approach and identification with such an interval model allows one to predict the worst case performance and stability margins using recent results on interval systems. The algorithm is illustrated by applying it to an 18 bay Mini-Mast truss structure.

Algorithms for Robust Identification and Control of Large Space Structures. Phase 1.

DTIC Science & Technology

1988-05-14

Variate Analysis," Proc. Amer. Control Conf., San Francisco, * pp. 445-451. LECTIQUE, J., Rault, A., Tessier, M., and Testud , J.L. (1978), "Multivariable...Rault, J.L. Testud , and J. Papon (1978), "Model Predictive Heuris- tic Control: Applications to Industrial Processes," Automatica, Vol. 14, pp. 413...Control ’. Conference, Minneapolis, MN, June. TESTUD , J.L. (1979), "Commande Numerique Multivariable du Ballon de Recupera- tion de Vapeur," Adersa/Gerbios

Lateral control system design for VTOL landing on a DD963 in high sea states. M.S. Thesis

NASA Technical Reports Server (NTRS)

Bodson, M.

1982-01-01

The problem of designing lateral control systems for the safe landing of VTOL aircraft on small ships is addressed. A ship model is derived. The issues of estimation and prediction of ship motions are discussed, using optimal linear linear estimation techniques. The roll motion is the most important of the lateral motions, and it is found that it can be predicted for up to 10 seconds in perfect conditions. The automatic landing of the VTOL aircraft is considered, and a lateral controller, defined as a ship motion tracker, is designed, using optimal control techniqes. The tradeoffs between the tracking errors and the control authority are obtained. The important couplings between the lateral motions and controls are demonstrated, and it is shown that the adverse couplings between the sway and the roll motion at the landing pad are significant constraints in the tracking of the lateral ship motions. The robustness of the control system, including the optimal estimator, is studied, using the singular values analysis. Through a robustification procedure, a robust control system is obtained, and the usefulness of the singular values to define stability margins that take into account general types of unstructured modelling errors is demonstrated. The minimal destabilizing perturbations indicated by the singular values analysis are interpreted and related to the multivariable Nyquist diagrams.

Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks.

PubMed

Yan, Zheng; Wang, Jun

2014-03-01

This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.

Perceived parenting style and adolescent adjustment: revisiting directions of effects and the role of parental knowledge.

PubMed

Kerr, Margaret; Stattin, Håkan; Ozdemir, Metin

2012-11-01

In the present research on parenting and adolescent behavior, there is much focus on reciprocal, bidirectional, and transactional processes, but parenting-style research still adheres to a unidirectional perspective in which parents affect youth behavior but are unaffected by it. In addition, many of the most cited parenting-style studies have used measures of parental behavioral control that are questionable because they include measures of parental knowledge. The goals of this study were to determine whether including knowledge items might have affected results of past studies and to test the unidirectional assumption. Data were from 978 adolescents participating in a longitudinal study. Parenting-style and adolescent adjustment measures at 2 time points were used, with a 2-year interval between time points. A variety of internal and external adjustment measures were used. Results showed that including knowledge items in measures of parental behavioral control elevated links between behavioral control and adjustment. Thus, the results and conclusions of many of the most highly cited studies are likely to have been stronger than if the measures had focused strictly on parental behavior. In addition, adolescent adjustment predicted changes in authoritative and neglectful parenting styles more robustly than these styles predicted changes in adolescent adjustment. Adolescent adjustment also predicted changes in authoritativeness more robustly than authoritativeness predicted changes in adjustment. Thus, parenting style cannot be seen as independent of the adolescent. In summary, both the theoretical premises of parenting-style research and the prior findings should be revisited.

Temperature control in a solar collector field using Filtered Dynamic Matrix Control.

PubMed

Lima, Daniel Martins; Normey-Rico, Julio Elias; Santos, Tito Luís Maia

2016-05-01

This paper presents the output temperature control of a solar collector field of a desalinization plant using the Filtered Dynamic Matrix Control (FDMC). The FDMC is a modified controller based on the Dynamic Matrix Control (DMC), a predictive control strategy widely used in industry. In the FDMC, a filter is used in the prediction error, which allows the modification of the robustness and disturbance rejection characteristics of the original algorithm. The implementation and tuning of the FDMC are simple and maintain the advantages of DMC. Several simulation results using a validated model of the solar plant are presented considering different scenarios. The results are also compared to nonlinear control techniques, showing that FDMC, if properly tuned, can yield similar results to more complex control algorithms. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A novel auto-tuning PID control mechanism for nonlinear systems.

PubMed

Cetin, Meric; Iplikci, Serdar

2015-09-01

In this paper, a novel Runge-Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning.

PubMed

Betthauser, Joseph L; Hunt, Christopher L; Osborn, Luke E; Masters, Matthew R; Levay, Gyorgy; Kaliki, Rahul R; Thakor, Nitish V

2018-04-01

Myoelectric signals can be used to predict the intended movements of an amputee for prosthesis control. However, untrained effects like limb position changes influence myoelectric signal characteristics, hindering the ability of pattern recognition algorithms to discriminate among motion classes. Despite frequent and long training sessions, these deleterious conditional influences may result in poor performance and device abandonment. We present a robust sparsity-based adaptive classification method that is significantly less sensitive to signal deviations resulting from untrained conditions. We compare this approach in the offline and online contexts of untrained upper-limb positions for amputee and able-bodied subjects to demonstrate its robustness compared against other myoelectric classification methods. We report significant performance improvements () in untrained limb positions across all subject groups. The robustness of our suggested approach helps to ensure better untrained condition performance from fewer training conditions. This method of prosthesis control has the potential to deliver real-world clinical benefits to amputees: better condition-tolerant performance, reduced training burden in terms of frequency and duration, and increased adoption of myoelectric prostheses.

Robust 1550-nm single-frequency all-fiber ns-pulsed fiber amplifier for wind-turbine predictive control by wind lidar

NASA Astrophysics Data System (ADS)

Beier, F.; de Vries, O.; Schreiber, T.; Eberhardt, R.; Tünnermann, A.; Bollig, C.; Hofmeister, P. G.; Schmidt, J.; Reuter, R.

2013-02-01

Scaling of the power yield of offshore wind farms relies on the capacity of the individual wind turbines. This results in a trend to very large rotor diameters, which are difficult to control. It is crucial to monitor the inhomogeneous wind field in front of the wind turbines at different distances to ensure reliable operation and a long lifetime at high output levels. In this contribution, we demonstrate an all-fiber ns-pulsed fiber amplifier based on cost-efficient commercially available components. The amplifier is a suitable source for coherent Doppler lidar pulses making a predictive control of the turbine operation feasible.

An Analysis of the Optimal Control Modification Method Applied to Flutter Suppression

NASA Technical Reports Server (NTRS)

Drew, Michael; Nguyen, Nhan T.; Hashemi, Kelley E.; Ting, Eric; Chaparro, Daniel

2017-01-01

Unlike basic Model Reference Adaptive Control (MRAC)l, Optimal Control Modification (OCM) has been shown to be a promising MRAC modification with robustness and analytical properties not present in other adaptive control methods. This paper presents an analysis of the OCM method, and how the asymptotic property of OCM is useful for analyzing and tuning the controller. We begin with a Lyapunov stability proof of an OCM controller having two adaptive gain terms, then the less conservative and easily analyzed OCM asymptotic property is presented. Two numerical examples are used to show how this property can accurately predict steady state stability and quantitative robustness in the presence of time delay, and relative to linear plant perturbations, and nominal Loop Transfer Recovery (LTR) tuning. The asymptotic property of the OCM controller is then used as an aid in tuning the controller applied to a large scale aeroservoelastic longitudinal aircraft model for flutter suppression. Control with OCM adaptive augmentation is shown to improve performance over that of the nominal non-adaptive controller when significant disparities exist between the controller/observer model and the true plant model.

Flux control-based design of furfural-resistance strains of Saccharomyces cerevisiae for lignocellulosic biorefinery.

PubMed

Unrean, Pornkamol

2017-04-01

We have previously developed a dynamic flux balance analysis of Saccharomyces cerevisiae for elucidation of genome-wide flux response to furfural perturbation (Unrean and Franzen, Biotechnol J 10(8):1248-1258, 2015). Herein, the dynamic flux distributions were analyzed by flux control analysis to identify target overexpressed genes for improved yeast robustness against furfural. The flux control coefficient (FCC) identified overexpressing isocitrate dehydrogenase (IDH1), a rate-controlling flux for ethanol fermentation, and dicarboxylate carrier (DIC1), a limiting flux for cell growth, as keys of furfural-resistance phenotype. Consistent with the model prediction, strain characterization showed 1.2- and 2.0-fold improvement in ethanol synthesis and furfural detoxification rates, respectively, by IDH1 overexpressed mutant compared to the control. DIC1 overexpressed mutant grew at 1.3-fold faster and reduced furfural at 1.4-fold faster than the control under the furfural challenge. This study hence demonstrated the FCC-based approach as an effective tool for guiding the design of robust yeast strains.

Designing Phononic Crystals with Wide and Robust Band Gaps

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jia, Zian; Chen, Yanyu; Yang, Haoxiang

Here, phononic crystals (PnCs) engineered to manipulate and control the propagation of mechanical waves have enabled the design of a range of novel devices, such as waveguides, frequency modulators, and acoustic cloaks, for which wide and robust phononic band gaps are highly preferable. While numerous PnCs have been designed in recent decades, to the best of our knowledge, PnCs that possess simultaneous wide and robust band gaps (to randomness and deformations) have not yet been reported. Here, we demonstrate that by combining the band-gap formation mechanisms of Bragg scattering and local resonances (the latter one is dominating), PnCs with widemore » and robust phononic band gaps can be established. The robustness of the phononic band gaps are then discussed from two aspects: robustness to geometric randomness (manufacture defects) and robustness to deformations (mechanical stimuli). Analytical formulations further predict the optimal design parameters, and an uncertainty analysis quantifies the randomness effect of each designing parameter. Moreover, we show that the deformation robustness originates from a local resonance-dominant mechanism together with the suppression of structural instability. Importantly, the proposed PnCs require only a small number of layers of elements (three unit cells) to obtain broad, robust, and strong attenuation bands, which offer great potential in designing flexible and deformable phononic devices.« less

Designing Phononic Crystals with Wide and Robust Band Gaps

DOE PAGES

Jia, Zian; Chen, Yanyu; Yang, Haoxiang; ...

2018-04-16

Here, phononic crystals (PnCs) engineered to manipulate and control the propagation of mechanical waves have enabled the design of a range of novel devices, such as waveguides, frequency modulators, and acoustic cloaks, for which wide and robust phononic band gaps are highly preferable. While numerous PnCs have been designed in recent decades, to the best of our knowledge, PnCs that possess simultaneous wide and robust band gaps (to randomness and deformations) have not yet been reported. Here, we demonstrate that by combining the band-gap formation mechanisms of Bragg scattering and local resonances (the latter one is dominating), PnCs with widemore » and robust phononic band gaps can be established. The robustness of the phononic band gaps are then discussed from two aspects: robustness to geometric randomness (manufacture defects) and robustness to deformations (mechanical stimuli). Analytical formulations further predict the optimal design parameters, and an uncertainty analysis quantifies the randomness effect of each designing parameter. Moreover, we show that the deformation robustness originates from a local resonance-dominant mechanism together with the suppression of structural instability. Importantly, the proposed PnCs require only a small number of layers of elements (three unit cells) to obtain broad, robust, and strong attenuation bands, which offer great potential in designing flexible and deformable phononic devices.« less

Designing Phononic Crystals with Wide and Robust Band Gaps

NASA Astrophysics Data System (ADS)

Jia, Zian; Chen, Yanyu; Yang, Haoxiang; Wang, Lifeng

2018-04-01

Phononic crystals (PnCs) engineered to manipulate and control the propagation of mechanical waves have enabled the design of a range of novel devices, such as waveguides, frequency modulators, and acoustic cloaks, for which wide and robust phononic band gaps are highly preferable. While numerous PnCs have been designed in recent decades, to the best of our knowledge, PnCs that possess simultaneous wide and robust band gaps (to randomness and deformations) have not yet been reported. Here, we demonstrate that by combining the band-gap formation mechanisms of Bragg scattering and local resonances (the latter one is dominating), PnCs with wide and robust phononic band gaps can be established. The robustness of the phononic band gaps are then discussed from two aspects: robustness to geometric randomness (manufacture defects) and robustness to deformations (mechanical stimuli). Analytical formulations further predict the optimal design parameters, and an uncertainty analysis quantifies the randomness effect of each designing parameter. Moreover, we show that the deformation robustness originates from a local resonance-dominant mechanism together with the suppression of structural instability. Importantly, the proposed PnCs require only a small number of layers of elements (three unit cells) to obtain broad, robust, and strong attenuation bands, which offer great potential in designing flexible and deformable phononic devices.

Validating Inertial Confinement Fusion (ICF) predictive capability using perturbed capsules

NASA Astrophysics Data System (ADS)

Schmitt, Mark; Magelssen, Glenn; Tregillis, Ian; Hsu, Scott; Bradley, Paul; Dodd, Evan; Cobble, James; Flippo, Kirk; Offerman, Dustin; Obrey, Kimberly; Wang, Yi-Ming; Watt, Robert; Wilke, Mark; Wysocki, Frederick; Batha, Steven

2009-11-01

Achieving ignition on NIF is a monumental step on the path toward utilizing fusion as a controlled energy source. Obtaining robust ignition requires accurate ICF models to predict the degradation of ignition caused by heterogeneities in capsule construction and irradiation. LANL has embarked on a project to induce controlled defects in capsules to validate our ability to predict their effects on fusion burn. These efforts include the validation of feature-driven hydrodynamics and mix in a convergent geometry. This capability is needed to determine the performance of capsules imploded under less-than-optimum conditions on future IFE facilities. LANL's recently initiated Defect Implosion Experiments (DIME) conducted at Rochester's Omega facility are providing input for these efforts. Recent simulation and experimental results will be shown.

Proteopathic tau seeding predicts tauopathy in vivo

PubMed Central

Holmes, Brandon B.; Furman, Jennifer L.; Mahan, Thomas E.; Yamasaki, Tritia R.; Mirbaha, Hilda; Eades, William C.; Belaygorod, Larisa; Cairns, Nigel J.; Holtzman, David M.; Diamond, Marc I.

2014-01-01

Transcellular propagation of protein aggregates, or proteopathic seeds, may drive the progression of neurodegenerative diseases in a prion-like manner. In tauopathies such as Alzheimer’s disease, this model predicts that tau seeds propagate pathology through the brain via cell–cell transfer in neural networks. The critical role of tau seeding activity is untested, however. It is unknown whether seeding anticipates and correlates with subsequent development of pathology as predicted for a causal agent. One major limitation has been the lack of a robust assay to measure proteopathic seeding activity in biological specimens. We engineered an ultrasensitive, specific, and facile FRET-based flow cytometry biosensor assay based on expression of tau or synuclein fusions to CFP and YFP, and confirmed its sensitivity and specificity to tau (∼300 fM) and synuclein (∼300 pM) fibrils. This assay readily discriminates Alzheimer’s disease vs. Huntington's disease and aged control brains. We then carried out a detailed time-course study in P301S tauopathy mice, comparing seeding activity versus histological markers of tau pathology, including MC1, AT8, PG5, and Thioflavin S. We detected robust seeding activity at 1.5 mo, >1 mo before the earliest histopathological stain. Proteopathic tau seeding is thus an early and robust marker of tauopathy, suggesting a proximal role for tau seeds in neurodegeneration. PMID:25261551

Metabolic robustness in young roots underpins a predictive model of maize hybrid performance in the field.

PubMed

de Abreu E Lima, Francisco; Westhues, Matthias; Cuadros-Inostroza, Álvaro; Willmitzer, Lothar; Melchinger, Albrecht E; Nikoloski, Zoran

2017-04-01

Heterosis has been extensively exploited for yield gain in maize (Zea mays L.). Here we conducted a comparative metabolomics-based analysis of young roots from in vitro germinating seedlings and from leaves of field-grown plants in a panel of inbred lines from the Dent and Flint heterotic patterns as well as selected F 1 hybrids. We found that metabolite levels in hybrids were more robust than in inbred lines. Using state-of-the-art modeling techniques, the most robust metabolites from roots and leaves explained up to 37 and 44% of the variance in the biomass from plants grown in two distinct field trials. In addition, a correlation-based analysis highlighted the trade-off between defense-related metabolites and hybrid performance. Therefore, our findings demonstrated the potential of metabolic profiles from young maize roots grown under tightly controlled conditions to predict hybrid performance in multiple field trials, thus bridging the greenhouse-field gap. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

Real-time economic nonlinear model predictive control for wind turbine control

NASA Astrophysics Data System (ADS)

Gros, Sebastien; Schild, Axel

2017-12-01

Nonlinear model predictive control (NMPC) is a strong candidate to handle the control challenges emerging in the modern wind energy industry. Recent research suggested that wind turbine (WT) control based on economic NMPC (ENMPC) can improve the closed-loop performance and simplify the task of controller design when compared to a classical NMPC approach. This paper establishes a formal relationship between the ENMPC controller and the classic NMPC approach, and compares empirically their closed-loop nominal behaviour and performance. The robustness of the performance is assessed for an inaccurate modelling of the tower fore-aft main frequency. Additionally, though a perfect wind preview is assumed here, the effect of having a limited horizon of preview of the wind speed via the LIght Detection And Ranging (LIDAR) sensor is investigated. Finally, this paper provides new algorithmic solutions for deploying ENMPC for WT control, and report improved computational times.

An Experimental Evaluation of Generalized Predictive Control for Tiltrotor Aeroelastic Stability Augmentation in Airplane Mode of Flight

NASA Technical Reports Server (NTRS)

Kvaternik, Raymond G.; Piatak, David J.; Nixon, Mark W.; Langston, Chester W.; Singleton, Jeffrey D.; Bennett, Richard L.; Brown, Ross K.

2001-01-01

The results of a joint NASA/Army/Bell Helicopter Textron wind-tunnel test to assess the potential of Generalized Predictive Control (GPC) for actively controlling the swashplate of tiltrotor aircraft to enhance aeroelastic stability in the airplane mode of flight are presented. GPC is an adaptive time-domain predictive control method that uses a linear difference equation to describe the input-output relationship of the system and to design the controller. The test was conducted in the Langley Transonic Dynamics Tunnel using an unpowered 1/5-scale semispan aeroelastic model of the V-22 that was modified to incorporate a GPC-based multi-input multi-output control algorithm to individually control each of the three swashplate actuators. Wing responses were used for feedback. The GPC-based control system was highly effective in increasing the stability of the critical wing mode for all of the conditions tested, without measurable degradation of the damping in the other modes. The algorithm was also robust with respect to its performance in adjusting to rapid changes in both the rotor speed and the tunnel airspeed.

Control theory meets synthetic biology

PubMed Central

2016-01-01

The past several years have witnessed an increased presence of control theoretic concepts in synthetic biology. This review presents an organized summary of how these control design concepts have been applied to tackle a variety of problems faced when building synthetic biomolecular circuits in living cells. In particular, we describe success stories that demonstrate how simple or more elaborate control design methods can be used to make the behaviour of synthetic genetic circuits within a single cell or across a cell population more reliable, predictable and robust to perturbations. The description especially highlights technical challenges that uniquely arise from the need to implement control designs within a new hardware setting, along with implemented or proposed solutions. Some engineering solutions employing complex feedback control schemes are also described, which, however, still require a deeper theoretical analysis of stability, performance and robustness properties. Overall, this paper should help synthetic biologists become familiar with feedback control concepts as they can be used in their application area. At the same time, it should provide some domain knowledge to control theorists who wish to enter the rising and exciting field of synthetic biology. PMID:27440256

Control theory meets synthetic biology.

PubMed

Del Vecchio, Domitilla; Dy, Aaron J; Qian, Yili

2016-07-01

The past several years have witnessed an increased presence of control theoretic concepts in synthetic biology. This review presents an organized summary of how these control design concepts have been applied to tackle a variety of problems faced when building synthetic biomolecular circuits in living cells. In particular, we describe success stories that demonstrate how simple or more elaborate control design methods can be used to make the behaviour of synthetic genetic circuits within a single cell or across a cell population more reliable, predictable and robust to perturbations. The description especially highlights technical challenges that uniquely arise from the need to implement control designs within a new hardware setting, along with implemented or proposed solutions. Some engineering solutions employing complex feedback control schemes are also described, which, however, still require a deeper theoretical analysis of stability, performance and robustness properties. Overall, this paper should help synthetic biologists become familiar with feedback control concepts as they can be used in their application area. At the same time, it should provide some domain knowledge to control theorists who wish to enter the rising and exciting field of synthetic biology. © 2016 The Author(s).

Predictive control of a chaotic permanent magnet synchronous generator in a wind turbine system

NASA Astrophysics Data System (ADS)

Manal, Messadi; Adel, Mellit; Karim, Kemih; Malek, Ghanes

2015-01-01

This paper investigates how to address the chaos problem in a permanent magnet synchronous generator (PMSG) in a wind turbine system. Predictive control approach is proposed to suppress chaotic behavior and make operating stable; the advantage of this method is that it can only be applied to one state of the wind turbine system. The use of the genetic algorithms to estimate the optimal parameter values of the wind turbine leads to maximization of the power generation. Moreover, some simulation results are included to visualize the effectiveness and robustness of the proposed method. Project supported by the CMEP-TASSILI Project (Grant No. 14MDU920).

Economic Model Predictive Control of Bihormonal Artificial Pancreas System Based on Switching Control and Dynamic R-parameter.

PubMed

Tang, Fengna; Wang, Youqing

2017-11-01

Blood glucose (BG) regulation is a long-term task for people with diabetes. In recent years, more and more researchers have attempted to achieve automated regulation of BG using automatic control algorithms, called the artificial pancreas (AP) system. In clinical practice, it is equally important to guarantee the treatment effect and reduce the treatment costs. The main motivation of this study is to reduce the cure burden. The dynamic R-parameter economic model predictive control (R-EMPC) is chosen to regulate the delivery rates of exogenous hormones (insulin and glucagon). It uses particle swarm optimization (PSO) to optimize the economic cost function and the switching logic between insulin delivery and glucagon delivery is designed based on switching control theory. The proposed method is first tested on the standard subject; the result is compared with the switching PID and the switching MPC. The effect of the dynamic R-parameter on improving the control performance is illustrated by comparing the results of the EMPC and the R-EMPC. Finally, the robustness tests on meal change (size and timing), hormone sensitivity (insulin and glucagon), and subject variability are performed. All results show that the proposed method can improve the control performance and reduce the economic costs. The simulation results verify the effectiveness of the proposed algorithm on improving the tracking performance, enhancing robustness, and reducing economic costs. The method proposed in this study owns great worth in practical application.

Unscented predictive variable structure filter for satellite attitude estimation with model errors when using low precision sensors

NASA Astrophysics Data System (ADS)

Cao, Lu; Li, Hengnian

2016-10-01

For the satellite attitude estimation problem, the serious model errors always exist and hider the estimation performance of the Attitude Determination and Control System (ACDS), especially for a small satellite with low precision sensors. To deal with this problem, a new algorithm for the attitude estimation, referred to as the unscented predictive variable structure filter (UPVSF) is presented. This strategy is proposed based on the variable structure control concept and unscented transform (UT) sampling method. It can be implemented in real time with an ability to estimate the model errors on-line, in order to improve the state estimation precision. In addition, the model errors in this filter are not restricted only to the Gaussian noises; therefore, it has the advantages to deal with the various kinds of model errors or noises. It is anticipated that the UT sampling strategy can further enhance the robustness and accuracy of the novel UPVSF. Numerical simulations show that the proposed UPVSF is more effective and robustness in dealing with the model errors and low precision sensors compared with the traditional unscented Kalman filter (UKF).

The comparison of predictive scheduling algorithms for different sizes of job shop scheduling problems

NASA Astrophysics Data System (ADS)

Paprocka, I.; Kempa, W. M.; Grabowik, C.; Kalinowski, K.; Krenczyk, D.

2016-08-01

In the paper a survey of predictive and reactive scheduling methods is done in order to evaluate how the ability of prediction of reliability characteristics influences over robustness criteria. The most important reliability characteristics are: Mean Time to Failure, Mean Time of Repair. Survey analysis is done for a job shop scheduling problem. The paper answers the question: what method generates robust schedules in the case of a bottleneck failure occurrence before, at the beginning of planned maintenance actions or after planned maintenance actions? Efficiency of predictive schedules is evaluated using criteria: makespan, total tardiness, flow time, idle time. Efficiency of reactive schedules is evaluated using: solution robustness criterion and quality robustness criterion. This paper is the continuation of the research conducted in the paper [1], where the survey of predictive and reactive scheduling methods is done only for small size scheduling problems.

Vehicle active steering control research based on two-DOF robust internal model control

NASA Astrophysics Data System (ADS)

Wu, Jian; Liu, Yahui; Wang, Fengbo; Bao, Chunjiang; Sun, Qun; Zhao, Youqun

2016-07-01

Because of vehicle's external disturbances and model uncertainties, robust control algorithms have obtained popularity in vehicle stability control. The robust control usually gives up performance in order to guarantee the robustness of the control algorithm, therefore an improved robust internal model control(IMC) algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness. The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties. In order to separate the design process of model tracking from the robustness design process, the improved 2 degree of freedom(DOF) robust internal model controller structure is given from the standard Youla parameterization. Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm, on the basis of a nonlinear vehicle simulation model with a magic tyre model. Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance, which can enhance the vehicle stability and handling, regardless of variations of the vehicle model parameters and the external crosswind interferences. Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing.

PubMed

Hwang, Han-Jeong; Hahne, Janne Mathias; Müller, Klaus-Robert

2017-01-01

There are some practical factors, such as arm position change and donning/doffing, which prevent robust myoelectric control. The objective of this study is to precisely characterize the impacts of the two representative factors on myoelectric controllability in practical control situations, thereby providing useful references that can be potentially used to find better solutions for clinically reliable myoelectric control. To this end, a real-time target acquisition task was performed by fourteen subjects including one individual with congenital upper-limb deficiency, where the impacts of arm position change, donning/doffing and a combination of both factors on control performance was systematically evaluated. The changes in online performance were examined with seven different performance metrics to comprehensively evaluate various aspects of myoelectric controllability. As a result, arm position change significantly affects offline prediction accuracy, but not online control performance due to real-time feedback, thereby showing no significant correlation between offline and online performance. Donning/doffing was still problematic in online control conditions. It was further observed that no benefit was attained when using a control model trained with multiple position data in terms of arm position change, and the degree of electrode shift caused by donning/doffing was not severely associated with the degree of performance loss under practical conditions (around 1 cm electrode shift). Since this study is the first to concurrently investigate the impacts of arm position change and donning/doffing in practical myoelectric control situations, all findings of this study provide new insights into robust myoelectric control with respect to arm position change and donning/doffing.

Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing

PubMed Central

Hahne, Janne Mathias; Müller, Klaus-Robert

2017-01-01

There are some practical factors, such as arm position change and donning/doffing, which prevent robust myoelectric control. The objective of this study is to precisely characterize the impacts of the two representative factors on myoelectric controllability in practical control situations, thereby providing useful references that can be potentially used to find better solutions for clinically reliable myoelectric control. To this end, a real-time target acquisition task was performed by fourteen subjects including one individual with congenital upper-limb deficiency, where the impacts of arm position change, donning/doffing and a combination of both factors on control performance was systematically evaluated. The changes in online performance were examined with seven different performance metrics to comprehensively evaluate various aspects of myoelectric controllability. As a result, arm position change significantly affects offline prediction accuracy, but not online control performance due to real-time feedback, thereby showing no significant correlation between offline and online performance. Donning/doffing was still problematic in online control conditions. It was further observed that no benefit was attained when using a control model trained with multiple position data in terms of arm position change, and the degree of electrode shift caused by donning/doffing was not severely associated with the degree of performance loss under practical conditions (around 1 cm electrode shift). Since this study is the first to concurrently investigate the impacts of arm position change and donning/doffing in practical myoelectric control situations, all findings of this study provide new insights into robust myoelectric control with respect to arm position change and donning/doffing. PMID:29095846

A model predictive speed tracking control approach for autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Zhu, Min; Chen, Huiyan; Xiong, Guangming

2017-03-01

This paper presents a novel speed tracking control approach based on a model predictive control (MPC) framework for autonomous ground vehicles. A switching algorithm without calibration is proposed to determine the drive or brake control. Combined with a simple inverse longitudinal vehicle model and adaptive regulation of MPC, this algorithm can make use of the engine brake torque for various driving conditions and avoid high frequency oscillations automatically. A simplified quadratic program (QP) solving algorithm is used to reduce the computational time, and the approach has been applied in a 16-bit microcontroller. The performance of the proposed approach is evaluated via simulations and vehicle tests, which were carried out in a range of speed-profile tracking tasks. With a well-designed system structure, high-precision speed control is achieved. The system can robustly model uncertainty and external disturbances, and yields a faster response with less overshoot than a PI controller.

Controller Strategies for Automation Tool Use under Varying Levels of Trajectory Prediction Uncertainty

NASA Technical Reports Server (NTRS)

Morey, Susan; Prevot, Thomas; Mercer, Joey; Martin, Lynne; Bienert, Nancy; Cabrall, Christopher; Hunt, Sarah; Homola, Jeffrey; Kraut, Joshua

2013-01-01

A human-in-the-loop simulation was conducted to examine the effects of varying levels of trajectory prediction uncertainty on air traffic controller workload and performance, as well as how strategies and the use of decision support tools change in response. This paper focuses on the strategies employed by two controllers from separate teams who worked in parallel but independently under identical conditions (airspace, arrival traffic, tools) with the goal of ensuring schedule conformance and safe separation for a dense arrival flow in en route airspace. Despite differences in strategy and methods, both controllers achieved high levels of schedule conformance and safe separation. Overall, results show that trajectory uncertainties introduced by wind and aircraft performance prediction errors do not affect the controllers' ability to manage traffic. Controller strategies were fairly robust to changes in error, though strategies were affected by the amount of delay to absorb (scheduled time of arrival minus estimated time of arrival). Using the results and observations, this paper proposes an ability to dynamically customize the display of information including delay time based on observed error to better accommodate different strategies and objectives.

Amotivation and functional outcomes in early schizophrenia.

PubMed

Fervaha, Gagan; Foussias, George; Agid, Ofer; Remington, Gary

2013-12-15

Negative symptoms, particularly amotivation/apathy, are intimately tied to functional outcomes. In the present study, apathy strongly predicted psychosocial functioning in a sample of early course schizophrenia patients. This relationship remained robust even after controlling for other clinical variables. These data suggest amotivation is core to functioning across the disease course. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Network control principles predict neuron function in the Caenorhabditis elegans connectome

PubMed Central

Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László

2017-01-01

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social and technological networks1–3. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode C. elegans4–6, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires twelve neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation7–13, as well as one previously uncharacterised neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed, with single-cell ablations of DD04 or DD05, but not DD02 or DD03, specifically affecting posterior body movements. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterised connectomes. PMID:29045391

Network control principles predict neuron function in the Caenorhabditis elegans connectome

NASA Astrophysics Data System (ADS)

Yan, Gang; Vértes, Petra E.; Towlson, Emma K.; Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László

2017-10-01

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

Network control principles predict neuron function in the Caenorhabditis elegans connectome.

PubMed

Yan, Gang; Vértes, Petra E; Towlson, Emma K; Chew, Yee Lian; Walker, Denise S; Schafer, William R; Barabási, Albert-László

2017-10-26

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

Molecular mechanisms governing differential robustness of development and environmental responses in plants

PubMed Central

Lachowiec, Jennifer; Queitsch, Christine; Kliebenstein, Daniel J.

2016-01-01

Background Robustness to genetic and environmental perturbation is a salient feature of multicellular organisms. Loss of developmental robustness can lead to severe phenotypic defects and fitness loss. However, perfect robustness, i.e. no variation at all, is evolutionarily unfit as organisms must be able to change phenotype to properly respond to changing environments and biotic challenges. Plasticity is the ability to adjust phenotypes predictably in response to specific environmental stimuli, which can be considered a transient shift allowing an organism to move from one robust phenotypic state to another. Plants, as sessile organisms that undergo continuous development, are particularly dependent on an exquisite fine-tuning of the processes that balance robustness and plasticity to maximize fitness. Scope and Conclusions This paper reviews recently identified mechanisms, both systems-level and molecular, that modulate robustness, and discusses their implications for the optimization of plant fitness. Robustness in living systems arises from the structure of genetic networks, the specific molecular functions of the underlying genes, and their interactions. This very same network responsible for the robustness of specific developmental states also has to be built such that it enables plastic yet robust shifts in response to environmental changes. In plants, the interactions and functions of signal transduction pathways activated by phytohormones and the tendency for plants to tolerate whole-genome duplications, tandem gene duplication and hybridization are emerging as major regulators of robustness in development. Despite their obvious implications for plant evolution and plant breeding, the mechanistic underpinnings by which plants modulate precise levels of robustness, plasticity and evolvability in networks controlling different phenotypes are under-studied. PMID:26473020

Molecular inspired models for prediction and control of directional FSO/RF wireless networks

NASA Astrophysics Data System (ADS)

Llorca, Jaime; Milner, Stuart D.; Davis, Christopher C.

2010-08-01

Directional wireless networks using FSO and RF transmissions provide wireless backbone support for mobile communications in dynamic environments. The heterogeneous and dynamic nature of such networks challenges their robustness and requires self-organization mechanisms to assure end-to-end broadband connectivity. We developed a framework based on the definition of a potential energy function to characterize robustness in communication networks and the study of first and second order variations of the potential energy to provide prediction and control strategies for network performance optimization. In this paper, we present non-convex molecular potentials such as the Morse Potential, used to describe the potential energy of bonds within molecules, for the characterization of communication links in the presence of physical constraints such as the power available at the network nodes. The inclusion of the Morse Potential translates into adaptive control strategies where forces on network nodes drive the release, retention or reconfiguration of communication links for network performance optimization. Simulation results show the effectiveness of our self-organized control mechanism, where the physical topology reorganizes to maximize the number of source to destination communicating pairs. Molecular Normal Mode Analysis (NMA) techniques for assessing network performance degradation in dynamic networks are also presented. Preliminary results show correlation between peaks in the eigenvalues of the Hessian of the network potential and network degradation.

The cortisol reactivity threshold model: Direction of trait rumination and cortisol reactivity association varies with stressor severity.

PubMed

Vrshek-Schallhorn, Suzanne; Avery, Bradley M; Ditcheva, Maria; Sapuram, Vaibhav R

2018-06-01

Various internalizing risk factors predict, in separate studies, both augmented and reduced cortisol responding to lab-induced stress. Stressor severity appears key: We tested whether heightened trait-like internalizing risk (here, trait rumination) predicts heightened cortisol reactivity under modest objective stress, but conversely predicts reduced reactivity under more robust objective stress. Thus, we hypothesized that trait rumination would interact with a curvilinear (quadratic) function of stress severity to predict cortisol reactivity. Evidence comes from 85 currently non-depressed emerging adults who completed either a non-stressful control protocol (n = 29), an intermediate difficulty Trier Social Stress Test (TSST; n = 26), or a robustly stressful negative evaluative TSST (n = 30). Latent growth curve models evaluated relationships between trait rumination and linear and quadratic effects of stressor severity on the change in cortisol and negative affect over time. Among other findings, a significant Trait Rumination x Quadratic Stress Severity interaction effect for cortisol's Quadratic Trend of Time (i.e., reactivity, B = .125, p = .017) supported the hypothesis. Rumination predicted greater cortisol reactivity to intermediate stress (r p  = .400, p = .043), but blunted reactivity to more robust negative evaluative stress (r p  = -0.379, p = 0.039). Contrasting hypotheses, negative affective reactivity increased independently of rumination as stressor severity increased (B = .453, p = 0.044). The direction of the relationship between an internalizing risk factor (trait rumination) and cortisol reactivity varies as a function of stressor severity. We propose the Cortisol Reactivity Threshold Model, which may help reconcile several divergent reactivity literatures and has implications for internalizing psychopathology, particularly depression. Copyright © 2017 Elsevier Ltd. All rights reserved.

Transonic Flutter Suppression Control Law Design, Analysis and Wind-Tunnel Results

NASA Technical Reports Server (NTRS)

Mukhopadhyay, Vivek

1999-01-01

The benchmark active controls technology and wind tunnel test program at NASA Langley Research Center was started with the objective to investigate the nonlinear, unsteady aerodynamics and active flutter suppression of wings in transonic flow. The paper will present the flutter suppression control law design process, numerical nonlinear simulation and wind tunnel test results for the NACA 0012 benchmark active control wing model. The flutter suppression control law design processes using classical, and minimax techniques are described. A unified general formulation and solution for the minimax approach, based on the steady state differential game theory is presented. Design considerations for improving the control law robustness and digital implementation are outlined. It was shown that simple control laws when properly designed based on physical principles, can suppress flutter with limited control power even in the presence of transonic shocks and flow separation. In wind tunnel tests in air and heavy gas medium, the closed-loop flutter dynamic pressure was increased to the tunnel upper limit of 200 psf. The control law robustness and performance predictions were verified in highly nonlinear flow conditions, gain and phase perturbations, and spoiler deployment. A non-design plunge instability condition was also successfully suppressed.

Robust nanopatterning by laser-induced dewetting of metal nanofilms.

PubMed

Favazza, Christopher; Kalyanaraman, Ramki; Sureshkumar, Radhakrishna

2006-08-28

We have observed nanopattern formation with robust and controllable spatial ordering by laser-induced dewetting in nanoscopic metal films. Pattern evolution in Co film of thickness 1≤h≤8 nm on SiO(2) was achieved under multiple pulse irradiation using a 9 ns pulse laser. Dewetting leads to the formation of cellular patterns which evolve into polygons that eventually break up into nanoparticles with unimodal size distribution and short range ordering in nearest neighbour spacing R. Spatial ordering was attributed to a hydrodynamic thin film instability and resulted in a predictable variation of R and particle diameter D with h. The length scales R and D were found to be independent of the laser energy. These results suggest that spatially ordered metal nanoparticles can be robustly assembled by laser-induced dewetting.

Observed Parent-Child Relationship Quality Predicts Antibody Response to Vaccination in Children

PubMed Central

O'Connor, Thomas G; Wang, Hongyue; Moynihan, Jan A; Wyman, Peter A.; Carnahan, Jennifer; Lofthus, Gerry; Quataert, Sally A.; Bowman, Melissa; Burke, Anne S.; Caserta, Mary T

2015-01-01

Background Quality of the parent-child relationship is a robust predictor of behavioral and emotional health for children and adolescents; the application to physical health is less clear. Methods We investigated the links between observed parent-child relationship quality in an interaction task and antibody response to meningococcal conjugate vaccine in a longitudinal study of 164 ambulatory 10-11 year-old children; additional analyses examine associations with cortisol reactivity, BMI, and somatic illness. Results Observed negative/conflict behavior in the interaction task predicted a less robust antibody response to meningococcal serotype C vaccine in the child over a 6 month-period, after controlling for socio-economic and other covariates. Observer rated interaction conflict also predicted increased cortisol reactivity following the interaction task and higher BMI, but these factors did not account for the link between relationship quality and antibody response. Conclusions The results begin to document the degree to which a major source of child stress exposure, parent-child relationship conflict, is associated with altered immune system development in children, and may constitute an important public health consideration. PMID:25862953

Development and validation of a melanoma risk score based on pooled data from 16 case-control studies

PubMed Central

Davies, John R; Chang, Yu-mei; Bishop, D Timothy; Armstrong, Bruce K; Bataille, Veronique; Bergman, Wilma; Berwick, Marianne; Bracci, Paige M; Elwood, J Mark; Ernstoff, Marc S; Green, Adele; Gruis, Nelleke A; Holly, Elizabeth A; Ingvar, Christian; Kanetsky, Peter A; Karagas, Margaret R; Lee, Tim K; Le Marchand, Loïc; Mackie, Rona M; Olsson, Håkan; Østerlind, Anne; Rebbeck, Timothy R; Reich, Kristian; Sasieni, Peter; Siskind, Victor; Swerdlow, Anthony J; Titus, Linda; Zens, Michael S; Ziegler, Andreas; Gallagher, Richard P.; Barrett, Jennifer H; Newton-Bishop, Julia

2015-01-01

Background We report the development of a cutaneous melanoma risk algorithm based upon 7 factors; hair colour, skin type, family history, freckling, nevus count, number of large nevi and history of sunburn, intended to form the basis of a self-assessment webtool for the general public. Methods Predicted odds of melanoma were estimated by analysing a pooled dataset from 16 case-control studies using logistic random coefficients models. Risk categories were defined based on the distribution of the predicted odds in the controls from these studies. Imputation was used to estimate missing data in the pooled datasets. The 30th, 60th and 90th centiles were used to distribute individuals into four risk groups for their age, sex and geographic location. Cross-validation was used to test the robustness of the thresholds for each group by leaving out each study one by one. Performance of the model was assessed in an independent UK case-control study dataset. Results Cross-validation confirmed the robustness of the threshold estimates. Cases and controls were well discriminated in the independent dataset (area under the curve 0.75, 95% CI 0.73-0.78). 29% of cases were in the highest risk group compared with 7% of controls, and 43% of controls were in the lowest risk group compared with 13% of cases. Conclusion We have identified a composite score representing an estimate of relative risk and successfully validated this score in an independent dataset. Impact This score may be a useful tool to inform members of the public about their melanoma risk. PMID:25713022

Fuzzy Behavior-Based Navigation for Planetary

NASA Technical Reports Server (NTRS)

Tunstel, Edward; Danny, Harrison; Lippincott, Tanya; Jamshidi, Mo

1997-01-01

Adaptive behavioral capabilities are necessary for robust rover navigation in unstructured and partially-mapped environments. A control approach is described which exploits the approximate reasoning capability of fuzzy logic to produce adaptive motion behavior. In particular, a behavior-based architecture for hierarchical fuzzy control of microrovers is presented. Its structure is described, as well as mechanisms of control decision-making which give rise to adaptive behavior. Control decisions for local navigation result from a consensus of recommendations offered only by behaviors that are applicable to current situations. Simulation predicts the navigation performance on a microrover in simplified Mars-analog terrain.

Temporal assessment of radiomic features on clinical mammography in a high-risk population

NASA Astrophysics Data System (ADS)

Mendel, Kayla R.; Li, Hui; Lan, Li; Chan, Chun-Wai; King, Lauren M.; Tayob, Nabihah; Whitman, Gary; El-Zein, Randa; Bedrosian, Isabelle; Giger, Maryellen L.

2018-02-01

Extraction of high-dimensional quantitative data from medical images has become necessary in disease risk assessment, diagnostics and prognostics. Radiomic workflows for mammography typically involve a single medical image for each patient although medical images may exist for multiple imaging exams, especially in screening protocols. Our study takes advantage of the availability of mammograms acquired over multiple years for the prediction of cancer onset. This study included 841 images from 328 patients who developed subsequent mammographic abnormalities, which were confirmed as either cancer (n=173) or non-cancer (n=155) through diagnostic core needle biopsy. Quantitative radiomic analysis was conducted on antecedent FFDMs acquired a year or more prior to diagnostic biopsy. Analysis was limited to the breast contralateral to that in which the abnormality arose. Novel metrics were used to identify robust radiomic features. The most robust features were evaluated in the task of predicting future malignancies on a subset of 72 subjects (23 cancer cases and 49 non-cancer controls) with mammograms over multiple years. Using linear discriminant analysis, the robust radiomic features were merged into predictive signatures by: (i) using features from only the most recent contralateral mammogram, (ii) change in feature values between mammograms, and (iii) ratio of feature values over time, yielding AUCs of 0.57 (SE=0.07), 0.63 (SE=0.06), and 0.66 (SE=0.06), respectively. The AUCs for temporal radiomics (ratio) statistically differed from chance, suggesting that changes in radiomics over time may be critical for risk assessment. Overall, we found that our two-stage process of robustness assessment followed by performance evaluation served well in our investigation on the role of temporal radiomics in risk assessment.

Proinflammatory isoforms of IL-32 as novel and robust biomarkers for control failure in HIV-infected slow progressors

PubMed Central

El-Far, Mohamed; Kouassi, Pascale; Sylla, Mohamed; Zhang, Yuwei; Fouda, Ahmed; Fabre, Thomas; Goulet, Jean-Philippe; van Grevenynghe, Julien; Lee, Terry; Singer, Joel; Harris, Marianne; Baril, Jean-Guy; Trottier, Benoit; Ancuta, Petronela; Routy, Jean-Pierre; Bernard, Nicole; Tremblay, Cécile L.; Angel, Jonathan; Conway, Brian; Côté, Pierre; Gill, John; Johnston, Lynn; Kovacs, Colin; Loutfy, Mona; Logue, Kenneth; Piché, Alain; Rachlis, Anita; Rouleau, Danielle; Thompson, Bill; Thomas, Réjean; Trottier, Sylvie; Walmsley, Sharon; Wobeser, Wendy

2016-01-01

HIV-infected slow progressors (SP) represent a heterogeneous group of subjects who spontaneously control HIV infection without treatment for several years while showing moderate signs of disease progression. Under conditions that remain poorly understood, a subgroup of these subjects experience failure of spontaneous immunological and virological control. Here we determined the frequency of SP subjects who showed loss of HIV control within our Canadian Cohort of HIV+ Slow Progressors and identified the proinflammatory cytokine IL-32 as a robust biomarker for control failure. Plasmatic levels of the proinflammatory isoforms of IL-32 (mainly β and γ) at earlier clinic visits positively correlated with the decline of CD4 T-cell counts, increased viral load, lower CD4/CD8 ratio and levels of inflammatory markers (sCD14 and IL-6) at later clinic visits. We present here a proof-of-concept for the use of IL-32 as a predictive biomarker for disease progression in SP subjects and identify IL-32 as a potential therapeutic target. PMID:26978598

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dobson, Ian; Hiskens, Ian; Linderoth, Jeffrey

Building on models of electrical power systems, and on powerful mathematical techniques including optimization, model predictive control, and simluation, this project investigated important issues related to the stable operation of power grids. A topic of particular focus was cascading failures of the power grid: simulation, quantification, mitigation, and control. We also analyzed the vulnerability of networks to component failures, and the design of networks that are responsive to and robust to such failures. Numerous other related topics were investigated, including energy hubs and cascading stall of induction machines

Using Symmetric Predication of Endogenous Subgroups for Causal Inferences about Program Effects under Robust Assumptions: Part Two of a Method Note in Three Parts

ERIC Educational Resources Information Center

Bell, Stephen H.; Peck, Laura R.

2013-01-01

To answer "what works?" questions about policy interventions based on an experimental design, Peck (2003) proposes to use baseline characteristics to symmetrically divide treatment and control group members into subgroups defined by endogenously determined postrandom assignment events. Symmetric prediction of these subgroups in both…

Designing robust control laws using genetic algorithms

NASA Technical Reports Server (NTRS)

Marrison, Chris

1994-01-01

The purpose of this research is to create a method of finding practical, robust control laws. The robustness of a controller is judged by Stochastic Robustness metrics and the level of robustness is optimized by searching for design parameters that minimize a robustness cost function.

Conditioning and Robustness of RNA Boltzmann Sampling under Thermodynamic Parameter Perturbations.

PubMed

Rogers, Emily; Murrugarra, David; Heitsch, Christine

2017-07-25

Understanding how RNA secondary structure prediction methods depend on the underlying nearest-neighbor thermodynamic model remains a fundamental challenge in the field. Minimum free energy (MFE) predictions are known to be "ill conditioned" in that small changes to the thermodynamic model can result in significantly different optimal structures. Hence, the best practice is now to sample from the Boltzmann distribution, which generates a set of suboptimal structures. Although the structural signal of this Boltzmann sample is known to be robust to stochastic noise, the conditioning and robustness under thermodynamic perturbations have yet to be addressed. We present here a mathematically rigorous model for conditioning inspired by numerical analysis, and also a biologically inspired definition for robustness under thermodynamic perturbation. We demonstrate the strong correlation between conditioning and robustness and use its tight relationship to define quantitative thresholds for well versus ill conditioning. These resulting thresholds demonstrate that the majority of the sequences are at least sample robust, which verifies the assumption of sampling's improved conditioning over the MFE prediction. Furthermore, because we find no correlation between conditioning and MFE accuracy, the presence of both well- and ill-conditioned sequences indicates the continued need for both thermodynamic model refinements and alternate RNA structure prediction methods beyond the physics-based ones. Copyright © 2017. Published by Elsevier Inc.

A Discrete-Time Average Model Based Predictive Control for Quasi-Z-Source Inverter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Yushan; Abu-Rub, Haitham; Xue, Yaosuo

A discrete-time average model-based predictive control (DTA-MPC) is proposed for a quasi-Z-source inverter (qZSI). As a single-stage inverter topology, the qZSI regulates the dc-link voltage and the ac output voltage through the shoot-through (ST) duty cycle and the modulation index. Several feedback strategies have been dedicated to produce these two control variables, among which the most popular are the proportional–integral (PI)-based control and the conventional model-predictive control (MPC). However, in the former, there are tradeoffs between fast response and stability; the latter is robust, but at the cost of high calculation burden and variable switching frequency. Moreover, they require anmore » elaborated design or fine tuning of controller parameters. The proposed DTA-MPC predicts future behaviors of the ST duty cycle and modulation signals, based on the established discrete-time average model of the quasi-Z-source (qZS) inductor current, the qZS capacitor voltage, and load currents. The prediction actions are applied to the qZSI modulator in the next sampling instant, without the need of other controller parameters’ design. A constant switching frequency and significantly reduced computations are achieved with high performance. Transient responses and steady-state accuracy of the qZSI system under the proposed DTA-MPC are investigated and compared with the PI-based control and the conventional MPC. Simulation and experimental results verify the effectiveness of the proposed approach for the qZSI.« less

A Discrete-Time Average Model Based Predictive Control for Quasi-Z-Source Inverter

DOE PAGES

Liu, Yushan; Abu-Rub, Haitham; Xue, Yaosuo; ...

2017-12-25

A discrete-time average model-based predictive control (DTA-MPC) is proposed for a quasi-Z-source inverter (qZSI). As a single-stage inverter topology, the qZSI regulates the dc-link voltage and the ac output voltage through the shoot-through (ST) duty cycle and the modulation index. Several feedback strategies have been dedicated to produce these two control variables, among which the most popular are the proportional–integral (PI)-based control and the conventional model-predictive control (MPC). However, in the former, there are tradeoffs between fast response and stability; the latter is robust, but at the cost of high calculation burden and variable switching frequency. Moreover, they require anmore » elaborated design or fine tuning of controller parameters. The proposed DTA-MPC predicts future behaviors of the ST duty cycle and modulation signals, based on the established discrete-time average model of the quasi-Z-source (qZS) inductor current, the qZS capacitor voltage, and load currents. The prediction actions are applied to the qZSI modulator in the next sampling instant, without the need of other controller parameters’ design. A constant switching frequency and significantly reduced computations are achieved with high performance. Transient responses and steady-state accuracy of the qZSI system under the proposed DTA-MPC are investigated and compared with the PI-based control and the conventional MPC. Simulation and experimental results verify the effectiveness of the proposed approach for the qZSI.« less

Robust logistic regression to narrow down the winner's curse for rare and recessive susceptibility variants.

PubMed

Kesselmeier, Miriam; Lorenzo Bermejo, Justo

2017-11-01

Logistic regression is the most common technique used for genetic case-control association studies. A disadvantage of standard maximum likelihood estimators of the genotype relative risk (GRR) is their strong dependence on outlier subjects, for example, patients diagnosed at unusually young age. Robust methods are available to constrain outlier influence, but they are scarcely used in genetic studies. This article provides a non-intimidating introduction to robust logistic regression, and investigates its benefits and limitations in genetic association studies. We applied the bounded Huber and extended the R package 'robustbase' with the re-descending Hampel functions to down-weight outlier influence. Computer simulations were carried out to assess the type I error rate, mean squared error (MSE) and statistical power according to major characteristics of the genetic study and investigated markers. Simulations were complemented with the analysis of real data. Both standard and robust estimation controlled type I error rates. Standard logistic regression showed the highest power but standard GRR estimates also showed the largest bias and MSE, in particular for associated rare and recessive variants. For illustration, a recessive variant with a true GRR=6.32 and a minor allele frequency=0.05 investigated in a 1000 case/1000 control study by standard logistic regression resulted in power=0.60 and MSE=16.5. The corresponding figures for Huber-based estimation were power=0.51 and MSE=0.53. Overall, Hampel- and Huber-based GRR estimates did not differ much. Robust logistic regression may represent a valuable alternative to standard maximum likelihood estimation when the focus lies on risk prediction rather than identification of susceptibility variants. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Real-Time Flight Envelope Monitoring System

NASA Technical Reports Server (NTRS)

Kerho, Michael; Bragg, Michael B.; Ansell, Phillip J.

2012-01-01

The objective of this effort was to show that real-time aircraft control-surface hinge-moment information could be used to provide a robust and reliable prediction of vehicle performance and control authority degradation. For a given airfoil section with a control surface -- be it a wing with an aileron, rudder, or elevator -- the control-surface hinge moment is sensitive to the aerodynamic characteristics of the section. As a result, changes in the aerodynamics of the section due to angle-of-attack or environmental effects such as icing, heavy rain, surface contaminants, bird strikes, or battle damage will affect the control surface hinge moment. These changes include both the magnitude of the hinge moment and its sign in a time-averaged sense, and the variation of the hinge moment with time. The current program attempts to take the real-time hinge moment information from the aircraft control surfaces and develop a system to predict aircraft envelope boundaries across a range of conditions, alerting the flight crew to reductions in aircraft controllability and flight boundaries.

Modeling and control of plasma rotation and βn for NSTX-U using Neoclassical Toroidal Viscosity and Neutral Beam Injection

NASA Astrophysics Data System (ADS)

Goumiri, Imene; Rowley, Clarence; Sabbagh, Steven; Gates, David; Gerhardt, Stefan; Boyer, Mark

2015-11-01

A model-based system is presented allowing control of the plasma rotation profile in a magnetically confined toroidal fusion device to maintain plasma stability for long pulse operation. The analysis, using NSTX data and NSTX-U TRANSP simulations, is aimed at controlling plasma rotation using momentum from six injected neutral beams and neoclassical toroidal viscosity generated by three-dimensional applied magnetic fields as actuators. Based on the momentum diffusion and torque balance model obtained, a feedback controller is designed and predictive simulations using TRANSP will be presented. Robustness of the model and the rotation controller will be discussed.

A simple model-based control for Pichia pastoris allows a more efficient heterologous protein production bioprocess.

PubMed

Cos, Oriol; Ramon, Ramon; Montesinos, José Luis; Valero, Francisco

2006-09-05

A predictive control algorithm coupled with a PI feedback controller has been satisfactorily implemented in the heterologous Rhizopus oryzae lipase production by Pichia pastoris methanol utilization slow (Mut(s)) phenotype. This control algorithm has allowed the study of the effect of methanol concentration, ranging from 0.5 to 1.75 g/L, on heterologous protein production. The maximal lipolytic activity (490 UA/mL), specific yield (11,236 UA/g(biomass)), productivity (4,901 UA/L . h), and specific productivity (112 UA/g(biomass)h were reached for a methanol concentration of 1 g/L. These parameters are almost double than those obtained with a manual control at a similar methanol set-point. The study of the specific growth, consumption, and production rates showed different patterns for these rates depending on the methanol concentration set-point. Results obtained have shown the need of implementing a robust control scheme when reproducible quality and productivity are sought. It has been demonstrated that the model-based control proposed here is a very efficient, robust, and easy-to-implement strategy from an industrial application point of view. (c) 2006 Wiley Periodicals, Inc.

Reliability of simulated robustness testing in fast liquid chromatography, using state-of-the-art column technology, instrumentation and modelling software.

PubMed

Kormány, Róbert; Fekete, Jenő; Guillarme, Davy; Fekete, Szabolcs

2014-02-01

The goal of this study was to evaluate the accuracy of simulated robustness testing using commercial modelling software (DryLab) and state-of-the-art stationary phases. For this purpose, a mixture of amlodipine and its seven related impurities was analyzed on short narrow bore columns (50×2.1mm, packed with sub-2μm particles) providing short analysis times. The performance of commercial modelling software for robustness testing was systematically compared to experimental measurements and DoE based predictions. We have demonstrated that the reliability of predictions was good, since the predicted retention times and resolutions were in good agreement with the experimental ones at the edges of the design space. In average, the retention time relative errors were <1.0%, while the predicted critical resolution errors were comprised between 6.9 and 17.2%. Because the simulated robustness testing requires significantly less experimental work than the DoE based predictions, we think that robustness could now be investigated in the early stage of method development. Moreover, the column interchangeability, which is also an important part of robustness testing, was investigated considering five different C8 and C18 columns packed with sub-2μm particles. Again, thanks to modelling software, we proved that the separation was feasible on all columns within the same analysis time (less than 4min), by proper adjustments of variables. Copyright © 2013 Elsevier B.V. All rights reserved.

Predicting posttraumatic stress symptoms in children following Hurricane Katrina: a prospective analysis of the effect of parental distress and parenting practices.

PubMed

Kelley, Mary Lou; Self-Brown, Shannon; Le, Brenda; Bosson, Julia Vigna; Hernandez, Brittany C; Gordon, Arlene T

2010-10-01

Research exhibits a robust relation between child hurricane exposure, parent distress, and child posttraumatic stress disorder (PTSD). This study explored parenting practices that could further explicate this association. Participants were 381 mothers and their children exposed to Hurricane Katrina. It was hypothesized that 3-7 months (T1) and 14-17 months (T2) post-Katrina: (a) hurricane exposure would predict child PTSD symptoms after controlling for history of violence exposure and (b) hurricane exposure would predict parent distress and negative parenting practices, which, in turn, would predict increased child PTSD symptoms. Hypotheses were partially supported. Hurricane exposure directly predicted child PTSD at T1 and indirectly at T2. Additionally, several significant paths emerged from hurricane exposure to parent distress and parenting practices, which were predictive of child PTSD.

The Mechanisms for Within-Host Influenza Virus Control Affect Model-Based Assessment and Prediction of Antiviral Treatment

PubMed Central

Cao, Pengxing

2017-01-01

Models of within-host influenza viral dynamics have contributed to an improved understanding of viral dynamics and antiviral effects over the past decade. Existing models can be classified into two broad types based on the mechanism of viral control: models utilising target cell depletion to limit the progress of infection and models which rely on timely activation of innate and adaptive immune responses to control the infection. In this paper, we compare how two exemplar models based on these different mechanisms behave and investigate how the mechanistic difference affects the assessment and prediction of antiviral treatment. We find that the assumed mechanism for viral control strongly influences the predicted outcomes of treatment. Furthermore, we observe that for the target cell-limited model the assumed drug efficacy strongly influences the predicted treatment outcomes. The area under the viral load curve is identified as the most reliable predictor of drug efficacy, and is robust to model selection. Moreover, with support from previous clinical studies, we suggest that the target cell-limited model is more suitable for modelling in vitro assays or infection in some immunocompromised/immunosuppressed patients while the immune response model is preferred for predicting the infection/antiviral effect in immunocompetent animals/patients. PMID:28933757

A cortically-inspired model for inverse kinematics computation of a humanoid finger with mechanically coupled joints.

PubMed

Gentili, Rodolphe J; Oh, Hyuk; Kregling, Alissa V; Reggia, James A

2016-05-19

The human hand's versatility allows for robust and flexible grasping. To obtain such efficiency, many robotic hands include human biomechanical features such as fingers having their two last joints mechanically coupled. Although such coupling enables human-like grasping, controlling the inverse kinematics of such mechanical systems is challenging. Here we propose a cortical model for fine motor control of a humanoid finger, having its two last joints coupled, that learns the inverse kinematics of the effector. This neural model functionally mimics the population vector coding as well as sensorimotor prediction processes of the brain's motor/premotor and parietal regions, respectively. After learning, this neural architecture could both overtly (actual execution) and covertly (mental execution or motor imagery) perform accurate, robust and flexible finger movements while reproducing the main human finger kinematic states. This work contributes to developing neuro-mimetic controllers for dexterous humanoid robotic/prosthetic upper-extremities, and has the potential to promote human-robot interactions.

Signaling mechanisms underlying the robustness and tunability of the plant immune network

PubMed Central

Kim, Yungil; Tsuda, Kenichi; Igarashi, Daisuke; Hillmer, Rachel A.; Sakakibara, Hitoshi; Myers, Chad L.; Katagiri, Fumiaki

2014-01-01

Summary How does robust and tunable behavior emerge in a complex biological network? We sought to understand this for the signaling network controlling pattern-triggered immunity (PTI) in Arabidopsis. A dynamic network model containing four major signaling sectors, the jasmonate, ethylene, PAD4, and salicylate sectors, which together explain up to 80% of the PTI level, was built using data for dynamic sector activities and PTI levels under exhaustive combinatorial sector perturbations. Our regularized multiple regression model had a high level of predictive power and captured known and unexpected signal flows in the network. The sole inhibitory sector in the model, the ethylene sector, was central to the network robustness via its inhibition of the jasmonate sector. The model's multiple input sites linked specific signal input patterns varying in strength and timing to different network response patterns, indicating a mechanism enabling tunability. PMID:24439900

Self-tuning bistable parametric feedback oscillator: Near-optimal amplitude maximization without model information

NASA Astrophysics Data System (ADS)

Braun, David J.; Sutas, Andrius; Vijayakumar, Sethu

2017-01-01

Theory predicts that parametrically excited oscillators, tuned to operate under resonant condition, are capable of large-amplitude oscillation useful in diverse applications, such as signal amplification, communication, and analog computation. However, due to amplitude saturation caused by nonlinearity, lack of robustness to model uncertainty, and limited sensitivity to parameter modulation, these oscillators require fine-tuning and strong modulation to generate robust large-amplitude oscillation. Here we present a principle of self-tuning parametric feedback excitation that alleviates the above-mentioned limitations. This is achieved using a minimalistic control implementation that performs (i) self-tuning (slow parameter adaptation) and (ii) feedback pumping (fast parameter modulation), without sophisticated signal processing past observations. The proposed approach provides near-optimal amplitude maximization without requiring model-based control computation, previously perceived inevitable to implement optimal control principles in practical application. Experimental implementation of the theory shows that the oscillator self-tunes itself near to the onset of dynamic bifurcation to achieve extreme sensitivity to small resonant parametric perturbations. As a result, it achieves large-amplitude oscillations by capitalizing on the effect of nonlinearity, despite substantial model uncertainties and strong unforeseen external perturbations. We envision the present finding to provide an effective and robust approach to parametric excitation when it comes to real-world application.

Tuning and predicting the wetting of nanoengineered material surface

NASA Astrophysics Data System (ADS)

Ramiasa-MacGregor, M.; Mierczynska, A.; Sedev, R.; Vasilev, K.

2016-02-01

The wetting of a material can be tuned by changing the roughness on its surface. Recent advances in the field of nanotechnology open exciting opportunities to control macroscopic wetting behaviour. Yet, the benchmark theories used to describe the wettability of macroscopically rough surfaces fail to fully describe the wetting behaviour of systems with topographical features at the nanoscale. To shed light on the events occurring at the nanoscale we have utilised model gradient substrata where surface nanotopography was tailored in a controlled and robust manner. The intrinsic wettability of the coatings was varied from hydrophilic to hydrophobic. The measured water contact angle could not be described by the classical theories. We developed an empirical model that effectively captures the experimental data, and further enables us to predict the wetting of surfaces with nanoscale roughness by considering the physical and chemical properties of the material. The fundamental insights presented here are important for the rational design of advanced materials having tailored surface nanotopography with predictable wettability.The wetting of a material can be tuned by changing the roughness on its surface. Recent advances in the field of nanotechnology open exciting opportunities to control macroscopic wetting behaviour. Yet, the benchmark theories used to describe the wettability of macroscopically rough surfaces fail to fully describe the wetting behaviour of systems with topographical features at the nanoscale. To shed light on the events occurring at the nanoscale we have utilised model gradient substrata where surface nanotopography was tailored in a controlled and robust manner. The intrinsic wettability of the coatings was varied from hydrophilic to hydrophobic. The measured water contact angle could not be described by the classical theories. We developed an empirical model that effectively captures the experimental data, and further enables us to predict the wetting of surfaces with nanoscale roughness by considering the physical and chemical properties of the material. The fundamental insights presented here are important for the rational design of advanced materials having tailored surface nanotopography with predictable wettability. Electronic supplementary information (ESI) available: Detailed characterization of the nanorough substrates and model derivation. See DOI: 10.1039/c5nr08329j

Aeroservoelasticity

NASA Technical Reports Server (NTRS)

Noll, Thomas E.

1990-01-01

The paper describes recent accomplishments and current research projects along four main thrusts in aeroservoelasticity at NASA Langley. One activity focuses on enhancing the modeling and analysis procedures to accurately predict aeroservoelastic interactions. Improvements to the minimum-state method of approximating unsteady aerodynamics are shown to provide precise low-order models for design and simulation tasks. Recent extensions in aerodynamic correction-factor methodology are also described. With respect to analysis procedures, the paper reviews novel enhancements to matched filter theory and random process theory for predicting the critical gust profile and the associated time-correlated gust loads for structural design considerations. Two research projects leading towards improved design capability are also summarized: (1) an integrated structure/control design capability and (2) procedures for obtaining low-order robust digital control laws for aeroelastic applications.

Pigeons (C. livia) Follow Their Head during Turning Flight: Head Stabilization Underlies the Visual Control of Flight.

PubMed

Ros, Ivo G; Biewener, Andrew A

2017-01-01

Similar flight control principles operate across insect and vertebrate fliers. These principles indicate that robust solutions have evolved to meet complex behavioral challenges. Following from studies of visual and cervical feedback control of flight in insects, we investigate the role of head stabilization in providing feedback cues for controlling turning flight in pigeons. Based on previous observations that the eyes of pigeons remain at relatively fixed orientations within the head during flight, we test potential sensory control inputs derived from head and body movements during 90° aerial turns. We observe that periods of angular head stabilization alternate with rapid head repositioning movements (head saccades), and confirm that control of head motion is decoupled from aerodynamic and inertial forces acting on the bird's continuously rotating body during turning flapping flight. Visual cues inferred from head saccades correlate with changes in flight trajectory; whereas the magnitude of neck bending predicts angular changes in body position. The control of head motion to stabilize a pigeon's gaze may therefore facilitate extraction of important motion cues, in addition to offering mechanisms for controlling body and wing movements. Strong similarities between the sensory flight control of birds and insects may also inspire novel designs of robust controllers for human-engineered autonomous aerial vehicles.

Pigeons (C. livia) Follow Their Head during Turning Flight: Head Stabilization Underlies the Visual Control of Flight

PubMed Central

Ros, Ivo G.; Biewener, Andrew A.

2017-01-01

Similar flight control principles operate across insect and vertebrate fliers. These principles indicate that robust solutions have evolved to meet complex behavioral challenges. Following from studies of visual and cervical feedback control of flight in insects, we investigate the role of head stabilization in providing feedback cues for controlling turning flight in pigeons. Based on previous observations that the eyes of pigeons remain at relatively fixed orientations within the head during flight, we test potential sensory control inputs derived from head and body movements during 90° aerial turns. We observe that periods of angular head stabilization alternate with rapid head repositioning movements (head saccades), and confirm that control of head motion is decoupled from aerodynamic and inertial forces acting on the bird's continuously rotating body during turning flapping flight. Visual cues inferred from head saccades correlate with changes in flight trajectory; whereas the magnitude of neck bending predicts angular changes in body position. The control of head motion to stabilize a pigeon's gaze may therefore facilitate extraction of important motion cues, in addition to offering mechanisms for controlling body and wing movements. Strong similarities between the sensory flight control of birds and insects may also inspire novel designs of robust controllers for human-engineered autonomous aerial vehicles. PMID:29249929

Mathematical Modeling of RNA-Based Architectures for Closed Loop Control of Gene Expression.

PubMed

Agrawal, Deepak K; Tang, Xun; Westbrook, Alexandra; Marshall, Ryan; Maxwell, Colin S; Lucks, Julius; Noireaux, Vincent; Beisel, Chase L; Dunlop, Mary J; Franco, Elisa

2018-05-08

Feedback allows biological systems to control gene expression precisely and reliably, even in the presence of uncertainty, by sensing and processing environmental changes. Taking inspiration from natural architectures, synthetic biologists have engineered feedback loops to tune the dynamics and improve the robustness and predictability of gene expression. However, experimental implementations of biomolecular control systems are still far from satisfying performance specifications typically achieved by electrical or mechanical control systems. To address this gap, we present mathematical models of biomolecular controllers that enable reference tracking, disturbance rejection, and tuning of the temporal response of gene expression. These controllers employ RNA transcriptional regulators to achieve closed loop control where feedback is introduced via molecular sequestration. Sensitivity analysis of the models allows us to identify which parameters influence the transient and steady state response of a target gene expression process, as well as which biologically plausible parameter values enable perfect reference tracking. We quantify performance using typical control theory metrics to characterize response properties and provide clear selection guidelines for practical applications. Our results indicate that RNA regulators are well-suited for building robust and precise feedback controllers for gene expression. Additionally, our approach illustrates several quantitative methods useful for assessing the performance of biomolecular feedback control systems.

Affiliation and control in marital interaction: interpersonal complementarity is present but is not associated with affect or relationship quality.

PubMed

Cundiff, Jenny M; Smith, Timothy W; Butner, Jonathan; Critchfield, Kenneth L; Nealey-Moore, Jill

2015-01-01

The principle of complementarity in interpersonal theory states that an actor's behavior tends to "pull, elicit, invite, or evoke" responses from interaction partners who are similar in affiliation (i.e., warmth vs. hostility) and opposite in control (i.e., dominance vs. submissiveness). Furthermore, complementary interactions are proposed to evoke less negative affect and promote greater relationship satisfaction. These predictions were examined in two studies of married couples. Results suggest that complementarity in affiliation describes a robust general pattern of marital interaction, but complementarity in control varies across contexts. Consistent with behavioral models of marital interaction, greater levels of affiliation and lower control by partners-not complementarity in affiliation or control-were associated with less anger and anxiety and greater relationship quality. Partners' levels of affiliation and control combined in ways other than complementarity-mostly additively, but sometimes synergistically-to predict negative affect and relationship satisfaction. © 2014 by the Society for Personality and Social Psychology, Inc.

The design and optimization for light-algae bioreactor controller based on Artificial Neural Network-Model Predictive Control

NASA Astrophysics Data System (ADS)

Hu, Dawei; Liu, Hong; Yang, Chenliang; Hu, Enzhu

As a subsystem of the bioregenerative life support system (BLSS), light-algae bioreactor (LABR) has properties of high reaction rate, efficiently synthesizing microalgal biomass, absorbing CO2 and releasing O2, so it is significant for BLSS to provide food and maintain gas balance. In order to manipulate the LABR properly, it has been designed as a closed-loop control system, and technology of Artificial Neural Network-Model Predictive Control (ANN-MPC) is applied to design the controller for LABR in which green microalgae, Spirulina platensis is cultivated continuously. The conclusion is drawn by computer simulation that ANN-MPC controller can intelligently learn the complicated dynamic performances of LABR, and automatically, robustly and self-adaptively regulate the light intensity illuminating on the LABR, hence make the growth of microalgae in the LABR be changed in line with the references, meanwhile provide appropriate damping to improve markedly the transient response performance of LABR.

Incremental inverse kinematics based vision servo for autonomous robotic capture of non-cooperative space debris

NASA Astrophysics Data System (ADS)

Dong, Gangqi; Zhu, Z. H.

2016-04-01

This paper proposed a new incremental inverse kinematics based vision servo approach for robotic manipulators to capture a non-cooperative target autonomously. The target's pose and motion are estimated by a vision system using integrated photogrammetry and EKF algorithm. Based on the estimated pose and motion of the target, the instantaneous desired position of the end-effector is predicted by inverse kinematics and the robotic manipulator is moved incrementally from its current configuration subject to the joint speed limits. This approach effectively eliminates the multiple solutions in the inverse kinematics and increases the robustness of the control algorithm. The proposed approach is validated by a hardware-in-the-loop simulation, where the pose and motion of the non-cooperative target is estimated by a real vision system. The simulation results demonstrate the effectiveness and robustness of the proposed estimation approach for the target and the incremental control strategy for the robotic manipulator.

Efficient Computation of Info-Gap Robustness for Finite Element Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stull, Christopher J.; Hemez, Francois M.; Williams, Brian J.

2012-07-05

A recent research effort at LANL proposed info-gap decision theory as a framework by which to measure the predictive maturity of numerical models. Info-gap theory explores the trade-offs between accuracy, that is, the extent to which predictions reproduce the physical measurements, and robustness, that is, the extent to which predictions are insensitive to modeling assumptions. Both accuracy and robustness are necessary to demonstrate predictive maturity. However, conducting an info-gap analysis can present a formidable challenge, from the standpoint of the required computational resources. This is because a robustness function requires the resolution of multiple optimization problems. This report offers anmore » alternative, adjoint methodology to assess the info-gap robustness of Ax = b-like numerical models solved for a solution x. Two situations that can arise in structural analysis and design are briefly described and contextualized within the info-gap decision theory framework. The treatments of the info-gap problems, using the adjoint methodology are outlined in detail, and the latter problem is solved for four separate finite element models. As compared to statistical sampling, the proposed methodology offers highly accurate approximations of info-gap robustness functions for the finite element models considered in the report, at a small fraction of the computational cost. It is noted that this report considers only linear systems; a natural follow-on study would extend the methodologies described herein to include nonlinear systems.« less

Transonic Flutter Suppression Control Law Design, Analysis and Wind Tunnel Results

NASA Technical Reports Server (NTRS)

Mukhopadhyay, Vivek

1999-01-01

The benchmark active controls technology and wind tunnel test program at NASA Langley Research Center was started with the objective to investigate the nonlinear, unsteady aerodynamics and active flutter suppression of wings in transonic flow. The paper will present the flutter suppression control law design process, numerical nonlinear simulation and wind tunnel test results for the NACA 0012 benchmark active control wing model. The flutter suppression control law design processes using (1) classical, (2) linear quadratic Gaussian (LQG), and (3) minimax techniques are described. A unified general formulation and solution for the LQG and minimax approaches, based on the steady state differential game theory is presented. Design considerations for improving the control law robustness and digital implementation are outlined. It was shown that simple control laws when properly designed based on physical principles, can suppress flutter with limited control power even in the presence of transonic shocks and flow separation. In wind tunnel tests in air and heavy gas medium, the closed-loop flutter dynamic pressure was increased to the tunnel upper limit of 200 psf The control law robustness and performance predictions were verified in highly nonlinear flow conditions, gain and phase perturbations, and spoiler deployment. A non-design plunge instability condition was also successfully suppressed.

Transonic Flutter Suppression Control Law Design, Analysis and Wind-Tunnel Results

NASA Technical Reports Server (NTRS)

Mukhopadhyay, Vivek

1999-01-01

The benchmark active controls technology and wind tunnel test program at NASA Langley Research Center was started with the objective to investigate the nonlinear, unsteady aerodynamics and active flutter suppression of wings in transonic flow. The paper will present the flutter suppression control law design process, numerical nonlinear simulation and wind tunnel test results for the NACA 0012 benchmark active control wing model. The flutter suppression control law design processes using (1) classical, (2) linear quadratic Gaussian (LQG), and (3) minimax techniques are described. A unified general formulation and solution for the LQG and minimax approaches, based on the steady state differential game theory is presented. Design considerations for improving the control law robustness and digital implementation are outlined. It was shown that simple control laws when properly designed based on physical principles, can suppress flutter with limited control power even in the presence of transonic shocks and flow separation. In wind tunnel tests in air and heavy gas medium, the closed-loop flutter dynamic pressure was increased to the tunnel upper limit of 200 psf. The control law robustness and performance predictions were verified in highly nonlinear flow conditions, gain and phase perturbations, and spoiler deployment. A non-design plunge instability condition was also successfully suppressed.

Transonic Flutter Suppression Control Law Design Using Classical and Optimal Techniques with Wind-Tunnel Results

NASA Technical Reports Server (NTRS)

Mukhopadhyay, Vivek

1999-01-01

The benchmark active controls technology and wind tunnel test program at NASA Langley Research Center was started with the objective to investigate the nonlinear, unsteady aerodynamics and active flutter suppression of wings in transonic flow. The paper will present the flutter suppression control law design process, numerical nonlinear simulation and wind tunnel test results for the NACA 0012 benchmark active control wing model. The flutter suppression control law design processes using (1) classical, (2) linear quadratic Gaussian (LQG), and (3) minimax techniques are described. A unified general formulation and solution for the LQG and minimax approaches, based on the steady state differential game theory is presented. Design considerations for improving the control law robustness and digital implementation are outlined. It was shown that simple control laws when properly designed based on physical principles, can suppress flutter with limited control power even in the presence of transonic shocks and flow separation. In wind tunnel tests in air and heavy gas medium, the closed-loop flutter dynamic pressure was increased to the tunnel upper limit of 200 psf. The control law robustness and performance predictions were verified in highly nonlinear flow conditions, gain and phase perturbations, and spoiler deployment. A non-design plunge instability condition was also successfully suppressed.

Fault diagnosis and fault-tolerant finite control set-model predictive control of a multiphase voltage-source inverter supplying BLDC motor.

PubMed

Salehifar, Mehdi; Moreno-Equilaz, Manuel

2016-01-01

Due to its fault tolerance, a multiphase brushless direct current (BLDC) motor can meet high reliability demand for application in electric vehicles. The voltage-source inverter (VSI) supplying the motor is subjected to open circuit faults. Therefore, it is necessary to design a fault-tolerant (FT) control algorithm with an embedded fault diagnosis (FD) block. In this paper, finite control set-model predictive control (FCS-MPC) is developed to implement the fault-tolerant control algorithm of a five-phase BLDC motor. The developed control method is fast, simple, and flexible. A FD method based on available information from the control block is proposed; this method is simple, robust to common transients in motor and able to localize multiple open circuit faults. The proposed FD and FT control algorithm are embedded in a five-phase BLDC motor drive. In order to validate the theory presented, simulation and experimental results are conducted on a five-phase two-level VSI supplying a five-phase BLDC motor. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Does a crouched leg posture enhance running stability and robustness?

PubMed

Blum, Yvonne; Birn-Jeffery, Aleksandra; Daley, Monica A; Seyfarth, Andre

2011-07-21

Humans and birds both walk and run bipedally on compliant legs. However, differences in leg architecture may result in species-specific leg control strategies as indicated by the observed gait patterns. In this work, control strategies for stable running are derived based on a conceptual model and compared with experimental data on running humans and pheasants (Phasianus colchicus). From a model perspective, running with compliant legs can be represented by the planar spring mass model and stabilized by applying swing leg control. Here, linear adaptations of the three leg parameters, leg angle, leg length and leg stiffness during late swing phase are assumed. Experimentally observed kinematic control parameters (leg rotation and leg length change) of human and avian running are compared, and interpreted within the context of this model, with specific focus on stability and robustness characteristics. The results suggest differences in stability characteristics and applied control strategies of human and avian running, which may relate to differences in leg posture (straight leg posture in humans, and crouched leg posture in birds). It has been suggested that crouched leg postures may improve stability. However, as the system of control strategies is overdetermined, our model findings suggest that a crouched leg posture does not necessarily enhance running stability. The model also predicts different leg stiffness adaptation rates for human and avian running, and suggests that a crouched avian leg posture, which is capable of both leg shortening and lengthening, allows for stable running without adjusting leg stiffness. In contrast, in straight-legged human running, the preparation of the ground contact seems to be more critical, requiring leg stiffness adjustment to remain stable. Finally, analysis of a simple robustness measure, the normalized maximum drop, suggests that the crouched leg posture may provide greater robustness to changes in terrain height. Copyright © 2011 Elsevier Ltd. All rights reserved.

A new robust adaptive controller for vibration control of active engine mount subjected to large uncertainties

NASA Astrophysics Data System (ADS)

Fakhari, Vahid; Choi, Seung-Bok; Cho, Chang-Hyun

2015-04-01

This work presents a new robust model reference adaptive control (MRAC) for vibration control caused from vehicle engine using an electromagnetic type of active engine mount. Vibration isolation performances of the active mount associated with the robust controller are evaluated in the presence of large uncertainties. As a first step, an active mount with linear solenoid actuator is prepared and its dynamic model is identified via experimental test. Subsequently, a new robust MRAC based on the gradient method with σ-modification is designed by selecting a proper reference model. In designing the robust adaptive control, structured (parametric) uncertainties in the stiffness of the passive part of the mount and in damping ratio of the active part of the mount are considered to investigate the robustness of the proposed controller. Experimental and simulation results are presented to evaluate performance focusing on the robustness behavior of the controller in the face of large uncertainties. The obtained results show that the proposed controller can sufficiently provide the robust vibration control performance even in the presence of large uncertainties showing an effective vibration isolation.

Hybrid Active/Passive Control of Sound Radiation from Panels with Constrained Layer Damping and Model Predictive Feedback Control

NASA Technical Reports Server (NTRS)

Cabell, Randolph H.; Gibbs, Gary P.

2000-01-01

There has been considerable interest over the past several years in applying feedback control methods to problems of structural acoustics. One problem of particular interest is the control of sound radiation from aircraft panels excited on one side by a turbulent boundary layer (TBL). TBL excitation appears as many uncorrelated sources acting on the panel, which makes it difficult to find a single reference signal that is coherent with the excitation. Feedback methods have no need for a reference signal, and are thus suited to this problem. Some important considerations for the structural acoustics problem include the fact that the required controller bandwidth can easily extend to several hundred Hertz, so a digital controller would have to operate at a few kilohertz. In addition, aircraft panel structures have a reasonably high modal density over this frequency range. A model based controller must therefore handle the modally dense system, or have some way to reduce the bandwidth of the problem. Further complicating the problem is the fact that the stiffness and dynamic properties of an aircraft panel can vary considerably during flight due to altitude changes resulting in significant resonant frequency shifts. These considerations make the tradeoff between robustness to changes in the system being controlled and controller performance especially important. Recent papers concerning the design and implementation of robust controllers for structural acoustic problems highlight the need to consider both performance and robustness when designing the controller. While robust control methods such as H1 can be used to balance performance and robustness, their implementation is not easy and requires assumptions about the types of uncertainties in the plant being controlled. Achieving a useful controller design may require many tradeoff studies of different types of parametric uncertainties in the system. Another approach to achieving robustness to plant changes is to make the controller adaptive. For example, a mathematical model of the plant could be periodically updated as the plant changes, and the feedback gains recomputed from the updated model. To be practical, this approach requires a simple plant model that can be updated quickly with reasonable computational requirements. A recent paper by the authors discussed one way to simplify a feedback controller, by reducing the number of actuators and sensors needed for good performance. The work was done on a tensioned aircraft-style panel excited on one side by TBL flow in a low speed wind tunnel. Actuation was provided by a piezoelectric (PZT) actuator mounted on the center of the panel. For sensing, the responses of four accelerometers, positioned to approximate the response of the first radiation mode of the panel, were summed and fed back through the controller. This single input-single output topology was found to have nearly the same noise reduction performance as a controller with fifteen accelerometers and three PZT patches. This paper extends the previous results by looking at how constrained layer damping (CLD) on a panel can be used to enhance the performance of the feedback controller thus providing a more robust and efficient hybrid active/passive system. The eventual goal is to use the CLD to reduce sound radiation at high frequencies, then implement a very simple, reduced order, low sample rate adaptive controller to attenuate sound radiation at low frequencies. Additionally this added damping smoothes phase transitions over the bandwidth which promotes robustness to natural frequency shifts. Experiments were conducted in a transmission loss facility on a clamped-clamped aluminum panel driven on one side by a loudspeaker. A generalized predictive control (GPC) algorithm, which is suited to online adaptation of its parameters, was used in single input-single output and multiple input-single output configurations. Because this was a preliminary look at the potential constrained layer damping for adaptive control, static feedback control with no online adaptation was used. Two configurations of CLD in addition to a bare panel configuration were studied. For each CLD configuration, two sensor arrangements for the feedback controller were compared. The first arrangement used fifteen accelerometers on the panel to estimate the responses of the first six radiation modes of the panel. The second sensor arrangement was simpler, using the summed responses of only four accelerometers to approximate the response of the first radiation mode of the panel. In all cases a PZT patch was mounted at the center of the panel for control input. The performance of the controller was quantified using the responses of the fifteen accelerometers on the panel to estimate radiated sound power. The paper begins with a brief discussion of the GPC algorithm and the experimental setup. The experimental results are discussed next, comparing the CLD and sensor configurations, followed by discussion and conclusions.

A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

NASA Astrophysics Data System (ADS)

Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

Robust output tracking control of a laboratory helicopter for automatic landing

NASA Astrophysics Data System (ADS)

Liu, Hao; Lu, Geng; Zhong, Yisheng

2014-11-01

In this paper, robust output tracking control problem of a laboratory helicopter for automatic landing in high seas is investigated. The motion of the helicopter is required to synchronise with that of an oscillating platform, e.g. the deck of a vessel subject to wave-induced motions. A robust linear time-invariant output feedback controller consisting of a nominal controller and a robust compensator is designed. The robust compensator is introduced to restrain the influences of parametric uncertainties, nonlinearities and external disturbances. It is shown that robust stability and robust tracking property can be achieved simultaneously. Experimental results on the laboratory helicopter for automatic landing demonstrate the effectiveness of the designed control approach.

Impact of rain gauge quality control and interpolation on streamflow simulation: an application to the Warwick catchment, Australia

NASA Astrophysics Data System (ADS)

Liu, Shulun; Li, Yuan; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

2017-12-01

Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash–Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In term of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2007–31/12/2011) and four gauges during the validation period (01/01/2012–30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored.

Impact of rain gauge quality control and interpolation on streamflow simulation: an application to the Warwick catchment, Australia

NASA Astrophysics Data System (ADS)

Liu, Shulun; Li, Yuan; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

2018-01-01

Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash–Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In term of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2007–31/12/2011) and four gauges during the validation period (01/01/2012–30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored.

Respiratory sinus arrhythmia reactivity to a sad film predicts depression symptom improvement and symptomatic trajectory

PubMed Central

Panaite, Vanessa; Hindash, Alexandra Cowden; Bylsma, Lauren M.; Small, Brent J.; Salomon, Kristen; Rottenberg, Jonathan

2015-01-01

Respiratory sinus arrhythmia (RSA) reactivity, an index of cardiac vagal tone, has been linked to self-regulation and the severity and course of depression (Rottenberg, 2007). Although initial data supports the proposition that RSA withdrawal during a sad film is a specific predictor of depression course (Fraguas, 2007; Rottenberg, 2005), the robustness and specificity of this finding are unclear. To provide a stronger test, RSA reactivity to three emotion films (happy, sad, fear) and to a more robust stressor, a speech task, were examined in currently depressed individuals (n = 37), who were assessed for their degree of symptomatic improvement over 30 weeks. Robust RSA reactivity to the sad film uniquely predicted overall symptom improvement over 30 weeks. RSA reactivity to both sad and stressful stimuli predicted the speed and maintenance of symptomatic improvement. The current analyses provide the most robust support to date that RSA withdrawal to sad stimuli (but not stressful) has specificity in predicting the overall symptomatic improvement. In contrast, RSA reactivity to negative stimuli (both sad and stressful) predicted the trajectory of depression course. Patients’ engagement with sad stimuli may be an important sign to attend to in therapeutic settings. PMID:26681648

A fast, robust and tunable synthetic gene oscillator.

PubMed

Stricker, Jesse; Cookson, Scott; Bennett, Matthew R; Mather, William H; Tsimring, Lev S; Hasty, Jeff

2008-11-27

One defining goal of synthetic biology is the development of engineering-based approaches that enable the construction of gene-regulatory networks according to 'design specifications' generated from computational modelling. This approach provides a systematic framework for exploring how a given regulatory network generates a particular phenotypic behaviour. Several fundamental gene circuits have been developed using this approach, including toggle switches and oscillators, and these have been applied in new contexts such as triggered biofilm development and cellular population control. Here we describe an engineered genetic oscillator in Escherichia coli that is fast, robust and persistent, with tunable oscillatory periods as fast as 13 min. The oscillator was designed using a previously modelled network architecture comprising linked positive and negative feedback loops. Using a microfluidic platform tailored for single-cell microscopy, we precisely control environmental conditions and monitor oscillations in individual cells through multiple cycles. Experiments reveal remarkable robustness and persistence of oscillations in the designed circuit; almost every cell exhibited large-amplitude fluorescence oscillations throughout observation runs. The oscillatory period can be tuned by altering inducer levels, temperature and the media source. Computational modelling demonstrates that the key design principle for constructing a robust oscillator is a time delay in the negative feedback loop, which can mechanistically arise from the cascade of cellular processes involved in forming a functional transcription factor. The positive feedback loop increases the robustness of the oscillations and allows for greater tunability. Examination of our refined model suggested the existence of a simplified oscillator design without positive feedback, and we construct an oscillator strain confirming this computational prediction.

Effect of interaction strength on robustness of controlling edge dynamics in complex networks

NASA Astrophysics Data System (ADS)

Pang, Shao-Peng; Hao, Fei

2018-05-01

Robustness plays a critical role in the controllability of complex networks to withstand failures and perturbations. Recent advances in the edge controllability show that the interaction strength among edges plays a more important role than network structure. Therefore, we focus on the effect of interaction strength on the robustness of edge controllability. Using three categories of all edges to quantify the robustness, we develop a universal framework to evaluate and analyze the robustness in complex networks with arbitrary structures and interaction strengths. Applying our framework to a large number of model and real-world networks, we find that the interaction strength is a dominant factor for the robustness in undirected networks. Meanwhile, the strongest robustness and the optimal edge controllability in undirected networks can be achieved simultaneously. Different from the case of undirected networks, the robustness in directed networks is determined jointly by the interaction strength and the network's degree distribution. Moreover, a stronger robustness is usually associated with a larger number of driver nodes required to maintain full control in directed networks. This prompts us to provide an optimization method by adjusting the interaction strength to optimize the robustness of edge controllability.

Robust Fixed-Structure Controller Synthesis

NASA Technical Reports Server (NTRS)

Corrado, Joseph R.; Haddad, Wassim M.; Gupta, Kajal (Technical Monitor)

2000-01-01

The ability to develop an integrated control system design methodology for robust high performance controllers satisfying multiple design criteria and real world hardware constraints constitutes a challenging task. The increasingly stringent performance specifications required for controlling such systems necessitates a trade-off between controller complexity and robustness. The principle challenge of the minimal complexity robust control design is to arrive at a tractable control design formulation in spite of the extreme complexity of such systems. Hence, design of minimal complexitY robust controllers for systems in the face of modeling errors has been a major preoccupation of system and control theorists and practitioners for the past several decades.

Stability and optimised H∞ control of tripped and untripped vehicle rollover

NASA Astrophysics Data System (ADS)

Jin, Zhilin; Zhang, Lei; Zhang, Jiale; Khajepour, Amir

2016-10-01

Vehicle rollover is a serious traffic accident. In order to accurately evaluate the possibility of untripped and some special tripped vehicle rollovers, and to prevent vehicle rollover under unpredictable variations of parameters and harsh driving conditions, a new rollover index and an anti-roll control strategy are proposed in this paper. Taking deflections of steering and suspension induced by the roll at the axles into consideration, a six degrees of freedom dynamic model is established, including lateral, yaw, roll, and vertical motions of sprung and unsprung masses. From the vehicle dynamics theory, a new rollover index is developed to predict vehicle rollover risk under both untripped and special tripped situations. This new rollover index is validated by Carsim simulations. In addition, an H-infinity controller with electro hydraulic brake system is optimised by genetic algorithm to improve the anti-rollover performance of the vehicle. The stability and robustness of the active rollover prevention control system are analysed by some numerical simulations. The results show that the control system can improve the critical speed of vehicle rollover obviously, and has a good robustness for variations in the number of passengers and longitude position of the centre of gravity.

On-Line Robust Modal Stability Prediction using Wavelet Processing

NASA Technical Reports Server (NTRS)

Brenner, Martin J.; Lind, Rick

1998-01-01

Wavelet analysis for filtering and system identification has been used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins is reduced with parametric and nonparametric time- frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data is used to reduce the effects of external disturbances and unmodeled dynamics. Parametric estimates of modal stability are also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. The F-18 High Alpha Research Vehicle aeroservoelastic flight test data demonstrates improved robust stability prediction by extension of the stability boundary beyond the flight regime. Guidelines and computation times are presented to show the efficiency and practical aspects of these procedures for on-line implementation. Feasibility of the method is shown for processing flight data from time- varying nonstationary test points.

Info-gap robust-satisficing model of foraging behavior: do foragers optimize or satisfice?

PubMed

Carmel, Yohay; Ben-Haim, Yakov

2005-11-01

In this note we compare two mathematical models of foraging that reflect two competing theories of animal behavior: optimizing and robust satisficing. The optimal-foraging model is based on the marginal value theorem (MVT). The robust-satisficing model developed here is an application of info-gap decision theory. The info-gap robust-satisficing model relates to the same circumstances described by the MVT. We show how these two alternatives translate into specific predictions that at some points are quite disparate. We test these alternative predictions against available data collected in numerous field studies with a large number of species from diverse taxonomic groups. We show that a large majority of studies appear to support the robust-satisficing model and reject the optimal-foraging model.

The perceptual shaping of anticipatory actions.

PubMed

Maffei, Giovanni; Herreros, Ivan; Sanchez-Fibla, Marti; Friston, Karl J; Verschure, Paul F M J

2017-12-20

Humans display anticipatory motor responses to minimize the adverse effects of predictable perturbations. A widely accepted explanation for this behaviour relies on the notion of an inverse model that, learning from motor errors, anticipates corrective responses. Here, we propose and validate the alternative hypothesis that anticipatory control can be realized through a cascade of purely sensory predictions that drive the motor system, reflecting the causal sequence of the perceptual events preceding the error. We compare both hypotheses in a simulated anticipatory postural adjustment task. We observe that adaptation in the sensory domain, but not in the motor one, supports the robust and generalizable anticipatory control characteristic of biological systems. Our proposal unites the neurobiology of the cerebellum with the theory of active inference and provides a concrete implementation of its core tenets with great relevance both to our understanding of biological control systems and, possibly, to their emulation in complex artefacts. © 2017 The Author(s).

Speckle lithography for fabricating Gaussian, quasi-random 2D structures and black silicon structures.

PubMed

Bingi, Jayachandra; Murukeshan, Vadakke Matham

2015-12-18

Laser speckle pattern is a granular structure formed due to random coherent wavelet interference and generally considered as noise in optical systems including photolithography. Contrary to this, in this paper, we use the speckle pattern to generate predictable and controlled Gaussian random structures and quasi-random structures photo-lithographically. The random structures made using this proposed speckle lithography technique are quantified based on speckle statistics, radial distribution function (RDF) and fast Fourier transform (FFT). The control over the speckle size, density and speckle clustering facilitates the successful fabrication of black silicon with different surface structures. The controllability and tunability of randomness makes this technique a robust method for fabricating predictable 2D Gaussian random structures and black silicon structures. These structures can enhance the light trapping significantly in solar cells and hence enable improved energy harvesting. Further, this technique can enable efficient fabrication of disordered photonic structures and random media based devices.

MOST: most-similar ligand based approach to target prediction.

PubMed

Huang, Tao; Mi, Hong; Lin, Cheng-Yuan; Zhao, Ling; Zhong, Linda L D; Liu, Feng-Bin; Zhang, Ge; Lu, Ai-Ping; Bian, Zhao-Xiang

2017-03-11

Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power. Here we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone. In the case of aloe-emodin's laxative effect, MOST predicted that acetylcholinesterase was the mechanism-of-action target; in vivo studies validated this prediction. Using the MOST approach can result in highly accurate and robust target prediction. Integrated with a FDR control procedure, MOST provides a reliable framework for multiple-target inference. It has prospective applications in drug repurposing and mechanism-of-action target prediction.

The robust model predictive control based on mixed H2/H∞ approach with separated performance formulations and its ISpS analysis

NASA Astrophysics Data System (ADS)

Li, Dewei; Li, Jiwei; Xi, Yugeng; Gao, Furong

2017-12-01

In practical applications, systems are always influenced by parameter uncertainties and external disturbance. Both the H2 performance and the H∞ performance are important for the real applications. For a constrained system, the previous designs of mixed H2/H∞ robust model predictive control (RMPC) optimise one performance with the other performance requirement as a constraint. But the two performances cannot be optimised at the same time. In this paper, an improved design of mixed H2/H∞ RMPC for polytopic uncertain systems with external disturbances is proposed to optimise them simultaneously. In the proposed design, the original uncertain system is decomposed into two subsystems by the additive character of linear systems. Two different Lyapunov functions are used to separately formulate the two performance indices for the two subsystems. Then, the proposed RMPC is designed to optimise both the two performances by the weighting method with the satisfaction of the H∞ performance requirement. Meanwhile, to make the design more practical, a simplified design is also developed. The recursive feasible conditions of the proposed RMPC are discussed and the closed-loop input state practical stable is proven. The numerical examples reflect the enlarged feasible region and the improved performance of the proposed design.

A stochastic optimal feedforward and feedback control methodology for superagility

NASA Technical Reports Server (NTRS)

Halyo, Nesim; Direskeneli, Haldun; Taylor, Deborah B.

1992-01-01

A new control design methodology is developed: Stochastic Optimal Feedforward and Feedback Technology (SOFFT). Traditional design techniques optimize a single cost function (which expresses the design objectives) to obtain both the feedforward and feedback control laws. This approach places conflicting demands on the control law such as fast tracking versus noise atttenuation/disturbance rejection. In the SOFFT approach, two cost functions are defined. The feedforward control law is designed to optimize one cost function, the feedback optimizes the other. By separating the design objectives and decoupling the feedforward and feedback design processes, both objectives can be achieved fully. A new measure of command tracking performance, Z-plots, is also developed. By analyzing these plots at off-nominal conditions, the sensitivity or robustness of the system in tracking commands can be predicted. Z-plots provide an important tool for designing robust control systems. The Variable-Gain SOFFT methodology was used to design a flight control system for the F/A-18 aircraft. It is shown that SOFFT can be used to expand the operating regime and provide greater performance (flying/handling qualities) throughout the extended flight regime. This work was performed under the NASA SBIR program. ICS plans to market the software developed as a new module in its commercial CACSD software package: ACET.

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Zhu, Xiaocong

2013-09-01

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.

Non-fragile observer-based output feedback control for polytopic uncertain system under distributed model predictive control approach

NASA Astrophysics Data System (ADS)

Zhu, Kaiqun; Song, Yan; Zhang, Sunjie; Zhong, Zhaozhun

2017-07-01

In this paper, a non-fragile observer-based output feedback control problem for the polytopic uncertain system under distributed model predictive control (MPC) approach is discussed. By decomposing the global system into some subsystems, the computation complexity is reduced, so it follows that the online designing time can be saved.Moreover, an observer-based output feedback control algorithm is proposed in the framework of distributed MPC to deal with the difficulties in obtaining the states measurements. In this way, the presented observer-based output-feedback MPC strategy is more flexible and applicable in practice than the traditional state-feedback one. What is more, the non-fragility of the controller has been taken into consideration in favour of increasing the robustness of the polytopic uncertain system. After that, a sufficient stability criterion is presented by using Lyapunov-like functional approach, meanwhile, the corresponding control law and the upper bound of the quadratic cost function are derived by solving an optimisation subject to convex constraints. Finally, some simulation examples are employed to show the effectiveness of the method.

Intelligent robust control for uncertain nonlinear time-varying systems and its application to robotic systems.

PubMed

Chang, Yeong-Chan

2005-12-01

This paper addresses the problem of designing adaptive fuzzy-based (or neural network-based) robust controls for a large class of uncertain nonlinear time-varying systems. This class of systems can be perturbed by plant uncertainties, unmodeled perturbations, and external disturbances. Nonlinear H(infinity) control technique incorporated with adaptive control technique and VSC technique is employed to construct the intelligent robust stabilization controller such that an H(infinity) control is achieved. The problem of the robust tracking control design for uncertain robotic systems is employed to demonstrate the effectiveness of the developed robust stabilization control scheme. Therefore, an intelligent robust tracking controller for uncertain robotic systems in the presence of high-degree uncertainties can easily be implemented. Its solution requires only to solve a linear algebraic matrix inequality and a satisfactorily transient and asymptotical tracking performance is guaranteed. A simulation example is made to confirm the performance of the developed control algorithms.

A robust classic.

PubMed

Kutzner, Florian; Vogel, Tobias; Freytag, Peter; Fiedler, Klaus

2011-01-01

In the present research, we argue for the robustness of illusory correlations (ICs, Hamilton & Gifford, 1976) regarding two boundary conditions suggested in previous research. First, we argue that ICs are maintained under extended experience. Using simulations, we derive conflicting predictions. Whereas noise-based accounts predict ICs to be maintained (Fielder, 2000; Smith, 1991), a prominent account based on discrepancy-reducing feedback learning predicts ICs to disappear (Van Rooy et al., 2003). An experiment involving 320 observations with majority and minority members supports the claim that ICs are maintained. Second, we show that actively using the stereotype to make predictions that are met with reward and punishment does not eliminate the bias. In addition, participants' operant reactions afford a novel online measure of ICs. In sum, our findings highlight the robustness of ICs that can be explained as a result of unbiased but noisy learning.

Wavelet Filtering to Reduce Conservatism in Aeroservoelastic Robust Stability Margins

NASA Technical Reports Server (NTRS)

Brenner, Marty; Lind, Rick

1998-01-01

Wavelet analysis for filtering and system identification was used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins was reduced with parametric and nonparametric time-frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data was used to reduce the effects of external desirableness and unmodeled dynamics. Parametric estimates of modal stability were also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. F-18 high Alpha Research Vehicle aeroservoelastic flight test data demonstrated improved robust stability prediction by extension of the stability boundary beyond the flight regime.

Automatic speech recognition using a predictive echo state network classifier.

PubMed

Skowronski, Mark D; Harris, John G

2007-04-01

We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

Robust tracking control of a magnetically suspended rigid body

NASA Technical Reports Server (NTRS)

Lim, Kyong B.; Cox, David E.

1994-01-01

This study is an application of H-infinity and micro-synthesis for designing robust tracking controllers for the Large Angle Magnetic Suspension Test Facility. The modeling, design, analysis, simulation, and testing of a control law that guarantees tracking performance under external disturbances and model uncertainties is investigated. The type of uncertainties considered and the tracking performance metric used is discussed. This study demonstrates the tradeoff between tracking performance at low frequencies and robustness at high frequencies. Two sets of controllers were designed and tested. The first set emphasized performance over robustness, while the second set traded off performance for robustness. Comparisons of simulation and test results are also included. Current simulation and experimental results indicate that reasonably good robust tracking performance can be attained for this system using multivariable robust control approach.

Predictive Control of the Blood Glucose Level in Type I Diabetic Patient Using Delay Differential Equation Wang Model.

PubMed

Esna-Ashari, Mojgan; Zekri, Maryam; Askari, Masood; Khalili, Noushin

2017-01-01

Because of increasing risk of diabetes, the measurement along with control of blood sugar has been of great importance in recent decades. In type I diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose leading to low level of glucose. To control blood glucose (BG), the insulin must be injected to the body. This paper proposes a method for BG level regulation in type I diabetes. The control strategy is based on nonlinear model predictive control. The aim of the proposed controller optimized with genetics algorithms is to measure BG level each time and predict it for the next time interval. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. Consequently, this method can decrease the risk of hypoglycemia, a lethal phenomenon in regulating BG level in diabetes caused by a low BG level. Two delay differential equation models, namely Wang model and Enhanced Wang model, are applied as controller model and plant, respectively. The simulation results exhibit an acceptable performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. As a result, if the nutrition of the person decreases instantly, the hypoglycemia will not happen. Furthermore, comparing this method with other works, it was shown that the new method outperforms previous studies.

Predictive Control of the Blood Glucose Level in Type I Diabetic Patient Using Delay Differential Equation Wang Model

PubMed Central

Esna-Ashari, Mojgan; Zekri, Maryam; Askari, Masood; Khalili, Noushin

2017-01-01

Because of increasing risk of diabetes, the measurement along with control of blood sugar has been of great importance in recent decades. In type I diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose leading to low level of glucose. To control blood glucose (BG), the insulin must be injected to the body. This paper proposes a method for BG level regulation in type I diabetes. The control strategy is based on nonlinear model predictive control. The aim of the proposed controller optimized with genetics algorithms is to measure BG level each time and predict it for the next time interval. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. Consequently, this method can decrease the risk of hypoglycemia, a lethal phenomenon in regulating BG level in diabetes caused by a low BG level. Two delay differential equation models, namely Wang model and Enhanced Wang model, are applied as controller model and plant, respectively. The simulation results exhibit an acceptable performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. As a result, if the nutrition of the person decreases instantly, the hypoglycemia will not happen. Furthermore, comparing this method with other works, it was shown that the new method outperforms previous studies. PMID:28487828

Feedforward/feedback control synthesis for performance and robustness

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang

1990-01-01

Both feedforward and feedback control approaches for uncertain dynamical systems are investigated. The control design objective is to achieve a fast settling time (high performance) and robustness (insensitivity) to plant modeling uncertainty. Preshapong of an ideal, time-optimal control input using a 'tapped-delay' filter is shown to provide a rapid maneuver with robust performance. A robust, non-minimum-phase feedback controller is synthesized with particular emphasis on its proper implementation for a non-zero set-point control problem. The proposed feedforward/feedback control approach is robust for a certain class of uncertain dynamical systems, since the control input command computed for a given desired output does not depend on the plant parameters.

Robust functional regression model for marginal mean and subject-specific inferences.

PubMed

Cao, Chunzheng; Shi, Jian Qing; Lee, Youngjo

2017-01-01

We introduce flexible robust functional regression models, using various heavy-tailed processes, including a Student t-process. We propose efficient algorithms in estimating parameters for the marginal mean inferences and in predicting conditional means as well as interpolation and extrapolation for the subject-specific inferences. We develop bootstrap prediction intervals (PIs) for conditional mean curves. Numerical studies show that the proposed model provides a robust approach against data contamination or distribution misspecification, and the proposed PIs maintain the nominal confidence levels. A real data application is presented as an illustrative example.

Advanced modelling, monitoring, and process control of bioconversion systems

NASA Astrophysics Data System (ADS)

Schmitt, Elliott C.

Production of fuels and chemicals from lignocellulosic biomass is an increasingly important area of research and industrialization throughout the world. In order to be competitive with fossil-based fuels and chemicals, maintaining cost-effectiveness is critical. Advanced process control (APC) and optimization methods could significantly reduce operating costs in the biorefining industry. Two reasons APC has previously proven challenging to implement for bioprocesses include: lack of suitable online sensor technology of key system components, and strongly nonlinear first principal models required to predict bioconversion behavior. To overcome these challenges batch fermentations with the acetogen Moorella thermoacetica were monitored with Raman spectroscopy for the conversion of real lignocellulosic hydrolysates and a kinetic model for the conversion of synthetic sugars was developed. Raman spectroscopy was shown to be effective in monitoring the fermentation of sugarcane bagasse and sugarcane straw hydrolysate, where univariate models predicted acetate concentrations with a root mean square error of prediction (RMSEP) of 1.9 and 1.0 g L-1 for bagasse and straw, respectively. Multivariate partial least squares (PLS) models were employed to predict acetate, xylose, glucose, and total sugar concentrations for both hydrolysate fermentations. The PLS models were more robust than univariate models, and yielded a percent error of approximately 5% for both sugarcane bagasse and sugarcane straw. In addition, a screening technique was discussed for improving Raman spectra of hydrolysate samples prior to collecting fermentation data. Furthermore, a mechanistic model was developed to predict batch fermentation of synthetic glucose, xylose, and a mixture of the two sugars to acetate. The models accurately described the bioconversion process with an RMSEP of approximately 1 g L-1 for each model and provided insights into how kinetic parameters changed during dual substrate fermentation with diauxic growth. Model predictive control (MPC), an advanced process control strategy, is capable of utilizing nonlinear models and sensor feedback to provide optimal input while ensuring critical process constraints are met. Using the microorganism Saccharomyces cerevisiae, a commonly used microorganism for biofuel production, and work performed with M. thermoacetica, a nonlinear MPC was implemented on a continuous membrane cell-recycle bioreactor (MCRB) for the conversion of glucose to ethanol. The dilution rate was used to control the ethanol productivity of the system will maintaining total substrate conversion above the constraint of 98%. PLS multivariate models for glucose (RMSEP 1.5 g L-1) and ethanol (RMSEP 0.4 g L-1) were robust in predicting concentrations and a mechanistic kinetic model built accurately predicted continuous fermentation behavior. A setpoint trajectory, ranging from 2 - 4.5 g L-1 h-1 for productivity was closely tracked by the fermentation system using Raman measurements and an extended Kalman filter to estimate biomass concentrations. Overall, this work was able to demonstrate an effective approach for real-time monitoring and control of a complex fermentation system.

Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces

PubMed Central

Kamrunnahar, M.; Dias, N. S.; Schiff, S. J.

2013-01-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain–computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8–23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications. PMID:21267657

Toward a model-based predictive controller design in brain-computer interfaces.

PubMed

Kamrunnahar, M; Dias, N S; Schiff, S J

2011-05-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.

Robust spin-valley polarization in commensurate Mo S2 /graphene heterostructures

NASA Astrophysics Data System (ADS)

Du, Luojun; Zhang, Qian; Gong, Benchao; Liao, Mengzhou; Zhu, Jianqi; Yu, Hua; He, Rui; Liu, Kai; Yang, Rong; Shi, Dongxia; Gu, Lin; Yan, Feng; Zhang, Guangyu; Zhang, Qingming

2018-03-01

The investigation and control of quantum degrees of freedom (DoFs) of carriers lie at the heart of condensed-matter physics and next-generation electronics/optoelectronics. van der Waals heterostructures stacked from distinct two-dimensional (2D) crystals offer an unprecedented platform for combining the superior properties of individual 2D materials and manipulating spin, layer, and valley DoFs. Mo S2 /graphene heterostructures, harboring prominent spin-transport properties of graphene, giant spin-orbit coupling, and spin-valley polarization of Mo S2 , are predicted as a perfect venue for optospintronics. Here, we report the epitaxial growth of commensurate Mo S2 on graphene with high quality by chemical vapor deposition, and demonstrate robust temperature-independent spin-valley polarization at off-resonant excitation. We further show that the helicity of B exciton is larger than that of A exciton, allowing the manipulation of spin bits in the commensurate heterostructures by both optical helicity and wavelength. Our results open a window for controlling spin DoF by light and pave a way for taking spin qubits as information carriers in the next-generation valley-controlled optospintronics.

The effects of ecology and evolutionary history on robust capuchin morphological diversity.

PubMed

Wright, Kristin A; Wright, Barth W; Ford, Susan M; Fragaszy, Dorothy; Izar, Patricia; Norconk, Marilyn; Masterson, Thomas; Hobbs, David G; Alfaro, Michael E; Lynch Alfaro, Jessica W

2015-01-01

Recent molecular work has confirmed the long-standing morphological hypothesis that capuchins are comprised of two distinct clades, the gracile (untufted) capuchins (genus Cebus, Erxleben, 1777) and the robust (tufted) capuchins (genus Sapajus Kerr, 1792). In the past, the robust group was treated as a single, undifferentiated and cosmopolitan species, with data from all populations lumped together in morphological and ecological studies, obscuring morphological differences that might exist across this radiation. Genetic evidence suggests that the modern radiation of robust capuchins began diversifying ∼2.5 Ma, with significant subsequent geographic expansion into new habitat types. In this study we use a morphological sample of gracile and robust capuchin craniofacial and postcranial characters to examine how ecology and evolutionary history have contributed to morphological diversity within the robust capuchins. We predicted that if ecology is driving robust capuchin variation, three distinct robust morphotypes would be identified: (1) the Atlantic Forest species (Sapajus xanthosternos, S. robustus, and S. nigritus), (2) the Amazonian rainforest species (S. apella, S. cay and S. macrocephalus), and (3) the Cerrado-Caatinga species (S. libidinosus). Alternatively, if diversification time between species pairs predicts degree of morphological difference, we predicted that the recently diverged S. apella, S. macrocephalus, S. libidinosus, and S. cay would be morphologically comparable, with greater variation among the more ancient lineages of S. nigritus, S. xanthosternos, and S. robustus. Our analyses suggest that S. libidinosus has the most derived craniofacial and postcranial features, indicative of inhabiting a more terrestrial niche that includes a dependence on tool use for the extraction of imbedded foods. We also suggest that the cranial robusticity of S. macrocephalus and S. apella are indicative of recent competition with sympatric gracile capuchin species, resulting in character displacement. Copyright © 2014 Elsevier Inc. All rights reserved.

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

PubMed

Voyle, Nicola; Keohane, Aoife; Newhouse, Stephen; Lunnon, Katie; Johnston, Caroline; Soininen, Hilkka; Kloszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon; Hodges, Angela; Kiddle, Steven; Dobson, Richard Jb

2016-01-01

Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer's disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust. This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts. Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis. Gene and pathway level models performed similarly to each other and to a model based on demographic information only. Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

Proband Mental Health Difficulties and Parental Stress Predict Mental Health in Toddlers at High-Risk for Autism Spectrum Disorders.

PubMed

Crea, Katherine; Dissanayake, Cheryl; Hudry, Kristelle

2016-10-01

Family-related predictors of mental health problems were investigated among 30 toddlers at familial high-risk for autism spectrum disorders (ASD) and 28 controls followed from age 2- to 3-years. Parents completed the self-report Depression Anxiety Stress Scales and the parent-report Behavior Assessment System for Children. High-risk toddlers were assessed for ASD at 3-years. Parent stress and proband mental health difficulties predicted concurrent toddler mental health difficulties at 2-years, but only baseline proband internalising problems continued to predict toddler internalising problems at 3-years; high-risk status did not confer additional risk. Baseline toddler mental health difficulties robustly predicted later difficulties, while high-risk status and diagnostic outcome conferred no additional risk. A family systems perspective may be useful for understanding toddler mental health difficulties.

Robust mobility in human-populated environments

NASA Astrophysics Data System (ADS)

Gonzalez, Juan Pablo; Phillips, Mike; Neuman, Brad; Likhachev, Max

2012-06-01

Creating robots that can help humans in a variety of tasks requires robust mobility and the ability to safely navigate among moving obstacles. This paper presents an overview of recent research in the Robotics Collaborative Technology Alliance (RCTA) that addresses many of the core requirements for robust mobility in human-populated environments. Safe Interval Path Planning (SIPP) allows for very fast planning in dynamic environments when planning timeminimal trajectories. Generalized Safe Interval Path Planning extends this concept to trajectories that minimize arbitrary cost functions. Finally, generalized PPCP algorithm is used to generate plans that reason about the uncertainty in the predicted trajectories of moving obstacles and try to actively disambiguate the intentions of humans whenever necessary. We show how these approaches consider moving obstacles and temporal constraints and produce high-fidelity paths. Experiments in simulated environments show the performance of the algorithms under different controlled conditions, and experiments on physical mobile robots interacting with humans show how the algorithms perform under the uncertainties of the real world.

Real time optimal guidance of low-thrust spacecraft: an application of nonlinear model predictive control.

PubMed

Arrieta-Camacho, Juan José; Biegler, Lorenz T

2005-12-01

Real time optimal guidance is considered for a class of low thrust spacecraft. In particular, nonlinear model predictive control (NMPC) is utilized for computing the optimal control actions required to transfer a spacecraft from a low Earth orbit to a mission orbit. The NMPC methodology presented is able to cope with unmodeled disturbances. The dynamics of the transfer are modeled using a set of modified equinoctial elements because they do not exhibit singularities for zero inclination and zero eccentricity. The idea behind NMPC is the repeated solution of optimal control problems; at each time step, a new control action is computed. The optimal control problem is solved using a direct method-fully discretizing the equations of motion. The large scale nonlinear program resulting from the discretization procedure is solved using IPOPT--a primal-dual interior point algorithm. Stability and robustness characteristics of the NMPC algorithm are reviewed. A numerical example is presented that encourages further development of the proposed methodology: the transfer from low-Earth orbit to a molniya orbit.

Respiratory sinus arrhythmia reactivity to a sad film predicts depression symptom improvement and symptomatic trajectory.

PubMed

Panaite, Vanessa; Hindash, Alexandra Cowden; Bylsma, Lauren M; Small, Brent J; Salomon, Kristen; Rottenberg, Jonathan

2016-01-01

Respiratory sinus arrhythmia (RSA) reactivity, an index of cardiac vagal tone, has been linked to self-regulation and the severity and course of depression (Rottenberg, 2007). Although initial data supports the proposition that RSA withdrawal during a sad film is a specific predictor of depression course (Fraguas, 2007; Rottenberg, 2005), the robustness and specificity of this finding are unclear. To provide a stronger test, RSA reactivity to three emotion films (happy, sad, fear) and to a more robust stressor, a speech task, were examined in currently depressed individuals (n=37), who were assessed for their degree of symptomatic improvement over 30weeks. Robust RSA reactivity to the sad film uniquely predicted overall symptom improvement over 30weeks. RSA reactivity to both sad and stressful stimuli predicted the speed and maintenance of symptomatic improvement. The current analyses provide the most robust support to date that RSA withdrawal to sad stimuli (but not stressful) has specificity in predicting the overall symptomatic improvement. In contrast, RSA reactivity to negative stimuli (both sad and stressful) predicted the trajectory of depression course. Patients' engagement with sad stimuli may be an important sign to attend to in therapeutic settings. Copyright © 2015 Elsevier B.V. All rights reserved.

Teacher Pupil Control Ideology and Behavior as Predictors of Classroom Robustness.

ERIC Educational Resources Information Center

Estep, Linda E.; And Others

1980-01-01

It was hypothesized that confrontations between a strict teacher and misbehaving students would add drama and robustness to the classroom. In 88 secondary classrooms, robustness and teacher's control ideology and behavior were measured. The hypothesis was rejected; humanistic control behavior related to high robustness. A companion elementary…

IECON '87: Industrial applications of control and simulation; Proceedings of the 1987 International Conference on Industrial Electronics, Control, and Instrumentation, Cambridge, MA, Nov. 3, 4, 1987

NASA Technical Reports Server (NTRS)

Hartley, Tom T. (Editor)

1987-01-01

Recent advances in control-system design and simulation are discussed in reviews and reports. Among the topics considered are fast algorithms for generating near-optimal binary decision programs, trajectory control of robot manipulators with compensation of load effects via a six-axis force sensor, matrix integrators for real-time simulation, a high-level control language for an autonomous land vehicle, and a practical engineering design method for stable model-reference adaptive systems. Also addressed are the identification and control of flexible-limb robots with unknown loads, adaptive control and robust adaptive control for manipulators with feedforward compensation, adaptive pole-placement controllers with predictive action, variable-structure strategies for motion control, and digital signal-processor-based variable-structure controls.

Why do mothers favor girls and fathers, boys? : A hypothesis and a test of investment disparity.

PubMed

Godoy, Ricardo; Reyes-García, Victoria; McDade, Thomas; Tanner, Susan; Leonard, William R; Huanca, Tomás; Vadez, Vincent; Patel, Karishma

2006-06-01

Growing evidence suggests mothers invest more in girls than boys and fathers more in boys than girls. We develop a hypothesis that predicts preference for girls by the parent facing more resource constraints and preference for boys by the parent facing less constraint. We test the hypothesis with panel data from the Tsimane', a foraging-farming society in the Bolivian Amazon. Tsimane' mothers face more resource constraints than fathers. As predicted, mother's wealth protected girl's BMI, but father's wealth had weak effects on boy's BMI. Numerous tests yielded robust results, including those that controlled for fixed effects of child and household.

Firing Costs and Flexibility: Evidence from Firms’ Employment Responses to Shocks in India*

PubMed Central

Adhvaryu, Achyuta; Chari, A. V.; Sharma, Siddharth

2013-01-01

A key prediction of dynamic labor demand models is that firing restrictions attenuate firms’ employment responses to economic fluctuations. We provide the first direct test of this prediction using data from India. We exploit the fact that rainfall fluctuations, through their effects on agricultural productivity, generate variation in local demand within districts over time. Consistent with the theory, we find that industrial employment is more sensitive to shocks where labor regulation is less restrictive. Our results are robust to controlling for endogenous firm placement and vary across factory size in a pattern consistent with institutional features of Indian labor law. PMID:24357882

Programmable single-cell mammalian biocomputers.

PubMed

Ausländer, Simon; Ausländer, David; Müller, Marius; Wieland, Markus; Fussenegger, Martin

2012-07-05

Synthetic biology has advanced the design of standardized control devices that program cellular functions and metabolic activities in living organisms. Rational interconnection of these synthetic switches resulted in increasingly complex designer networks that execute input-triggered genetic instructions with precision, robustness and computational logic reminiscent of electronic circuits. Using trigger-controlled transcription factors, which independently control gene expression, and RNA-binding proteins that inhibit the translation of transcripts harbouring specific RNA target motifs, we have designed a set of synthetic transcription–translation control devices that could be rewired in a plug-and-play manner. Here we show that these combinatorial circuits integrated a two-molecule input and performed digital computations with NOT, AND, NAND and N-IMPLY expression logic in single mammalian cells. Functional interconnection of two N-IMPLY variants resulted in bitwise intracellular XOR operations, and a combinatorial arrangement of three logic gates enabled independent cells to perform programmable half-subtractor and half-adder calculations. Individual mammalian cells capable of executing basic molecular arithmetic functions isolated or coordinated to metabolic activities in a predictable, precise and robust manner may provide new treatment strategies and bio-electronic interfaces in future gene-based and cell-based therapies.

Robust Control Design for Systems With Probabilistic Uncertainty

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.

2005-01-01

This paper presents a reliability- and robustness-based formulation for robust control synthesis for systems with probabilistic uncertainty. In a reliability-based formulation, the probability of violating design requirements prescribed by inequality constraints is minimized. In a robustness-based formulation, a metric which measures the tendency of a random variable/process to cluster close to a target scalar/function is minimized. A multi-objective optimization procedure, which combines stability and performance requirements in time and frequency domains, is used to search for robustly optimal compensators. Some of the fundamental differences between the proposed strategy and conventional robust control methods are: (i) unnecessary conservatism is eliminated since there is not need for convex supports, (ii) the most likely plants are favored during synthesis allowing for probabilistic robust optimality, (iii) the tradeoff between robust stability and robust performance can be explored numerically, (iv) the uncertainty set is closely related to parameters with clear physical meaning, and (v) compensators with improved robust characteristics for a given control structure can be synthesized.

H∞ robust fault-tolerant controller design for an autonomous underwater vehicle's navigation control system

NASA Astrophysics Data System (ADS)

Cheng, Xiang-Qin; Qu, Jing-Yuan; Yan, Zhe-Ping; Bian, Xin-Qian

2010-03-01

In order to improve the security and reliability for autonomous underwater vehicle (AUV) navigation, an H∞ robust fault-tolerant controller was designed after analyzing variations in state-feedback gain. Operating conditions and the design method were then analyzed so that the control problem could be expressed as a mathematical optimization problem. This permitted the use of linear matrix inequalities (LMI) to solve for the H∞ controller for the system. When considering different actuator failures, these conditions were then also mathematically expressed, allowing the H∞ robust controller to solve for these events and thus be fault-tolerant. Finally, simulation results showed that the H∞ robust fault-tolerant controller could provide precise AUV navigation control with strong robustness.

Enhanced robust fractional order proportional-plus-integral controller based on neural network for velocity control of permanent magnet synchronous motor.

PubMed

Zhang, Bitao; Pi, YouGuo

2013-07-01

The traditional integer order proportional-integral-differential (IO-PID) controller is sensitive to the parameter variation or/and external load disturbance of permanent magnet synchronous motor (PMSM). And the fractional order proportional-integral-differential (FO-PID) control scheme based on robustness tuning method is proposed to enhance the robustness. But the robustness focuses on the open-loop gain variation of controlled plant. In this paper, an enhanced robust fractional order proportional-plus-integral (ERFOPI) controller based on neural network is proposed. The control law of the ERFOPI controller is acted on a fractional order implement function (FOIF) of tracking error but not tracking error directly, which, according to theory analysis, can enhance the robust performance of system. Tuning rules and approaches, based on phase margin, crossover frequency specification and robustness rejecting gain variation, are introduced to obtain the parameters of ERFOPI controller. And the neural network algorithm is used to adjust the parameter of FOIF. Simulation and experimental results show that the method proposed in this paper not only achieve favorable tracking performance, but also is robust with regard to external load disturbance and parameter variation. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Development of a Comprehensive Digital Avionics Curriculum for the Aeronautical Engineer

DTIC Science & Technology

2006-03-01

able to analyze and design aircraft and missile guidance and control systems, including feedback stabilization schemes and stochastic processes, using ...Uncertainty modeling for robust control; Robust closed-loop stability and performance; Robust H- infinity control; Robustness check using mu-analysis...Controlled feedback (reduces noise) 3. Statistical group response (reduce pressure toward conformity) When used as a tool to study a complex problem

Robust control of flexible space vehicles with minimum structural excitation: On-off pulse control of flexible space vehicles

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang

1992-01-01

Both feedback and feedforward control approaches for uncertain dynamical systems (in particular, with uncertainty in structural mode frequency) are investigated. The control objective is to achieve a fast settling time (high performance) and robustness (insensitivity) to plant uncertainty. Preshaping of an ideal, time optimal control input using a tapped-delay filter is shown to provide a fast settling time with robust performance. A robust, non-minimum-phase feedback controller is synthesized with particular emphasis on its proper implementation for a non-zero set-point control problem. It is shown that a properly designed, feedback controller performs well, as compared with a time optimal open loop controller with special preshaping for performance robustness. Also included are two separate papers by the same authors on this subject.

Nonlinear robust control of hypersonic aircrafts with interactions between flight dynamics and propulsion systems.

PubMed

Li, Zhaoying; Zhou, Wenjie; Liu, Hao

2016-09-01

This paper addresses the nonlinear robust tracking controller design problem for hypersonic vehicles. This problem is challenging due to strong coupling between the aerodynamics and the propulsion system, and the uncertainties involved in the vehicle dynamics including parametric uncertainties, unmodeled model uncertainties, and external disturbances. By utilizing the feedback linearization technique, a linear tracking error system is established with prescribed references. For the linear model, a robust controller is proposed based on the signal compensation theory to guarantee that the tracking error dynamics is robustly stable. Numerical simulation results are given to show the advantages of the proposed nonlinear robust control method, compared to the robust loop-shaping control approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

The X-43A Six Degree of Freedom Monte Carlo Analysis

NASA Technical Reports Server (NTRS)

Baumann, Ethan; Bahm, Catherine; Strovers, Brian; Beck, Roger

2008-01-01

This report provides an overview of the Hyper-X research vehicle Monte Carlo analysis conducted with the six-degree-of-freedom simulation. The methodology and model uncertainties used for the Monte Carlo analysis are presented as permitted. In addition, the process used to select hardware validation test cases from the Monte Carlo data is described. The preflight Monte Carlo analysis indicated that the X-43A control system was robust to the preflight uncertainties and provided the Hyper-X project an important indication that the vehicle would likely be successful in accomplishing the mission objectives. The X-43A inflight performance is compared to the preflight Monte Carlo predictions and shown to exceed the Monte Carlo bounds in several instances. Possible modeling shortfalls are presented that may account for these discrepancies. The flight control laws and guidance algorithms were robust enough as a result of the preflight Monte Carlo analysis that the unexpected in-flight performance did not have undue consequences. Modeling and Monte Carlo analysis lessons learned are presented.

The X-43A Six Degree of Freedom Monte Carlo Analysis

NASA Technical Reports Server (NTRS)

Baumann, Ethan; Bahm, Catherine; Strovers, Brian; Beck, Roger; Richard, Michael

2007-01-01

This report provides an overview of the Hyper-X research vehicle Monte Carlo analysis conducted with the six-degree-of-freedom simulation. The methodology and model uncertainties used for the Monte Carlo analysis are presented as permitted. In addition, the process used to select hardware validation test cases from the Monte Carlo data is described. The preflight Monte Carlo analysis indicated that the X-43A control system was robust to the preflight uncertainties and provided the Hyper-X project an important indication that the vehicle would likely be successful in accomplishing the mission objectives. The X-43A in-flight performance is compared to the preflight Monte Carlo predictions and shown to exceed the Monte Carlo bounds in several instances. Possible modeling shortfalls are presented that may account for these discrepancies. The flight control laws and guidance algorithms were robust enough as a result of the preflight Monte Carlo analysis that the unexpected in-flight performance did not have undue consequences. Modeling and Monte Carlo analysis lessons learned are presented.

Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology.

PubMed

Wang, Baojun; Kitney, Richard I; Joly, Nicolas; Buck, Martin

2011-10-18

Modular and orthogonal genetic logic gates are essential for building robust biologically based digital devices to customize cell signalling in synthetic biology. Here we constructed an orthogonal AND gate in Escherichia coli using a novel hetero-regulation module from Pseudomonas syringae. The device comprises two co-activating genes hrpR and hrpS controlled by separate promoter inputs, and a σ(54)-dependent hrpL promoter driving the output. The hrpL promoter is activated only when both genes are expressed, generating digital-like AND integration behaviour. The AND gate is demonstrated to be modular by applying new regulated promoters to the inputs, and connecting the output to a NOT gate module to produce a combinatorial NAND gate. The circuits were assembled using a parts-based engineering approach of quantitative characterization, modelling, followed by construction and testing. The results show that new genetic logic devices can be engineered predictably from novel native orthogonal biological control elements using quantitatively in-context characterized parts. © 2011 Macmillan Publishers Limited. All rights reserved.

Cognitive control, attention, and the other race effect in memory.

PubMed

Brown, Thackery I; Uncapher, Melina R; Chow, Tiffany E; Eberhardt, Jennifer L; Wagner, Anthony D

2017-01-01

People are better at remembering faces from their own race than other races-a phenomenon with significant societal implications. This Other Race Effect (ORE) in memory could arise from different attentional allocation to, and cognitive control over, same- and other-race faces during encoding. Deeper or more differentiated processing of same-race faces could yield more robust representations of same- vs. other-race faces that could support better recognition memory. Conversely, to the extent that other-race faces may be characterized by lower perceptual expertise, attention and cognitive control may be more important for successful encoding of robust, distinct representations of these stimuli. We tested a mechanistic model in which successful encoding of same- and other-race faces, indexed by subsequent memory performance, is differentially predicted by (a) engagement of frontoparietal networks subserving top-down attention and cognitive control, and (b) interactions between frontoparietal networks and fusiform cortex face processing. European American (EA) and African American (AA) participants underwent fMRI while intentionally encoding EA and AA faces, and ~24 hrs later performed an "old/new" recognition memory task. Univariate analyses revealed greater engagement of frontoparietal top-down attention and cognitive control networks during encoding for same- vs. other-race faces, stemming particularly from a failure to engage the cognitive control network during processing of other-race faces that were subsequently forgotten. Psychophysiological interaction (PPI) analyses further revealed that OREs were characterized by greater functional interaction between medial intraparietal sulcus, a component of the top-down attention network, and fusiform cortex during same- than other-race face encoding. Together, these results suggest that group-based face memory biases at least partially stem from differential allocation of cognitive control and top-down attention during encoding, such that same-race memory benefits from elevated top-down attentional engagement with face processing regions; conversely, reduced recruitment of cognitive control circuitry appears more predictive of memory failure when encoding out-group faces.

Cognitive control, attention, and the other race effect in memory

PubMed Central

Uncapher, Melina R.; Chow, Tiffany E.; Eberhardt, Jennifer L.; Wagner, Anthony D.

2017-01-01

People are better at remembering faces from their own race than other races–a phenomenon with significant societal implications. This Other Race Effect (ORE) in memory could arise from different attentional allocation to, and cognitive control over, same- and other-race faces during encoding. Deeper or more differentiated processing of same-race faces could yield more robust representations of same- vs. other-race faces that could support better recognition memory. Conversely, to the extent that other-race faces may be characterized by lower perceptual expertise, attention and cognitive control may be more important for successful encoding of robust, distinct representations of these stimuli. We tested a mechanistic model in which successful encoding of same- and other-race faces, indexed by subsequent memory performance, is differentially predicted by (a) engagement of frontoparietal networks subserving top-down attention and cognitive control, and (b) interactions between frontoparietal networks and fusiform cortex face processing. European American (EA) and African American (AA) participants underwent fMRI while intentionally encoding EA and AA faces, and ~24 hrs later performed an “old/new” recognition memory task. Univariate analyses revealed greater engagement of frontoparietal top-down attention and cognitive control networks during encoding for same- vs. other-race faces, stemming particularly from a failure to engage the cognitive control network during processing of other-race faces that were subsequently forgotten. Psychophysiological interaction (PPI) analyses further revealed that OREs were characterized by greater functional interaction between medial intraparietal sulcus, a component of the top-down attention network, and fusiform cortex during same- than other-race face encoding. Together, these results suggest that group-based face memory biases at least partially stem from differential allocation of cognitive control and top-down attention during encoding, such that same-race memory benefits from elevated top-down attentional engagement with face processing regions; conversely, reduced recruitment of cognitive control circuitry appears more predictive of memory failure when encoding out-group faces. PMID:28282414

Robustness Analysis and Optimally Robust Control Design via Sum-of-Squares

NASA Technical Reports Server (NTRS)

Dorobantu, Andrei; Crespo, Luis G.; Seiler, Peter J.

2012-01-01

A control analysis and design framework is proposed for systems subject to parametric uncertainty. The underlying strategies are based on sum-of-squares (SOS) polynomial analysis and nonlinear optimization to design an optimally robust controller. The approach determines a maximum uncertainty range for which the closed-loop system satisfies a set of stability and performance requirements. These requirements, de ned as inequality constraints on several metrics, are restricted to polynomial functions of the uncertainty. To quantify robustness, SOS analysis is used to prove that the closed-loop system complies with the requirements for a given uncertainty range. The maximum uncertainty range, calculated by assessing a sequence of increasingly larger ranges, serves as a robustness metric for the closed-loop system. To optimize the control design, nonlinear optimization is used to enlarge the maximum uncertainty range by tuning the controller gains. Hence, the resulting controller is optimally robust to parametric uncertainty. This approach balances the robustness margins corresponding to each requirement in order to maximize the aggregate system robustness. The proposed framework is applied to a simple linear short-period aircraft model with uncertain aerodynamic coefficients.

Enhancers and super-enhancers have an equivalent regulatory role in embryonic stem cells through regulation of single or multiple genes

PubMed Central

Moorthy, Sakthi D.; Davidson, Scott; Shchuka, Virlana M.; Singh, Gurdeep; Malek-Gilani, Nakisa; Langroudi, Lida; Martchenko, Alexandre; So, Vincent; Macpherson, Neil N.; Mitchell, Jennifer A.

2017-01-01

Transcriptional enhancers are critical for maintaining cell-type–specific gene expression and driving cell fate changes during development. Highly transcribed genes are often associated with a cluster of individual enhancers such as those found in locus control regions. Recently, these have been termed stretch enhancers or super-enhancers, which have been predicted to regulate critical cell identity genes. We employed a CRISPR/Cas9-mediated deletion approach to study the function of several enhancer clusters (ECs) and isolated enhancers in mouse embryonic stem (ES) cells. Our results reveal that the effect of deleting ECs, also classified as ES cell super-enhancers, is highly variable, resulting in target gene expression reductions ranging from 12% to as much as 92%. Partial deletions of these ECs which removed only one enhancer or a subcluster of enhancers revealed partially redundant control of the regulated gene by multiple enhancers within the larger cluster. Many highly transcribed genes in ES cells are not associated with a super-enhancer; furthermore, super-enhancer predictions ignore 81% of the potentially active regulatory elements predicted by cobinding of five or more pluripotency-associated transcription factors. Deletion of these additional enhancer regions revealed their robust regulatory role in gene transcription. In addition, select super-enhancers and enhancers were identified that regulated clusters of paralogous genes. We conclude that, whereas robust transcriptional output can be achieved by an isolated enhancer, clusters of enhancers acting on a common target gene act in a partially redundant manner to fine tune transcriptional output of their target genes. PMID:27895109

Robust H(∞) positional control of 2-DOF robotic arm driven by electro-hydraulic servo system.

PubMed

Guo, Qing; Yu, Tian; Jiang, Dan

2015-11-01

In this paper an H∞ positional feedback controller is developed to improve the robust performance under structural and parametric uncertainty disturbance in electro-hydraulic servo system (EHSS). The robust control model is described as the linear state-space equation by upper linear fractional transformation. According to the solution of H∞ sub-optimal control problem, the robust controller is designed and simplified to lower order linear model which is easily realized in EHSS. The simulation and experimental results can validate the robustness of this proposed method. The comparison result with PI control shows that the robust controller is suitable for this EHSS under the critical condition where the desired system bandwidth is higher and the external load of the hydraulic actuator is closed to its limited capability. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Panaceas, uncertainty, and the robust control framework in sustainability science

PubMed Central

Anderies, John M.; Rodriguez, Armando A.; Janssen, Marco A.; Cifdaloz, Oguzhan

2007-01-01

A critical challenge faced by sustainability science is to develop strategies to cope with highly uncertain social and ecological dynamics. This article explores the use of the robust control framework toward this end. After briefly outlining the robust control framework, we apply it to the traditional Gordon–Schaefer fishery model to explore fundamental performance–robustness and robustness–vulnerability trade-offs in natural resource management. We find that the classic optimal control policy can be very sensitive to parametric uncertainty. By exploring a large class of alternative strategies, we show that there are no panaceas: even mild robustness properties are difficult to achieve, and increasing robustness to some parameters (e.g., biological parameters) results in decreased robustness with respect to others (e.g., economic parameters). On the basis of this example, we extract some broader themes for better management of resources under uncertainty and for sustainability science in general. Specifically, we focus attention on the importance of a continual learning process and the use of robust control to inform this process. PMID:17881574

Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory

PubMed Central

Tao, Qing

2017-01-01

Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM. PMID:29391864

Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory.

PubMed

Yang, Haimin; Pan, Zhisong; Tao, Qing

2017-01-01

Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.

A robust fractional-order PID controller design based on active queue management for TCP network

NASA Astrophysics Data System (ADS)

Hamidian, Hamideh; Beheshti, Mohammad T. H.

2018-01-01

In this paper, a robust fractional-order controller is designed to control the congestion in transmission control protocol (TCP) networks with time-varying parameters. Fractional controllers can increase the stability and robustness. Regardless of advantages of fractional controllers, they are still not common in congestion control in TCP networks. The network parameters are time-varying, so the robust stability is important in congestion controller design. Therefore, we focused on the robust controller design. The fractional PID controller is developed based on active queue management (AQM). D-partition technique is used. The most important property of designed controller is the robustness to the time-varying parameters of the TCP network. The vertex quasi-polynomials of the closed-loop characteristic equation are obtained, and the stability boundaries are calculated for each vertex quasi-polynomial. The intersection of all stability regions is insensitive to network parameter variations, and results in robust stability of TCP/AQM system. NS-2 simulations show that the proposed algorithm provides a stable queue length. Moreover, simulations show smaller oscillations of the queue length and less packet drop probability for FPID compared to PI and PID controllers. We can conclude from NS-2 simulations that the average packet loss probability variations are negligible when the network parameters change.

Robustness of larval development of intertidal sea urchin species to simulated ocean warming and acidification.

PubMed

García, Eliseba; Hernández, José Carlos; Clemente, Sabrina

2018-08-01

Ocean warming and acidification are the two most significant side effects of carbone dioxide emissions in the world's oceans. By changing water, temperature and pH are the main environmental factors controlling the distribution, physiology, morphology and behaviour of marine invertebrates. This study evaluated the combined effects of predicted high temperature levels, and predicted low pH values, on fertilization and early development stages of the sea urchins Arbacia lixula, Paracentrotus lividus, Sphaerechinus granularis and Diadema africanum. Twelve treatments, combining different temperatures (19, 21, 23 and 25 °C) and pH values (8.1, 7.7 and 7.4 units), were tested in laboratory experiments. All of the tested temperatures and pH values were within the open coast seawater range expected within the next century. We examined fertilization rate, cleavage rate, 3-day larvae survival, and development of the different sea urchin species at set time intervals after insemination. Our results highlight the susceptibility of subtidal species to environmental changes, and the robustness of intertidal species to ocean warming and acidification. Copyright © 2018 Elsevier Ltd. All rights reserved.

Closed-Loop Control of Vortex Formation in Separated Flows with Application to Micro Air Vehicles

DTIC Science & Technology

2010-10-25

ruich. roll, «nd phasgi. innmt.yi of th« wwtg m tnpnap» lo die nnHriiiy fin—n nant flow r 1 faA fhiif—irm ■IIIIUIJ wah gutting fnxjBfju flow wa...a» ■ Altar kernel to predict the retpone of Ihe »nig lo mote complex actuator omul »pails A «oic. of caonvTkn of incrcaaang eornpkxirv were <toa>gard... lo rupracis bit Muctuatioru m puffing condition. The mow robust controller» were «Me to suppre« lift fluctuation» associated with • broadband

Robust adaptive vibration control of a flexible structure.

PubMed

Khoshnood, A M; Moradi, H M

2014-07-01

Different types of L1 adaptive control systems show that using robust theories with adaptive control approaches has produced high performance controllers. In this study, a model reference adaptive control scheme considering robust theories is used to propose a practical control system for vibration suppression of a flexible launch vehicle (FLV). In this method, control input of the system is shaped from the dynamic model of the vehicle and components of the control input are adaptively constructed by estimating the undesirable vibration frequencies. Robust stability of the adaptive vibration control system is guaranteed by using the L1 small gain theorem. Simulation results of the robust adaptive vibration control strategy confirm that the effects of vibration on the vehicle performance considerably decrease without the loss of the phase margin of the system. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Scene-Based Contextual Cueing in Pigeons

PubMed Central

Wasserman, Edward A.; Teng, Yuejia; Brooks, Daniel I.

2014-01-01

Repeated pairings of a particular visual context with a specific location of a target stimulus facilitate target search in humans. We explored an animal model of such contextual cueing. Pigeons had to peck a target which could appear in one of four locations on color photographs of real-world scenes. On half of the trials, each of four scenes was consistently paired with one of four possible target locations; on the other half of the trials, each of four different scenes was randomly paired with the same four possible target locations. In Experiments 1 and 2, pigeons exhibited robust contextual cueing when the context preceded the target by 1 s to 8 s, with reaction times to the target being shorter on predictive-scene trials than on random-scene trials. Pigeons also responded more frequently during the delay on predictive-scene trials than on random-scene trials; indeed, during the delay on predictive-scene trials, pigeons predominately pecked toward the location of the upcoming target, suggesting that attentional guidance contributes to contextual cueing. In Experiment 3, involving left-right and top-bottom scene reversals, pigeons exhibited stronger control by global than by local scene cues. These results attest to the robustness and associative basis of contextual cueing in pigeons. PMID:25546098

Identification and robust control of an experimental servo motor.

PubMed

Adam, E J; Guestrin, E D

2002-04-01

In this work, the design of a robust controller for an experimental laboratory-scale position control system based on a dc motor drive as well as the corresponding identification and robust stability analysis are presented. In order to carry out the robust design procedure, first, a classic closed-loop identification technique is applied and then, the parametrization by internal model control is used. The model uncertainty is evaluated under both parametric and global representation. For the latter case, an interesting discussion about the conservativeness of this description is presented by means of a comparison between the uncertainty disk and the critical perturbation radius approaches. Finally, conclusions about the performance of the experimental system with the robust controller are discussed using comparative graphics of the controlled variable and the Nyquist stability margin as a robustness measurement.

The effectiveness of robust RMCD control chart as outliers’ detector

NASA Astrophysics Data System (ADS)

Darmanto; Astutik, Suci

2017-12-01

A well-known control chart to monitor a multivariate process is Hotelling’s T 2 which its parameters are estimated classically, very sensitive and also marred by masking and swamping of outliers data effect. To overcome these situation, robust estimators are strongly recommended. One of robust estimators is re-weighted minimum covariance determinant (RMCD) which has robust characteristics as same as MCD. In this paper, the effectiveness term is accuracy of the RMCD control chart in detecting outliers as real outliers. In other word, how effectively this control chart can identify and remove masking and swamping effects of outliers. We assessed the effectiveness the robust control chart based on simulation by considering different scenarios: n sample sizes, proportion of outliers, number of p quality characteristics. We found that in some scenarios, this RMCD robust control chart works effectively.

Robust model predictive control for optimal continuous drug administration.

PubMed

Sopasakis, Pantelis; Patrinos, Panagiotis; Sarimveis, Haralambos

2014-10-01

In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise. A case study on a PBPK model with 7 compartments, constraints on 5 tissues and a variable drug concentration set-point illustrates the efficiency of the methodology in drug dosing control applications. The proposed methodology is also tested in an uncertain setting and proves successful in presence of modelling errors and inaccurate measurements. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Design and Experimental Evaluation of a Robust Position Controller for an Electrohydrostatic Actuator Using Adaptive Antiwindup Sliding Mode Scheme

PubMed Central

Lee, Ji Min; Park, Sung Hwan; Kim, Jong Shik

2013-01-01

A robust control scheme is proposed for the position control of the electrohydrostatic actuator (EHA) when considering hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. To reduce overshoot due to a saturation of electric motor and to realize robustness against load disturbance and lumped system uncertainties such as varying parameters and modeling error, this paper proposes an adaptive antiwindup PID sliding mode scheme as a robust position controller for the EHA system. An optimal PID controller and an optimal anti-windup PID controller are also designed to compare control performance. An EHA prototype is developed, carrying out system modeling and parameter identification in designing the position controller. The simply identified linear model serves as the basis for the design of the position controllers, while the robustness of the control systems is compared by experiments. The adaptive anti-windup PID sliding mode controller has been found to have the desired performance and become robust against hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. PMID:23983640

Structure, function, and control of the human musculoskeletal network

PubMed Central

Murphy, Andrew C.; Muldoon, Sarah F.; Baker, David; Lastowka, Adam; Bennett, Brittany; Yang, Muzhi

2018-01-01

The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle’s role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments. PMID:29346370

Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre- and Post-Processing in Sequential Data Assimilation

NASA Astrophysics Data System (ADS)

Wang, S.; Ancell, B. C.; Huang, G. H.; Baetz, B. W.

2018-03-01

Data assimilation using the ensemble Kalman filter (EnKF) has been increasingly recognized as a promising tool for probabilistic hydrologic predictions. However, little effort has been made to conduct the pre- and post-processing of assimilation experiments, posing a significant challenge in achieving the best performance of hydrologic predictions. This paper presents a unified data assimilation framework for improving the robustness of hydrologic ensemble predictions. Statistical pre-processing of assimilation experiments is conducted through the factorial design and analysis to identify the best EnKF settings with maximized performance. After the data assimilation operation, statistical post-processing analysis is also performed through the factorial polynomial chaos expansion to efficiently address uncertainties in hydrologic predictions, as well as to explicitly reveal potential interactions among model parameters and their contributions to the predictive accuracy. In addition, the Gaussian anamorphosis is used to establish a seamless bridge between data assimilation and uncertainty quantification of hydrologic predictions. Both synthetic and real data assimilation experiments are carried out to demonstrate feasibility and applicability of the proposed methodology in the Guadalupe River basin, Texas. Results suggest that statistical pre- and post-processing of data assimilation experiments provide meaningful insights into the dynamic behavior of hydrologic systems and enhance robustness of hydrologic ensemble predictions.

Robust PD Sway Control of a Lifted Load for a Crane Using a Genetic Algorithm

NASA Astrophysics Data System (ADS)

Kawada, Kazuo; Sogo, Hiroyuki; Yamamoto, Toru; Mada, Yasuhiro

PID control schemes still continue to be widely used for most industrial control systems. This is mainly because PID controllers have simple control structures, and are simple to maintain and tune. However, it is difficult to find a set of suitable control parameters in the case of time-varying and/or nonlinear systems. For such a problem, the robust controller has been proposed.Although it is important to choose the suitable nominal model in designing the robust controller, it is not usually easy.In this paper, a new robust PD controller design scheme is proposed, which utilizes a genetic algorithm.

Guidance and control 1992; Proceedings of the 15th Annual AAS Rocky Mountain Conference, Keystone, CO, Feb. 8-12, 1992

NASA Astrophysics Data System (ADS)

Culp, Robert D.; Zietz, Richard P.

The present volume on guidance and control discusses advances in guidance, navigation, and control, guidance and control storyboard displays, space robotic control, spacecraft control and flexible body interaction, and the Mission to Planet Earth. Attention is given to applications of Newton's method to attitude determination, a new family of low-cost momentum/reaction wheels, stellar attitude data handling, and satellite life prediction using propellant quantity measurements. Topics addressed include robust manipulator controller specification and design, implementations and applications of a manipulator control testbed, optimizing transparency in teleoperator architectures, and MIMO system identification using frequency response data. Also discussed are instrument configurations for the restructured Earth Observing System, the HIRIS instrument, clouds and the earth's radiant energy system, and large space-based systems for dealing with global change.

Robustness of near-infrared calibration models for the prediction of milk constituents during the milking process.

PubMed

Melfsen, Andreas; Hartung, Eberhard; Haeussermann, Angelika

2013-02-01

The robustness of in-line raw milk analysis with near-infrared spectroscopy (NIRS) was tested with respect to the prediction of the raw milk contents fat, protein and lactose. Near-infrared (NIR) spectra of raw milk (n = 3119) were acquired on three different farms during the milking process of 354 milkings over a period of six months. Calibration models were calculated for: a random data set of each farm (fully random internal calibration); first two thirds of the visits per farm (internal calibration); whole datasets of two of the three farms (external calibration), and combinations of external and internal datasets. Validation was done either on the remaining data set per farm (internal validation) or on data of the remaining farms (external validation). Excellent calibration results were obtained when fully randomised internal calibration sets were used for milk analysis. In this case, RPD values of around ten, five and three for the prediction of fat, protein and lactose content, respectively, were achieved. Farm internal calibrations achieved much poorer prediction results especially for the prediction of protein and lactose with RPD values of around two and one respectively. The prediction accuracy improved when validation was done on spectra of an external farm, mainly due to the higher sample variation in external calibration sets in terms of feeding diets and individual cow effects. The results showed that further improvements were achieved when additional farm information was added to the calibration set. One of the main requirements towards a robust calibration model is the ability to predict milk constituents in unknown future milk samples. The robustness and quality of prediction increases with increasing variation of, e.g., feeding and cow individual milk composition in the calibration model.

Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.

PubMed

Kneissler, Jan; Stalph, Patrick O; Drugowitsch, Jan; Butz, Martin V

2014-01-01

It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.

Developpement d'une plateforme de simulation et d'un pilote automatique - Application aux Cessna Citation X et Hawker 800XP

NASA Astrophysics Data System (ADS)

Ghazi, Georges

This report presents several methodologies for the design of tools intended to the analysis of the stability and the control of a business aircraft. At first, a generic flight dynamic model was developed to predict the behavior of the aircraft further to a movement on the control surfaces or further to any disturbance. For that purpose, different categories of winds were considered in the module of simulation to generate various scenarios and conclude about the efficiency of the autopilot. Besides being realistic, the flight model takes into account the variation of the mass parameters according to fuel consumption. A comparison with a simulator of the company CAE Inc. and certified level D allowed to validate this first stage with an acceptable success rate. Once the dynamics is validated, the next stage deals with the stability around a flight condition. For that purpose, a first static analysis is established to find the trim conditions inside the flight envelop. Then, two algorithms of linearization generate the state space models which approximate the decoupled dynamics (longitudinal and lateral) of the aircraft. Then to test the viability of the linear models, 1,500 comparisons with the nonlinear dynamics have been done with a 100% rate of success. The study of stability allowed to highlight the need of control systems to improve first the performances of the plane, then to control its different axes. A methodology based on a coupling between a modern control technique (LQR) and a genetic algorithm is presented. This methodology allowed to find optimal and successful controllers which satisfy a large number of specifications. Besides being successful, they have to be robust to uncertainties owed to the variation of mass. Thus, an analysis of robustness using the theory of the guardian maps was applied to uncertain dynamics. However, because of a too sensitive region of the flight envelop, some analyses are biased. Nevertheless, a validation with the nonlinear dynamics allowed to prove the robustness of the controllers over the entire flight envelope. Finally, the last stage of this project concerned the control laws for the autopilot. Once again, the proposed methodology, bases itself on the association of flight mechanic equations, control theory and a metaheuristic optimization method. Afterward, four detailed test scenarios are presented to illustrate the efficiency and the robustness of the entire autopilot.

H∞ Robust Control of a Large-Piston MEMS Micromirror for Compact Fourier Transform Spectrometer Systems.

PubMed

Chen, Huipeng; Li, Mengyuan; Zhang, Yi; Xie, Huikai; Chen, Chang; Peng, Zhangming; Su, Shaohui

2018-02-08

Incorporating linear-scanning micro-electro-mechanical systems (MEMS) micromirrors into Fourier transform spectral acquisition systems can greatly reduce the size of the spectrometer equipment, making portable Fourier transform spectrometers (FTS) possible. How to minimize the tilting of the MEMS mirror plate during its large linear scan is a major problem in this application. In this work, an FTS system has been constructed based on a biaxial MEMS micromirror with a large-piston displacement of 180 μm, and a biaxial H∞ robust controller is designed. Compared with open-loop control and proportional-integral-derivative (PID) closed-loop control, H∞ robust control has good stability and robustness. The experimental results show that the stable scanning displacement reaches 110.9 μm under the H∞ robust control, and the tilting angle of the MEMS mirror plate in that full scanning range falls within ±0.0014°. Without control, the FTS system cannot generate meaningful spectra. In contrast, the FTS yields a clean spectrum with a full width at half maximum (FWHM) spectral linewidth of 96 cm -1 under the H∞ robust control. Moreover, the FTS system can maintain good stability and robustness under various driving conditions.

H∞ Robust Control of a Large-Piston MEMS Micromirror for Compact Fourier Transform Spectrometer Systems

PubMed Central

Li, Mengyuan; Zhang, Yi; Chen, Chang; Peng, Zhangming; Su, Shaohui

2018-01-01

Incorporating linear-scanning micro-electro-mechanical systems (MEMS) micromirrors into Fourier transform spectral acquisition systems can greatly reduce the size of the spectrometer equipment, making portable Fourier transform spectrometers (FTS) possible. How to minimize the tilting of the MEMS mirror plate during its large linear scan is a major problem in this application. In this work, an FTS system has been constructed based on a biaxial MEMS micromirror with a large-piston displacement of 180 μm, and a biaxial H∞ robust controller is designed. Compared with open-loop control and proportional-integral-derivative (PID) closed-loop control, H∞ robust control has good stability and robustness. The experimental results show that the stable scanning displacement reaches 110.9 μm under the H∞ robust control, and the tilting angle of the MEMS mirror plate in that full scanning range falls within ±0.0014°. Without control, the FTS system cannot generate meaningful spectra. In contrast, the FTS yields a clean spectrum with a full width at half maximum (FWHM) spectral linewidth of 96 cm−1 under the H∞ robust control. Moreover, the FTS system can maintain good stability and robustness under various driving conditions. PMID:29419765

Robust Kalman filter design for predictive wind shear detection

NASA Technical Reports Server (NTRS)

Stratton, Alexander D.; Stengel, Robert F.

1991-01-01

Severe, low-altitude wind shear is a threat to aviation safety. Airborne sensors under development measure the radial component of wind along a line directly in front of an aircraft. In this paper, optimal estimation theory is used to define a detection algorithm to warn of hazardous wind shear from these sensors. To achieve robustness, a wind shear detection algorithm must distinguish threatening wind shear from less hazardous gustiness, despite variations in wind shear structure. This paper presents statistical analysis methods to refine wind shear detection algorithm robustness. Computational methods predict the ability to warn of severe wind shear and avoid false warning. Comparative capability of the detection algorithm as a function of its design parameters is determined, identifying designs that provide robust detection of severe wind shear.

Multi-time Scale Joint Scheduling Method Considering the Grid of Renewable Energy

NASA Astrophysics Data System (ADS)

Zhijun, E.; Wang, Weichen; Cao, Jin; Wang, Xin; Kong, Xiangyu; Quan, Shuping

2018-01-01

Renewable new energy power generation prediction error like wind and light, brings difficulties to dispatch the power system. In this paper, a multi-time scale robust scheduling method is set to solve this problem. It reduces the impact of clean energy prediction bias to the power grid by using multi-time scale (day-ahead, intraday, real time) and coordinating the dispatching power output of various power supplies such as hydropower, thermal power, wind power, gas power and. The method adopts the robust scheduling method to ensure the robustness of the scheduling scheme. By calculating the cost of the abandon wind and the load, it transforms the robustness into the risk cost and optimizes the optimal uncertainty set for the smallest integrative costs. The validity of the method is verified by simulation.

Fast and robust method for the determination of microstructure and composition in butadiene, styrene-butadiene, and isoprene rubber by near-infrared spectroscopy.

PubMed

Vilmin, Franck; Dussap, Claude; Coste, Nathalie

2006-06-01

In the tire industry, synthetic styrene-butadiene rubber (SBR), butadiene rubber (BR), and isoprene rubber (IR) elastomers are essential for conferring to the product its properties of grip and rolling resistance. Their physical properties depend on their chemical composition, i. e., their microstructure and styrene content, which must be accurately controlled. This paper describes a fast, robust, and highly reproducible near-infrared analytical method for the quantitative determination of the microstructure and styrene content. The quantitative models are calculated with the help of pure spectral profiles estimated from a partial least squares (PLS) regression, using (13)C nuclear magnetic resonance (NMR) as the reference method. This versatile approach allows the models to be applied over a large range of compositions, from a single BR to an SBR-IR blend. The resulting quantitative predictions are independent of the sample path length. As a consequence, the sample preparation is solvent free and simplified with a very fast (five minutes) hot filming step of a bulk polymer piece. No precise thickness control is required. Thus, the operator effect becomes negligible and the method is easily transferable. The root mean square error of prediction, depending on the rubber composition, is between 0.7% and 1.3%. The reproducibility standard error is less than 0.2% in every case.

Arabidopsis Ensemble Reverse-Engineered Gene Regulatory Network Discloses Interconnected Transcription Factors in Oxidative Stress[W

PubMed Central

Vermeirssen, Vanessa; De Clercq, Inge; Van Parys, Thomas; Van Breusegem, Frank; Van de Peer, Yves

2014-01-01

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation. PMID:25549671

Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress.

PubMed

Vermeirssen, Vanessa; De Clercq, Inge; Van Parys, Thomas; Van Breusegem, Frank; Van de Peer, Yves

2014-12-01

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation. © 2014 American Society of Plant Biologists. All rights reserved.

Prediction and perception of hazards in professional drivers: Does hazard perception skill differ between safe and less-safe fire-appliance drivers?

PubMed

Crundall, David; Kroll, Victoria

2018-05-18

Can hazard perception testing be useful for the emergency services? Previous research has found emergency response drivers' (ERDs) to perform better than controls, however these studies used clips of normal driving. In contrast, the current study filmed footage from a fire-appliance on blue-light training runs through Nottinghamshire, and endeavoured to discriminate between different groups of EDRs based on experience and collision risk. Thirty clips were selected to create two variants of the hazard perception test: a traditional push-button test requiring speeded-responses to hazards, and a prediction test that occludes at hazard onset and provides four possible outcomes for participants to choose between. Three groups of fire-appliance drivers (novices, low-risk experienced and high-risk experienced), and age-matched controls undertook both tests. The hazard perception test only discriminated between controls and all FA drivers, whereas the hazard prediction test was more sensitive, discriminating between high and low-risk experienced fire appliance drivers. Eye movement analyses suggest that the low-risk drivers were better at prioritising the hazardous precursors, leading to better predictive accuracy. These results pave the way for future assessment and training tools to supplement emergency response driver training, while supporting the growing literature that identifies hazard prediction as a more robust measure of driver safety than traditional hazard perception tests. Copyright © 2018 Elsevier Ltd. All rights reserved.

Design and implementation of robust controllers for a gait trainer.

PubMed

Wang, F C; Yu, C H; Chou, T Y

2009-08-01

This paper applies robust algorithms to control an active gait trainer for children with walking disabilities. Compared with traditional rehabilitation procedures, in which two or three trainers are required to assist the patient, a motor-driven mechanism was constructed to improve the efficiency of the procedures. First, a six-bar mechanism was designed and constructed to mimic the trajectory of children's ankles in walking. Second, system identification techniques were applied to obtain system transfer functions at different operating points by experiments. Third, robust control algorithms were used to design Hinfinity robust controllers for the system. Finally, the designed controllers were implemented to verify experimentally the system performance. From the results, the proposed robust control strategies are shown to be effective.

Free will beliefs predict attitudes toward unethical behavior and criminal punishment

PubMed Central

Martin, Nathan D.; Rigoni, Davide; Vohs, Kathleen D.

2017-01-01

Do free will beliefs influence moral judgments? Answers to this question from theoretical and empirical perspectives are controversial. This study attempted to replicate past research and offer theoretical insights by analyzing World Values Survey data from residents of 46 countries (n = 65,111 persons). Corroborating experimental findings, free will beliefs predicted intolerance of unethical behaviors and support for severe criminal punishment. Further, the link between free will beliefs and intolerance of unethical behavior was moderated by variations in countries’ institutional integrity, defined as the degree to which countries had accountable, corruption-free public sectors. Free will beliefs predicted intolerance of unethical behaviors for residents of countries with high and moderate institutional integrity, but this correlation was not seen for countries with low institutional integrity. Free will beliefs predicted support for criminal punishment regardless of countries’ institutional integrity. Results were robust across different operationalizations of institutional integrity and with or without statistical control variables. PMID:28652361

Free will beliefs predict attitudes toward unethical behavior and criminal punishment.

PubMed

Martin, Nathan D; Rigoni, Davide; Vohs, Kathleen D

2017-07-11

Do free will beliefs influence moral judgments? Answers to this question from theoretical and empirical perspectives are controversial. This study attempted to replicate past research and offer theoretical insights by analyzing World Values Survey data from residents of 46 countries ( n = 65,111 persons). Corroborating experimental findings, free will beliefs predicted intolerance of unethical behaviors and support for severe criminal punishment. Further, the link between free will beliefs and intolerance of unethical behavior was moderated by variations in countries' institutional integrity, defined as the degree to which countries had accountable, corruption-free public sectors. Free will beliefs predicted intolerance of unethical behaviors for residents of countries with high and moderate institutional integrity, but this correlation was not seen for countries with low institutional integrity. Free will beliefs predicted support for criminal punishment regardless of countries' institutional integrity. Results were robust across different operationalizations of institutional integrity and with or without statistical control variables.

Robust control of combustion instabilities

NASA Astrophysics Data System (ADS)

Hong, Boe-Shong

Several interactive dynamical subsystems, each of which has its own time-scale and physical significance, are decomposed to build a feedback-controlled combustion- fluid robust dynamics. On the fast-time scale, the phenomenon of combustion instability is corresponding to the internal feedback of two subsystems: acoustic dynamics and flame dynamics, which are parametrically dependent on the slow-time-scale mean-flow dynamics controlled for global performance by a mean-flow controller. This dissertation constructs such a control system, through modeling, analysis and synthesis, to deal with model uncertainties, environmental noises and time- varying mean-flow operation. Conservation law is decomposed as fast-time acoustic dynamics and slow-time mean-flow dynamics, served for synthesizing LPV (linear parameter varying)- L2-gain robust control law, in which a robust observer is embedded for estimating and controlling the internal status, while achieving trade- offs among robustness, performances and operation. The robust controller is formulated as two LPV-type Linear Matrix Inequalities (LMIs), whose numerical solver is developed by finite-element method. Some important issues related to physical understanding and engineering application are discussed in simulated results of the control system.

Relationship of Personality Disorders to the Course of Major Depressive Disorder in a Nationally Representative Sample

PubMed Central

Skodol, Andrew E.; Grilo, Carlos M.; Keyes, Katherine; Geier, Timothy; Grant, Bridget F.; Hasin, Deborah S.

2011-01-01

Objective The purpose of this study was to examine the effects of specific personality disorder co-morbidity on the course of major depressive disorder in a nationally-representative sample. Method Data were drawn from 1,996 participants in a national survey. Participants who met criteria for major depressive disorder at baseline in face-to-face interviews (2001–2002) were re-interviewed three years later (2004–2005) to determine persistence and recurrence. Predictors included all DSM-IV personality disorders. Control variables included demographic characteristics, other Axis I disorders, family and treatment histories, and previously established predictors of the course of major depressive disorder. Results 15.1% of participants had persistent major depressive disorder and 7.3% of those who remitted had a recurrence. Univariate analyses indicated that avoidant, borderline, histrionic, paranoid, schizoid, and schizotypal personality disorders all elevated the risk for persistence. With Axis I co-morbidity controlled, all but histrionic personality disorder remained significant. With all other personality disorders controlled, borderline and schizotypal remained significant predictors. In final, multivariate analyses that controlled for age at onset of major depressive disorder, number of previous episodes, duration of current episode, family history, and treatment, borderline personality disorder remained a robust predictor of major depressive disorder persistence. Neither personality disorders nor other clinical variables predicted recurrence. Conclusions In this nationally-representative sample of adults with major depressive disorder, borderline personality disorder robustly predicted persistence, a finding that converges with recent clinical studies. Personality psychopathology, particularly borderline personality disorder, should be assessed in all patients with major depressive disorder, considered in prognosis, and addressed in treatment. PMID:21245088

Adaptive Critic Nonlinear Robust Control: A Survey.

PubMed

Wang, Ding; He, Haibo; Liu, Derong

2017-10-01

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear H ∞ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.

Generalized internal model robust control for active front steering intervention

NASA Astrophysics Data System (ADS)

Wu, Jian; Zhao, Youqun; Ji, Xuewu; Liu, Yahui; Zhang, Lipeng

2015-03-01

Because of the tire nonlinearity and vehicle's parameters' uncertainties, robust control methods based on the worst cases, such as H ∞, µ synthesis, have been widely used in active front steering control, however, in order to guarantee the stability of active front steering system (AFS) controller, the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control. In this paper, a generalized internal model robust control (GIMC) that can overcome the contradiction between performance and stability is used in the AFS control. In GIMC, the Youla parameterization is used in an improved way. And GIMC controller includes two sections: a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters' uncertainties and some external disturbances. Simulations of double lane change (DLC) maneuver and that of braking on split- µ road are conducted to compare the performance and stability of the GIMC control, the nominal performance PID controller and the H ∞ controller. Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle's parameters variations, H ∞ controller is conservative so that the performance is a little low, and only the GIMC controller overcomes the contradiction between performance and robustness, which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller. Therefore, the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system, that is, can solve the instability of PID or LQP control methods and the low performance of the standard H ∞ controller.

DeSigN: connecting gene expression with therapeutics for drug repurposing and development.

PubMed

Lee, Bernard Kok Bang; Tiong, Kai Hung; Chang, Jit Kang; Liew, Chee Sun; Abdul Rahman, Zainal Ariff; Tan, Aik Choon; Khang, Tsung Fei; Cheong, Sok Ching

2017-01-25

The drug discovery and development pipeline is a long and arduous process that inevitably hampers rapid drug development. Therefore, strategies to improve the efficiency of drug development are urgently needed to enable effective drugs to enter the clinic. Precision medicine has demonstrated that genetic features of cancer cells can be used for predicting drug response, and emerging evidence suggest that gene-drug connections could be predicted more accurately by exploring the cumulative effects of many genes simultaneously. We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC 50 ) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC 50 of 0.8-1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control. DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.

Robust prediction of consensus secondary structures using averaged base pairing probability matrices.

PubMed

Kiryu, Hisanori; Kin, Taishin; Asai, Kiyoshi

2007-02-15

Recent transcriptomic studies have revealed the existence of a considerable number of non-protein-coding RNA transcripts in higher eukaryotic cells. To investigate the functional roles of these transcripts, it is of great interest to find conserved secondary structures from multiple alignments on a genomic scale. Since multiple alignments are often created using alignment programs that neglect the special conservation patterns of RNA secondary structures for computational efficiency, alignment failures can cause potential risks of overlooking conserved stem structures. We investigated the dependence of the accuracy of secondary structure prediction on the quality of alignments. We compared three algorithms that maximize the expected accuracy of secondary structures as well as other frequently used algorithms. We found that one of our algorithms, called McCaskill-MEA, was more robust against alignment failures than others. The McCaskill-MEA method first computes the base pairing probability matrices for all the sequences in the alignment and then obtains the base pairing probability matrix of the alignment by averaging over these matrices. The consensus secondary structure is predicted from this matrix such that the expected accuracy of the prediction is maximized. We show that the McCaskill-MEA method performs better than other methods, particularly when the alignment quality is low and when the alignment consists of many sequences. Our model has a parameter that controls the sensitivity and specificity of predictions. We discussed the uses of that parameter for multi-step screening procedures to search for conserved secondary structures and for assigning confidence values to the predicted base pairs. The C++ source code that implements the McCaskill-MEA algorithm and the test dataset used in this paper are available at http://www.ncrna.org/papers/McCaskillMEA/. Supplementary data are available at Bioinformatics online.

Occupant-vehicle dynamics and the role of the internal model

NASA Astrophysics Data System (ADS)

Cole, David J.

2018-05-01

With the increasing need to reduce time and cost of vehicle development there is increasing advantage in simulating mathematically the dynamic interaction of a vehicle and its occupant. The larger design space arising from the introduction of automated vehicles further increases the potential advantage. The aim of the paper is to outline the role of the internal model hypothesis in understanding and modelling occupant-vehicle dynamics, specifically the dynamics associated with direction and speed control of the vehicle. The internal model is the driver's or passenger's understanding of the vehicle dynamics and is thought to be employed in the perception, cognition and action processes of the brain. The internal model aids the estimation of the states of the vehicle from noisy sensory measurements. It can also be used to optimise cognitive control action by predicting the consequence of the action; thus model predictive control (MPC) theory provides a foundation for modelling the cognition process. The stretch reflex of the neuromuscular system also makes use of the prediction of the internal model. Extensions to the MPC approach are described which account for: interaction with an automated vehicle; robust control; intermittent control; and cognitive workload. Further work to extend understanding of occupant-vehicle dynamic interaction is outlined. This paper is based on a keynote presentation given by the author to the 13th International Symposium on Advanced Vehicle Control (AVEC) conference held in Munich, September 2016.

Effect of intermittent feedback control on robustness of human-like postural control system

NASA Astrophysics Data System (ADS)

Tanabe, Hiroko; Fujii, Keisuke; Suzuki, Yasuyuki; Kouzaki, Motoki

2016-03-01

Humans have to acquire postural robustness to maintain stability against internal and external perturbations. Human standing has been recently modelled using an intermittent feedback control. However, the causality inside of the closed-loop postural control system associated with the neural control strategy is still unknown. Here, we examined the effect of intermittent feedback control on postural robustness and of changes in active/passive components on joint coordinative structure. We implemented computer simulation of a quadruple inverted pendulum that is mechanically close to human tiptoe standing. We simulated three pairs of joint viscoelasticity and three choices of neural control strategies for each joint: intermittent, continuous, or passive control. We examined postural robustness for each parameter set by analysing the region of active feedback gain. We found intermittent control at the hip joint was necessary for model stabilisation and model parameters affected the robustness of the pendulum. Joint sways of the pendulum model were partially smaller than or similar to those of experimental data. In conclusion, intermittent feedback control was necessary for the stabilisation of the quadruple inverted pendulum. Also, postural robustness of human-like multi-link standing would be achieved by both passive joint viscoelasticity and neural joint control strategies.

Effect of intermittent feedback control on robustness of human-like postural control system.

PubMed

Tanabe, Hiroko; Fujii, Keisuke; Suzuki, Yasuyuki; Kouzaki, Motoki

2016-03-02

Humans have to acquire postural robustness to maintain stability against internal and external perturbations. Human standing has been recently modelled using an intermittent feedback control. However, the causality inside of the closed-loop postural control system associated with the neural control strategy is still unknown. Here, we examined the effect of intermittent feedback control on postural robustness and of changes in active/passive components on joint coordinative structure. We implemented computer simulation of a quadruple inverted pendulum that is mechanically close to human tiptoe standing. We simulated three pairs of joint viscoelasticity and three choices of neural control strategies for each joint: intermittent, continuous, or passive control. We examined postural robustness for each parameter set by analysing the region of active feedback gain. We found intermittent control at the hip joint was necessary for model stabilisation and model parameters affected the robustness of the pendulum. Joint sways of the pendulum model were partially smaller than or similar to those of experimental data. In conclusion, intermittent feedback control was necessary for the stabilisation of the quadruple inverted pendulum. Also, postural robustness of human-like multi-link standing would be achieved by both passive joint viscoelasticity and neural joint control strategies.

Effect of intermittent feedback control on robustness of human-like postural control system

PubMed Central

Tanabe, Hiroko; Fujii, Keisuke; Suzuki, Yasuyuki; Kouzaki, Motoki

2016-01-01

Humans have to acquire postural robustness to maintain stability against internal and external perturbations. Human standing has been recently modelled using an intermittent feedback control. However, the causality inside of the closed-loop postural control system associated with the neural control strategy is still unknown. Here, we examined the effect of intermittent feedback control on postural robustness and of changes in active/passive components on joint coordinative structure. We implemented computer simulation of a quadruple inverted pendulum that is mechanically close to human tiptoe standing. We simulated three pairs of joint viscoelasticity and three choices of neural control strategies for each joint: intermittent, continuous, or passive control. We examined postural robustness for each parameter set by analysing the region of active feedback gain. We found intermittent control at the hip joint was necessary for model stabilisation and model parameters affected the robustness of the pendulum. Joint sways of the pendulum model were partially smaller than or similar to those of experimental data. In conclusion, intermittent feedback control was necessary for the stabilisation of the quadruple inverted pendulum. Also, postural robustness of human-like multi-link standing would be achieved by both passive joint viscoelasticity and neural joint control strategies. PMID:26931281

Speckle lithography for fabricating Gaussian, quasi-random 2D structures and black silicon structures

PubMed Central

Bingi, Jayachandra; Murukeshan, Vadakke Matham

2015-01-01

Laser speckle pattern is a granular structure formed due to random coherent wavelet interference and generally considered as noise in optical systems including photolithography. Contrary to this, in this paper, we use the speckle pattern to generate predictable and controlled Gaussian random structures and quasi-random structures photo-lithographically. The random structures made using this proposed speckle lithography technique are quantified based on speckle statistics, radial distribution function (RDF) and fast Fourier transform (FFT). The control over the speckle size, density and speckle clustering facilitates the successful fabrication of black silicon with different surface structures. The controllability and tunability of randomness makes this technique a robust method for fabricating predictable 2D Gaussian random structures and black silicon structures. These structures can enhance the light trapping significantly in solar cells and hence enable improved energy harvesting. Further, this technique can enable efficient fabrication of disordered photonic structures and random media based devices. PMID:26679513

Optimization and real-time control for laser treatment of heterogeneous soft tissues.

PubMed

Feng, Yusheng; Fuentes, David; Hawkins, Andrea; Bass, Jon M; Rylander, Marissa Nichole

2009-01-01

Predicting the outcome of thermotherapies in cancer treatment requires an accurate characterization of the bioheat transfer processes in soft tissues. Due to the biological and structural complexity of tumor (soft tissue) composition and vasculature, it is often very difficult to obtain reliable tissue properties that is one of the key factors for the accurate treatment outcome prediction. Efficient algorithms employing in vivo thermal measurements to determine heterogeneous thermal tissues properties in conjunction with a detailed sensitivity analysis can produce essential information for model development and optimal control. The goals of this paper are to present a general formulation of the bioheat transfer equation for heterogeneous soft tissues, review models and algorithms developed for cell damage, heat shock proteins, and soft tissues with nanoparticle inclusion, and demonstrate an overall computational strategy for developing a laser treatment framework with the ability to perform real-time robust calibrations and optimal control. This computational strategy can be applied to other thermotherapies using the heat source such as radio frequency or high intensity focused ultrasound.

Real-time control systems: feedback, scheduling and robustness

NASA Astrophysics Data System (ADS)

Simon, Daniel; Seuret, Alexandre; Sename, Olivier

2017-08-01

The efficient control of real-time distributed systems, where continuous components are governed through digital devices and communication networks, needs a careful examination of the constraints arising from the different involved domains inside co-design approaches. Thanks to the robustness of feedback control, both new control methodologies and slackened real-time scheduling schemes are proposed beyond the frontiers between these traditionally separated fields. A methodology to design robust aperiodic controllers is provided, where the sampling interval is considered as a control variable of the system. Promising experimental results are provided to show the feasibility and robustness of the approach.

Predictive and Neural Predictive Control of Uncertain Systems

NASA Technical Reports Server (NTRS)

Kelkar, Atul G.

2000-01-01

Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.

Robustness of a distributed neural network controller for locomotion in a hexapod robot

NASA Technical Reports Server (NTRS)

Chiel, Hillel J.; Beer, Randall D.; Quinn, Roger D.; Espenschied, Kenneth S.

1992-01-01

A distributed neural-network controller for locomotion, based on insect neurobiology, has been used to control a hexapod robot. How robust is this controller? Disabling any single sensor, effector, or central component did not prevent the robot from walking. Furthermore, statically stable gaits could be established using either sensor input or central connections. Thus, a complex interplay between central neural elements and sensor inputs is responsible for the robustness of the controller and its ability to generate a continuous range of gaits. These results suggest that biologically inspired neural-network controllers may be a robust method for robotic control.

Robust nonlinear control of vectored thrust aircraft

NASA Technical Reports Server (NTRS)

Doyle, John C.; Murray, Richard; Morris, John

1993-01-01

An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations.

FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced State-Awareness of Resilient Hybrid Energy System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ondrej Linda; Dumidu Wijayasekara; Milos Manic

Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a Fuzzy-Neural Data Fusion Engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFEmore » is composed of a three-layered system consisting of: 1) traditional threshold based alarms, 2) anomalous behavior detector using self-organizing fuzzy logic system, and 3) artificial neural network based system modeling and prediction. The improved control system state-awareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratory’s (INL) hybrid energy system facility know as HYTEST. Experimental results demonstrate that the proposed FN-DFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold based alarm systems.« less

Optimal robust control strategy of a solid oxide fuel cell system

NASA Astrophysics Data System (ADS)

Wu, Xiaojuan; Gao, Danhui

2018-01-01

Optimal control can ensure system safe operation with a high efficiency. However, only a few papers discuss optimal control strategies for solid oxide fuel cell (SOFC) systems. Moreover, the existed methods ignore the impact of parameter uncertainty on system instantaneous performance. In real SOFC systems, several parameters may vary with the variation of operation conditions and can not be identified exactly, such as load current. Therefore, a robust optimal control strategy is proposed, which involves three parts: a SOFC model with parameter uncertainty, a robust optimizer and robust controllers. During the model building process, boundaries of the uncertain parameter are extracted based on Monte Carlo algorithm. To achieve the maximum efficiency, a two-space particle swarm optimization approach is employed to obtain optimal operating points, which are used as the set points of the controllers. To ensure the SOFC safe operation, two feed-forward controllers and a higher-order robust sliding mode controller are presented to control fuel utilization ratio, air excess ratio and stack temperature afterwards. The results show the proposed optimal robust control method can maintain the SOFC system safe operation with a maximum efficiency under load and uncertainty variations.

Robust on-off pulse control of flexible space vehicles

NASA Technical Reports Server (NTRS)

Wie, Bong; Sinha, Ravi

1993-01-01

The on-off reaction jet control system is often used for attitude and orbital maneuvering of various spacecraft. Future space vehicles such as the orbital transfer vehicles, orbital maneuvering vehicles, and space station will extensively use reaction jets for orbital maneuvering and attitude stabilization. The proposed robust fuel- and time-optimal control algorithm is used for a three-mass spacing model of flexible spacecraft. A fuel-efficient on-off control logic is developed for robust rest-to-rest maneuver of a flexible vehicle with minimum excitation of structural modes. The first part of this report is concerned with the problem of selecting a proper pair of jets for practical trade-offs among the maneuvering time, fuel consumption, structural mode excitation, and performance robustness. A time-optimal control problem subject to parameter robustness constraints is formulated and solved. The second part of this report deals with obtaining parameter insensitive fuel- and time- optimal control inputs by solving a constrained optimization problem subject to robustness constraints. It is shown that sensitivity to modeling errors can be significantly reduced by the proposed, robustified open-loop control approach. The final part of this report deals with sliding mode control design for uncertain flexible structures. The benchmark problem of a flexible structure is used as an example for the feedback sliding mode controller design with bounded control inputs and robustness to parameter variations is investigated.

Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba

NASA Astrophysics Data System (ADS)

Melchiorre, C.; Castellanos Abella, E. A.; van Westen, C. J.; Matteucci, M.

2011-04-01

This paper describes a procedure for landslide susceptibility assessment based on artificial neural networks, and focuses on the estimation of the prediction capability, robustness, and sensitivity of susceptibility models. The study is carried out in the Guantanamo Province of Cuba, where 186 landslides were mapped using photo-interpretation. Twelve conditioning factors were mapped including geomorphology, geology, soils, landuse, slope angle, slope direction, internal relief, drainage density, distance from roads and faults, rainfall intensity, and ground peak acceleration. A methodology was used that subdivided the database in 3 subsets. A training set was used for updating the weights. A validation set was used to stop the training procedure when the network started losing generalization capability, and a test set was used to calculate the performance of the network. A 10-fold cross-validation was performed in order to show that the results are repeatable. The prediction capability, the robustness analysis, and the sensitivity analysis were tested on 10 mutually exclusive datasets. The results show that by means of artificial neural networks it is possible to obtain models with high prediction capability and high robustness, and that an exploration of the effect of the individual variables is possible, even if they are considered as a black-box model.

Robust diagnosis of non-Hodgkin lymphoma phenotypes validated on gene expression data from different laboratories.

PubMed

Bhanot, Gyan; Alexe, Gabriela; Levine, Arnold J; Stolovitzky, Gustavo

2005-01-01

A major challenge in cancer diagnosis from microarray data is the need for robust, accurate, classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose such a classification scheme originally developed for phenotype identification from mass spectrometry data. The method uses a robust multivariate gene selection procedure and combines the results of several machine learning tools trained on raw and pattern data to produce an accurate meta-classifier. We illustrate and validate our method by applying it to gene expression datasets: the oligonucleotide HuGeneFL microarray dataset of Shipp et al. (www.genome.wi.mit.du/MPR/lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our pattern-based meta-classification technique achieves higher predictive accuracies than each of the individual classifiers , is robust against data perturbations and provides subsets of related predictive genes. Our techniques predict that combinations of some genes in the p53 pathway are highly predictive of phenotype. In particular, we find that in 80% of DLBCL cases the mRNA level of at least one of the three genes p53, PLK1 and CDK2 is elevated, while in 80% of FL cases, the mRNA level of at most one of them is elevated.

BCI Competition IV – Data Set I: Learning Discriminative Patterns for Self-Paced EEG-Based Motor Imagery Detection

PubMed Central

Zhang, Haihong; Guan, Cuntai; Ang, Kai Keng; Wang, Chuanchu

2012-01-01

Detecting motor imagery activities versus non-control in brain signals is the basis of self-paced brain-computer interfaces (BCIs), but also poses a considerable challenge to signal processing due to the complex and non-stationary characteristics of motor imagery as well as non-control. This paper presents a self-paced BCI based on a robust learning mechanism that extracts and selects spatio-spectral features for differentiating multiple EEG classes. It also employs a non-linear regression and post-processing technique for predicting the time-series of class labels from the spatio-spectral features. The method was validated in the BCI Competition IV on Dataset I where it produced the lowest prediction error of class labels continuously. This report also presents and discusses analysis of the method using the competition data set. PMID:22347153

Proteomic Biomarkers for Acute Interstitial Lung Disease in Gefitinib-Treated Japanese Lung Cancer Patients

PubMed Central

Kawakami, Takao; Nagasaka, Keiko; Takami, Sachiko; Wada, Kazuya; Tu, Hsiao-Kun; Otsuji, Makiko; Kyono, Yutaka; Dobashi, Tae; Komatsu, Yasuhiko; Kihara, Makoto; Akimoto, Shingo; Peers, Ian S.; South, Marie C.; Higenbottam, Tim; Fukuoka, Masahiro; Nakata, Koichiro; Ohe, Yuichiro; Kudoh, Shoji; Clausen, Ib Groth; Nishimura, Toshihide; Marko-Varga, György; Kato, Harubumi

2011-01-01

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10−25), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control. PMID:21799770

Real-time networked control of an industrial robot manipulator via discrete-time second-order sliding modes

NASA Astrophysics Data System (ADS)

Massimiliano Capisani, Luca; Facchinetti, Tullio; Ferrara, Antonella

2010-08-01

This article presents the networked control of a robotic anthropomorphic manipulator based on a second-order sliding mode technique, where the control objective is to track a desired trajectory for the manipulator. The adopted control scheme allows an easy and effective distribution of the control algorithm over two networked machines. While the predictability of real-time tasks execution is achieved by the Soft Hard Real-Time Kernel (S.Ha.R.K.) real-time operating system, the communication is established via a standard Ethernet network. The performances of the control system are evaluated under different experimental system configurations using, to perform the experiments, a COMAU SMART3-S2 industrial robot, and the results are analysed to put into evidence the robustness of the proposed approach against possible network delays, packet losses and unmodelled effects.

Developing Uncertainty Models for Robust Flutter Analysis Using Ground Vibration Test Data

NASA Technical Reports Server (NTRS)

Potter, Starr; Lind, Rick; Kehoe, Michael W. (Technical Monitor)

2001-01-01

A ground vibration test can be used to obtain information about structural dynamics that is important for flutter analysis. Traditionally, this information#such as natural frequencies of modes#is used to update analytical models used to predict flutter speeds. The ground vibration test can also be used to obtain uncertainty models, such as natural frequencies and their associated variations, that can update analytical models for the purpose of predicting robust flutter speeds. Analyzing test data using the -norm, rather than the traditional 2-norm, is shown to lead to a minimum-size uncertainty description and, consequently, a least-conservative robust flutter speed. This approach is demonstrated using ground vibration test data for the Aerostructures Test Wing. Different norms are used to formulate uncertainty models and their associated robust flutter speeds to evaluate which norm is least conservative.

Model robustness as a confirmatory virtue: The case of climate science.

PubMed

Lloyd, Elisabeth A

2015-02-01

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independently-supported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists' discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Robust mitotic entry is ensured by a latching switch.

PubMed

Tuck, Chloe; Zhang, Tongli; Potapova, Tamara; Malumbres, Marcos; Novák, Béla

2013-01-01

Cell cycle events are driven by Cyclin dependent kinases (CDKs) and by their counter-acting phosphatases. Activation of the Cdk1:Cyclin B complex during mitotic entry is controlled by the Wee1/Myt1 inhibitory kinases and by Cdc25 activatory phosphatase, which are themselves regulated by Cdk1:Cyclin B within two positive circuits. Impairing these two feedbacks with chemical inhibitors induces a transient entry into M phase referred to as mitotic collapse. The pathology of mitotic collapse reveals that the positive circuits play a significant role in maintaining the M phase state. To better understand the function of these feedback loops during G2/M transition, we propose a simple model for mitotic entry in mammalian cells including spatial control over Greatwall kinase phosphorylation. After parameter calibration, the model is able to recapture the complex and non-intuitive molecular dynamics reported by Potapova et al. (Potapova et al., 2011). Moreover, it predicts the temporal patterns of other mitotic regulators which have not yet been experimentally tested and suggests a general design principle of cell cycle control: latching switches buffer the cellular stresses which accompany cell cycle processes to ensure that the transitions are smooth and robust.

Increased Reliability of Gas Turbine Components by Robust Coatings Manufacturing

NASA Astrophysics Data System (ADS)

Sharma, A.; Dudykevych, T.; Sansom, D.; Subramanian, R.

2017-08-01

The expanding operational windows of the advanced gas turbine components demand increasing performance capability from protective coating systems. This demand has led to the development of novel multi-functional, multi-materials coating system architectures over the last years. In addition, the increasing dependency of components exposed to extreme environment on protective coatings results in more severe penalties, in case of a coating system failure. This emphasizes that reliability and consistency of protective coating systems are equally important to their superior performance. By means of examples, this paper describes the effects of scatter in the material properties resulting from manufacturing variations on coating life predictions. A strong foundation in process-property-performance correlations as well as regular monitoring and control of the coating process is essential for robust and well-controlled coating process. Proprietary and/or commercially available diagnostic tools can help in achieving these goals, but their usage in industrial setting is still limited. Various key contributors to process variability are briefly discussed along with the limitations of existing process and product control methods. Other aspects that are important for product reliability and consistency in serial manufacturing as well as advanced testing methodologies to simplify and enhance product inspection and improve objectivity are briefly described.

Robust Control for Microgravity Vibration Isolation using Fixed Order, Mixed H2/Mu Design

NASA Technical Reports Server (NTRS)

Whorton, Mark

2003-01-01

Many space-science experiments need an active isolation system to provide a sufficiently quiescent microgravity environment. Modern control methods provide the potential for both high-performance and robust stability in the presence of parametric uncertainties that are characteristic of microgravity vibration isolation systems. While H2 and H(infinity) methods are well established, neither provides the levels of attenuation performance and robust stability in a compensator with low order. Mixed H2/H(infinity), controllers provide a means for maximizing robust stability for a given level of mean-square nominal performance while directly optimizing for controller order constraints. This paper demonstrates the benefit of mixed norm design from the perspective of robustness to parametric uncertainties and controller order for microgravity vibration isolation. A nominal performance metric analogous to the mu measure, for robust stability assessment is also introduced in order to define an acceptable trade space from which different control methodologies can be compared.

A Robust H ∞ Controller for an UAV Flight Control System.

PubMed

López, J; Dormido, R; Dormido, S; Gómez, J P

2015-01-01

The objective of this paper is the implementation and validation of a robust H ∞ controller for an UAV to track all types of manoeuvres in the presence of noisy environment. A robust inner-outer loop strategy is implemented. To design the H ∞ robust controller in the inner loop, H ∞ control methodology is used. The two controllers that conform the outer loop are designed using the H ∞ Loop Shaping technique. The reference vector used in the control architecture formed by vertical velocity, true airspeed, and heading angle, suggests a nontraditional way to pilot the aircraft. The simulation results show that the proposed control scheme works well despite the presence of noise and uncertainties, so the control system satisfies the requirements.

Closed-loop and robust control of quantum systems.

PubMed

Chen, Chunlin; Wang, Lin-Cheng; Wang, Yuanlong

2013-01-01

For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control as H(∞) control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention.

Dynamic Response and Maneuvering Strategies of a Hybrid Autonomous Underwater Vehicle in Hovering

DTIC Science & Technology

2009-02-01

Highlights of ECC’99, pages 391– 449. Springer, 1999. [7] F. Allgower, R. Findeisen , and Z. K. Nagy. Nonlinear model predictive con- trol: From theory...vehicle. In OCEANS, pages 2129–2134. MTS/IEEE, 2005. [17] M. Diehl, R. Findeisen , F. Allgower, H. G. Bock, and J. P. Schloder. Nominal stability of real...International Journal of Robust and Nonlinear Control, 18(8):816–830, May 2008. [22] R. Findeisen and F. Allgower. An introduction to nonlinear model

Experimental noise-resistant Bell-inequality violations for polarization-entangled photons

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bovino, Fabio A.; Castagnoli, Giuseppe; Cabello, Adan

2006-06-15

We experimentally demonstrate that violations of Bell's inequalities for two-photon polarization-entangled states with colored noise are extremely robust, whereas this is not the case for states with white noise. Controlling the amount of noise by using the timing compensation scheme introduced by Kim et al. [Phys. Rev. A 67, 010301(R) (2003)], we have observed violations even for states with very high noise, in excellent agrement with the predictions of Cabello et al. [Phys. Rev. A 72, 052112 (2005)].

Asynchronous machine rotor speed estimation using a tabulated numerical approach

NASA Astrophysics Data System (ADS)

Nguyen, Huu Phuc; De Miras, Jérôme; Charara, Ali; Eltabach, Mario; Bonnet, Stéphane

2017-12-01

This paper proposes a new method to estimate the rotor speed of the asynchronous machine by looking at the estimation problem as a nonlinear optimal control problem. The behavior of the nonlinear plant model is approximated off-line as a prediction map using a numerical one-step time discretization obtained from simulations. At each time-step, the speed of the induction machine is selected satisfying the dynamic fitting problem between the plant output and the predicted output, leading the system to adopt its dynamical behavior. Thanks to the limitation of the prediction horizon to a single time-step, the execution time of the algorithm can be completely bounded. It can thus easily be implemented and embedded into a real-time system to observe the speed of the real induction motor. Simulation results show the performance and robustness of the proposed estimator.

Robust Control for The G-Limit Microgravity Vibration Isolation System

NASA Technical Reports Server (NTRS)

Whorton, Mark S.

2004-01-01

Many microgravity science experiments need an active isolation system to provide a sufficiently quiescent acceleration environment. The g-LIMIT vibration isolation system will provide isolation for Microgravity Science Glovebox experiments in the International Space Station. While standard control system technologies have been demonstrated for these applications, modern control methods have the potential for meeting performance requirements while providing robust stability in the presence of parametric uncertainties that are characteristic of microgravity vibration isolation systems. While H2 and H infinity methods are well established, neither provides the levels of attenuation performance and robust stability in a compensator with low order. Mixed H2/mu controllers provide a means for maximizing robust stability for a given level of mean-square nominal performance while directly optimizing for controller order constraints. This paper demonstrates the benefit of mixed norm design from the perspective of robustness to parametric uncertainties and controller order for microgravity vibration isolation. A nominal performance metric analogous to the mu measure for robust stability assessment is also introduced in order to define an acceptable trade space from which different control methodologies can be compared.

Modern CACSD using the Robust-Control Toolbox

NASA Technical Reports Server (NTRS)

Chiang, Richard Y.; Safonov, Michael G.

1989-01-01

The Robust-Control Toolbox is a collection of 40 M-files which extend the capability of PC/PRO-MATLAB to do modern multivariable robust control system design. Included are robust analysis tools like singular values and structured singular values, robust synthesis tools like continuous/discrete H(exp 2)/H infinity synthesis and Linear Quadratic Gaussian Loop Transfer Recovery methods and a variety of robust model reduction tools such as Hankel approximation, balanced truncation and balanced stochastic truncation, etc. The capabilities of the toolbox are described and illustated with examples to show how easily they can be used in practice. Examples include structured singular value analysis, H infinity loop-shaping and large space structure model reduction.

Robust control for uncertain structures

NASA Technical Reports Server (NTRS)

Douglas, Joel; Athans, Michael

1991-01-01

Viewgraphs on robust control for uncertain structures are presented. Topics covered include: robust linear quadratic regulator (RLQR) formulas; mismatched LQR design; RLQR design; interpretations of RLQR design; disturbance rejection; and performance comparisons: RLQR vs. mismatched LQR.

Evaluation of Ares-I Control System Robustness to Uncertain Aerodynamics and Flex Dynamics

NASA Technical Reports Server (NTRS)

Jang, Jiann-Woei; VanTassel, Chris; Bedrossian, Nazareth; Hall, Charles; Spanos, Pol

2008-01-01

This paper discusses the application of robust control theory to evaluate robustness of the Ares-I control systems. Three techniques for estimating upper and lower bounds of uncertain parameters which yield stable closed-loop response are used here: (1) Monte Carlo analysis, (2) mu analysis, and (3) characteristic frequency response analysis. All three methods are used to evaluate stability envelopes of the Ares-I control systems with uncertain aerodynamics and flex dynamics. The results show that characteristic frequency response analysis is the most effective of these methods for assessing robustness.

Robust Kriged Kalman Filtering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baingana, Brian; Dall'Anese, Emiliano; Mateos, Gonzalo

2015-11-11

Although the kriged Kalman filter (KKF) has well-documented merits for prediction of spatial-temporal processes, its performance degrades in the presence of outliers due to anomalous events, or measurement equipment failures. This paper proposes a robust KKF model that explicitly accounts for presence of measurement outliers. Exploiting outlier sparsity, a novel l1-regularized estimator that jointly predicts the spatial-temporal process at unmonitored locations, while identifying measurement outliers is put forth. Numerical tests are conducted on a synthetic Internet protocol (IP) network, and real transformer load data. Test results corroborate the effectiveness of the novel estimator in joint spatial prediction and outlier identification.

Robust Thermal Control of Propulsion Lines for Space Missions

NASA Technical Reports Server (NTRS)

Bhandari, Pradeep

2011-01-01

A document discusses an approach to insulating propulsion lines for spacecraft. In spacecraft that have propulsion lines that are located externally with open bus architecture, the lines are typically insulated by Multi Layer Insulation (MLI) blankets. MLI on propulsion lines tends to have large and somewhat random variances in its heat loss properties (effective emittance) from one location to the next, which makes it an un-robust approach to control propulsion line temperatures. The approach described here consists of a clamshell design in which the inner surface of the shell is coated with low-emissivity aluminized Kapton tape, and the outer surface is covered with black tape. This clamshell completely encloses the propulsion line. The line itself is covered with its heater, which in turn, is covered completely with black tape. This approach would be low in heater power needs because even though the outer surface of the prop line (and its heater) is covered with black tape as well as the outer surface of the clamshell, the inner surface of the clamshell is covered with low-emissivity aluminized Kapton tape. Hence, the heat loss from the line will be small and comparable to the MLI based one. In terms of contamination changing the radiative properties of surfaces, since the clamshell s inner surface is always protected during handling and is only installed after all the work on the prop line has been completed, the controlling surface, which is the clamshell s inner surface, is always in pristine condition. This proposed design allows for a much more deterministic and predictable design using a very simple and implementable approach for thermal control. It also uses low heater power and is robust to handling and contamination during and after implementation.

Robust control of accelerators

NASA Astrophysics Data System (ADS)

Joel, W.; Johnson, D.; Chaouki, Abdallah T.

1991-07-01

The problem of controlling the variations in the rf power system can be effectively cast as an application of modern control theory. Two components of this theory are obtaining a model and a feedback structure. The model inaccuracies influence the choice of a particular controller structure. Because of the modelling uncertainty, one has to design either a variable, adaptive controller or a fixed, robust controller to achieve the desired objective. The adaptive control scheme usually results in very complex hardware; and, therefore, shall not be pursued in this research. In contrast, the robust control method leads to simpler hardware. However, robust control requires a more accurate mathematical model of the physical process than is required by adaptive control. Our research at the Los Alamos National Laboratory (LANL) and the University of New Mexico (UNM) has led to the development and implementation of a new robust rf power feedback system. In this article, we report on our research progress. In section 1, the robust control problem for the rf power system and the philosophy adopted for the beginning phase of our research is presented. In section 2, the results of our proof-of-principle experiments are presented. In section 3, we describe the actual controller configuration that is used in LANL FEL physics experiments. The novelty of our approach is that the control hardware is implemented directly in rf. without demodulating, compensating, and then remodulating.

Combining micro-RNA and protein sequencing to detect robust biomarkers for Graves' disease and orbitopathy.

PubMed

Zhang, Lei; Masetti, Giulia; Colucci, Giuseppe; Salvi, Mario; Covelli, Danila; Eckstein, Anja; Kaiser, Ulrike; Draman, Mohd Shazli; Muller, Ilaria; Ludgate, Marian; Lucini, Luigi; Biscarini, Filippo

2018-05-30

Graves' Disease (GD) is an autoimmune condition in which thyroid-stimulating antibodies (TRAB) mimic thyroid-stimulating hormone function causing hyperthyroidism. 5% of GD patients develop inflammatory Graves' orbitopathy (GO) characterized by proptosis and attendant sight problems. A major challenge is to identify which GD patients are most likely to develop GO and has relied on TRAB measurement. We screened sera/plasma from 14 GD, 19 GO and 13 healthy controls using high-throughput proteomics and miRNA sequencing (Illumina's HiSeq2000 and Agilent-6550 Funnel quadrupole-time-of-flight mass spectrometry) to identify potential biomarkers for diagnosis or prognosis evaluation. Euclidean distances and differential expression (DE) based on miRNA and protein quantification were analysed by multidimensional scaling (MDS) and multinomial regression respectively. We detected 3025 miRNAs and 1886 proteins and MDS revealed good separation of the 3 groups. Biomarkers were identified by combined DE and Lasso-penalized predictive models; accuracy of predictions was 0.86 (±0:18), and 5 miRNA and 20 proteins were found including Zonulin, Alpha-2 macroglobulin, Beta-2 glycoprotein 1 and Fibronectin. Functional analysis identified relevant metabolic pathways, including hippo signaling, bacterial invasion of epithelial cells and mRNA surveillance. Proteomic and miRNA analyses, combined with robust bioinformatics, identified circulating biomarkers applicable to diagnose GD, predict GO disease status and optimize patient management.

Optimization-Based Robust Nonlinear Control

DTIC Science & Technology

2006-08-01

ABSTRACT New control algorithms were developed for robust stabilization of nonlinear dynamical systems . Novel, linear matrix inequality-based synthesis...was to further advance optimization-based robust nonlinear control design, for general nonlinear systems (especially in discrete time ), for linear...Teel, IEEE Transactions on Control Systems Technology, vol. 14, no. 3, p. 398-407, May 2006. 3. "A unified framework for input-to-state stability in

A Robust H ∞ Controller for an UAV Flight Control System

PubMed Central

López, J.

2015-01-01

The objective of this paper is the implementation and validation of a robust H ∞ controller for an UAV to track all types of manoeuvres in the presence of noisy environment. A robust inner-outer loop strategy is implemented. To design the H ∞ robust controller in the inner loop, H ∞ control methodology is used. The two controllers that conform the outer loop are designed using the H ∞ Loop Shaping technique. The reference vector used in the control architecture formed by vertical velocity, true airspeed, and heading angle, suggests a nontraditional way to pilot the aircraft. The simulation results show that the proposed control scheme works well despite the presence of noise and uncertainties, so the control system satisfies the requirements. PMID:26221622

Robust fractional order sliding mode control of doubly-fed induction generator (DFIG)-based wind turbines.

PubMed

Ebrahimkhani, Sadegh

2016-07-01

Wind power plants have nonlinear dynamics and contain many uncertainties such as unknown nonlinear disturbances and parameter uncertainties. Thus, it is a difficult task to design a robust reliable controller for this system. This paper proposes a novel robust fractional-order sliding mode (FOSM) controller for maximum power point tracking (MPPT) control of doubly fed induction generator (DFIG)-based wind energy conversion system. In order to enhance the robustness of the control system, uncertainties and disturbances are estimated using a fractional order uncertainty estimator. In the proposed method a continuous control strategy is developed to achieve the chattering free fractional order sliding-mode control, and also no knowledge of the uncertainties and disturbances or their bound is assumed. The boundedness and convergence properties of the closed-loop signals are proven using Lyapunov׳s stability theory. Simulation results in the presence of various uncertainties were carried out to evaluate the effectiveness and robustness of the proposed control scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Robust small area prediction for counts.

PubMed

Tzavidis, Nikos; Ranalli, M Giovanna; Salvati, Nicola; Dreassi, Emanuela; Chambers, Ray

2015-06-01

A new semiparametric approach to model-based small area prediction for counts is proposed and used for estimating the average number of visits to physicians for Health Districts in Central Italy. The proposed small area predictor can be viewed as an outlier robust alternative to the more commonly used empirical plug-in predictor that is based on a Poisson generalized linear mixed model with Gaussian random effects. Results from the real data application and from a simulation experiment confirm that the proposed small area predictor has good robustness properties and in some cases can be more efficient than alternative small area approaches. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

A Worst-Case Approach for On-Line Flutter Prediction

NASA Technical Reports Server (NTRS)

Lind, Rick C.; Brenner, Martin J.

1998-01-01

Worst-case flutter margins may be computed for a linear model with respect to a set of uncertainty operators using the structured singular value. This paper considers an on-line implementation to compute these robust margins in a flight test program. Uncertainty descriptions are updated at test points to account for unmodeled time-varying dynamics of the airplane by ensuring the robust model is not invalidated by measured flight data. Robust margins computed with respect to this uncertainty remain conservative to the changing dynamics throughout the flight. A simulation clearly demonstrates this method can improve the efficiency of flight testing by accurately predicting the flutter margin to improve safety while reducing the necessary flight time.

Structural acoustic control of plates with variable boundary conditions: design methodology.

PubMed

Sprofera, Joseph D; Cabell, Randolph H; Gibbs, Gary P; Clark, Robert L

2007-07-01

A method for optimizing a structural acoustic control system subject to variations in plate boundary conditions is provided. The assumed modes method is used to build a plate model with varying levels of rotational boundary stiffness to simulate the dynamics of a plate with uncertain edge conditions. A transducer placement scoring process, involving Hankel singular values, is combined with a genetic optimization routine to find spatial locations robust to boundary condition variation. Predicted frequency response characteristics are examined, and theoretically optimized results are discussed in relation to the range of boundary conditions investigated. Modeled results indicate that it is possible to minimize the impact of uncertain boundary conditions in active structural acoustic control by optimizing the placement of transducers with respect to those uncertainties.

Predictable and reliable ECG monitoring over IEEE 802.11 WLANs within a hospital.

PubMed

Park, Juyoung; Kang, Kyungtae

2014-09-01

Telecardiology provides mobility for patients who require constant electrocardiogram (ECG) monitoring. However, its safety is dependent on the predictability and robustness of data delivery, which must overcome errors in the wireless channel through which the ECG data are transmitted. We report here a framework that can be used to gauge the applicability of IEEE 802.11 wireless local area network (WLAN) technology to ECG monitoring systems in terms of delay constraints and transmission reliability. For this purpose, a medical-grade WLAN architecture achieved predictable delay through the combination of a medium access control mechanism based on the point coordination function provided by IEEE 802.11 and an error control scheme based on Reed-Solomon coding and block interleaving. The size of the jitter buffer needed was determined by this architecture to avoid service dropout caused by buffer underrun, through analysis of variations in transmission delay. Finally, we assessed this architecture in terms of service latency and reliability by modeling the transmission of uncompressed two-lead electrocardiogram data from the MIT-BIH Arrhythmia Database and highlight the applicability of this wireless technology to telecardiology.

Prediction of North Pacific Height Anomalies During Strong Madden-Julian Oscillation Events

NASA Astrophysics Data System (ADS)

Kai-Chih, T.; Barnes, E. A.; Maloney, E. D.

2017-12-01

The Madden Julian Oscillation (MJO) creates strong variations in extratropical atmospheric circulations that have important implications for subseasonal-to-seasonal prediction. In particular, certain MJO phases are characterized by a consistent modulation of geopotential height in the North Pacific and adjacent regions across different MJO events. Until recently, only limited research has examined the relationship between these robust MJO tropical-extratropical teleconnections and model prediction skill. In this study, reanalysis data (MERRA and ERA-Interim) and ECMWF ensemble hindcasts are used to demonstrate that robust teleconnections in specific MJO phases and time lags are also characterized by excellent agreement in the prediction of geopotential height anoma- lies across model ensemble members at forecast leads of up to 3 weeks. These periods of enhanced prediction capabilities extend the possibility for skillful extratropical weather prediction beyond traditional 10-13 day limits. Furthermore, we also examine the phase dependency of teleconnection robustness by using Linear Baroclinic Model (LBM) and the result is consistent with the ensemble hindcasts : the anomalous heating of MJO phase 2 (phase 6) can consistently generate positive (negative) geopotential height anomalies around the extratropical Pacific with a lead of 15-20 days, while other phases are more sensitive to the variaion of the mean state.

Aeroservoelastic Model Validation and Test Data Analysis of the F/A-18 Active Aeroelastic Wing

NASA Technical Reports Server (NTRS)

Brenner, Martin J.; Prazenica, Richard J.

2003-01-01

Model validation and flight test data analysis require careful consideration of the effects of uncertainty, noise, and nonlinearity. Uncertainty prevails in the data analysis techniques and results in a composite model uncertainty from unmodeled dynamics, assumptions and mechanics of the estimation procedures, noise, and nonlinearity. A fundamental requirement for reliable and robust model development is an attempt to account for each of these sources of error, in particular, for model validation, robust stability prediction, and flight control system development. This paper is concerned with data processing procedures for uncertainty reduction in model validation for stability estimation and nonlinear identification. F/A-18 Active Aeroelastic Wing (AAW) aircraft data is used to demonstrate signal representation effects on uncertain model development, stability estimation, and nonlinear identification. Data is decomposed using adaptive orthonormal best-basis and wavelet-basis signal decompositions for signal denoising into linear and nonlinear identification algorithms. Nonlinear identification from a wavelet-based Volterra kernel procedure is used to extract nonlinear dynamics from aeroelastic responses, and to assist model development and uncertainty reduction for model validation and stability prediction by removing a class of nonlinearity from the uncertainty.

Robust Stability and Control of Multi-Body Ground Vehicles with Uncertain Dynamics and Failures

DTIC Science & Technology

2010-01-01

and N. Zhang, 2008. “Robust stability control of vehicle rollover subject to actuator time delay”. Proc. IMechE Part I: J. of systems and control ...Dynamic Systems and Control Conference, Boston, MA, Sept 2010 R.K. Yedavalli,”Robust Stability of Linear Interval Parameter Matrix Family Problem...for control coupled output regulation for a class of systems is presented. In section 2.1.7, the control design algorithm developed in section

A robust H∞ control-based hierarchical mode transition control system for plug-in hybrid electric vehicle

NASA Astrophysics Data System (ADS)

Yang, Chao; Jiao, Xiaohong; Li, Liang; Zhang, Yuanbo; Chen, Zheng

2018-01-01

To realize a fast and smooth operating mode transition process from electric driving mode to engine-on driving mode, this paper presents a novel robust hierarchical mode transition control method for a plug-in hybrid electric bus (PHEB) with pre-transmission parallel hybrid powertrain. Firstly, the mode transition process is divided into five stages to clearly describe the powertrain dynamics. Based on the dynamics models of powertrain and clutch actuating mechanism, a hierarchical control structure including two robust H∞ controllers in both upper layer and lower layer is proposed. In upper layer, the demand clutch torque can be calculated by a robust H∞controller considering the clutch engaging time and the vehicle jerk. While in lower layer a robust tracking controller with L2-gain is designed to perform the accurate position tracking control, especially when the parameters uncertainties and external disturbance occur in the clutch actuating mechanism. Simulation and hardware-in-the-loop (HIL) test are carried out in a traditional driving condition of PHEB. Results show that the proposed hierarchical control approach can obtain the good control performance: mode transition time is greatly reduced with the acceptable jerk. Meanwhile, the designed control system shows the obvious robustness with the uncertain parameters and disturbance. Therefore, the proposed approach may offer a theoretical reference for the actual vehicle controller.

Aircraft ride quality controller design using new robust root clustering theory for linear uncertain systems

NASA Technical Reports Server (NTRS)

Yedavalli, R. K.

1992-01-01

The aspect of controller design for improving the ride quality of aircraft in terms of damping ratio and natural frequency specifications on the short period dynamics is addressed. The controller is designed to be robust with respect to uncertainties in the real parameters of the control design model such as uncertainties in the dimensional stability derivatives, imperfections in actuator/sensor locations and possibly variations in flight conditions, etc. The design is based on a new robust root clustering theory developed by the author by extending the nominal root clustering theory of Gutman and Jury to perturbed matrices. The proposed methodology allows to get an explicit relationship between the parameters of the root clustering region and the uncertainty radius of the parameter space. The current literature available for robust stability becomes a special case of this unified theory. The bounds derived on the parameter perturbation for robust root clustering are then used in selecting the robust controller.

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

PubMed Central

Daws, Richard E.; Soreq, Eyal; Johnson, Eileanoir B.; Scahill, Rachael I.; Tabrizi, Sarah J.; Barker, Roger A.; Hampshire, Adam

2018-01-01

Objective Huntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real‐life clinical diagnosis in HD. Method A multivariate machine learning approach was applied to resting‐state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross‐group comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy. Results Classification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models. Interpretation We propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials. Ann Neurol 2018;83:532–543 PMID:29405351

Closed-Loop and Robust Control of Quantum Systems

PubMed Central

Wang, Lin-Cheng

2013-01-01

For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control as H ∞ control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention. PMID:23997680

Robust Fuzzy Logic Stabilization with Disturbance Elimination

PubMed Central

Danapalasingam, Kumeresan A.

2014-01-01

A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design. PMID:25177713

A Robust Design Methodology for Optimal Microscale Secondary Flow Control in Compact Inlet Diffusers

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Keller, Dennis J.

2001-01-01

It is the purpose of this study to develop an economical Robust design methodology for microscale secondary flow control in compact inlet diffusers. To illustrate the potential of economical Robust Design methodology, two different mission strategies were considered for the subject inlet, namely Maximum Performance and Maximum HCF Life Expectancy. The Maximum Performance mission maximized total pressure recovery while the Maximum HCF Life Expectancy mission minimized the mean of the first five Fourier harmonic amplitudes, i.e., 'collectively' reduced all the harmonic 1/2 amplitudes of engine face distortion. Each of the mission strategies was subject to a low engine face distortion constraint, i.e., DC60<0.10, which is a level acceptable for commercial engines. For each of these missions strategies, an 'Optimal Robust' (open loop control) and an 'Optimal Adaptive' (closed loop control) installation was designed over a twenty degree angle-of-incidence range. The Optimal Robust installation used economical Robust Design methodology to arrive at a single design which operated over the entire angle-of-incident range (open loop control). The Optimal Adaptive installation optimized all the design parameters at each angle-of-incidence. Thus, the Optimal Adaptive installation would require a closed loop control system to sense a proper signal for each effector and modify that effector device, whether mechanical or fluidic, for optimal inlet performance. In general, the performance differences between the Optimal Adaptive and Optimal Robust installation designs were found to be marginal. This suggests, however, that Optimal Robust open loop installation designs can be very competitive with Optimal Adaptive close loop designs. Secondary flow control in inlets is inherently robust, provided it is optimally designed. Therefore, the new methodology presented in this paper, combined array 'Lower Order' approach to Robust DOE, offers the aerodynamicist a very viable and economical way of exploring the concept of Robust inlet design, where the mission variables are brought directly into the inlet design process and insensitivity or robustness to the mission variables becomes a design objective.

Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models

PubMed Central

Coelho, Antonio Augusto Rodrigues

2016-01-01

This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723

Hydrogen and Oxygen Isotope Ratios in Body Water and Hair: Modeling Isotope Dynamics in Nonhuman Primates

PubMed Central

O’Grady, Shannon P.; Valenzuela, Luciano O.; Remien, Christopher H.; Enright, Lindsey E.; Jorgensen, Matthew J.; Kaplan, Jay R.; Wagner, Janice D.; Cerling, Thure E.; Ehleringer, James R.

2012-01-01

The stable isotopic composition of drinking water, diet, and atmospheric oxygen influence the isotopic composition of body water (2H/1H, 18O/16O expressed as δ2H and δ18O). In turn, body water influences the isotopic composition of organic matter in tissues, such as hair and teeth, which are often used to reconstruct historical dietary and movement patterns of animals and humans. Here, we used a nonhuman primate system (Macaca fascicularis) to test the robustness of two different mechanistic stable isotope models: a model to predict the δ2H and δ18O values of body water and a second model to predict the δ2H and δ18O values of hair. In contrast to previous human-based studies, use of nonhuman primates fed controlled diets allowed us to further constrain model parameter values and evaluate model predictions. Both models reliably predicted the δ2H and δ18O values of body water and of hair. Moreover, the isotope data allowed us to better quantify values for two critical variables in the models: the δ2H and δ18O values of gut water and the 18O isotope fractionation associated with a carbonyl oxygen-water interaction in the gut (αow). Our modeling efforts indicated that better predictions for body water and hair isotope values were achieved by making the isotopic composition of gut water approached that of body water. Additionally, the value of αow was 1.0164, in close agreement with the only other previously measured observation (microbial spore cell walls), suggesting robustness of this fractionation factor across different biological systems. PMID:22553163

Hydrogen and oxygen isotope ratios in body water and hair: modeling isotope dynamics in nonhuman primates.

PubMed

O'Grady, Shannon P; Valenzuela, Luciano O; Remien, Christopher H; Enright, Lindsey E; Jorgensen, Matthew J; Kaplan, Jay R; Wagner, Janice D; Cerling, Thure E; Ehleringer, James R

2012-07-01

The stable isotopic composition of drinking water, diet, and atmospheric oxygen influence the isotopic composition of body water ((2)H/(1)H, (18)O/(16)O expressed as δ(2) H and δ(18)O). In turn, body water influences the isotopic composition of organic matter in tissues, such as hair and teeth, which are often used to reconstruct historical dietary and movement patterns of animals and humans. Here, we used a nonhuman primate system (Macaca fascicularis) to test the robustness of two different mechanistic stable isotope models: a model to predict the δ(2)H and δ(18)O values of body water and a second model to predict the δ(2)H and δ(18)O values of hair. In contrast to previous human-based studies, use of nonhuman primates fed controlled diets allowed us to further constrain model parameter values and evaluate model predictions. Both models reliably predicted the δ(2)H and δ(18)O values of body water and of hair. Moreover, the isotope data allowed us to better quantify values for two critical variables in the models: the δ(2)H and δ(18)O values of gut water and the (18)O isotope fractionation associated with a carbonyl oxygen-water interaction in the gut (α(ow)). Our modeling efforts indicated that better predictions for body water and hair isotope values were achieved by making the isotopic composition of gut water approached that of body water. Additionally, the value of α(ow) was 1.0164, in close agreement with the only other previously measured observation (microbial spore cell walls), suggesting robustness of this fractionation factor across different biological systems. © 2012 Wiley Periodicals, Inc.

First French Pilot Quality Assessment of the EndoPredict Test for Early Luminal Breast Carcinoma.

PubMed

Lehmann-Che, Jacqueline; Miquel, Catherine; Wong, Jennifer; Callens, Celine; Rouleau, Etienne; Quillien, Veronique; Lozano, Nicolas; Cayre, Anne; Lacroix, Ludovic; Bieche, Ivan; Bertheau, Philippe; Teixeira, Luis; Llorca, Frederique Penault; Lamy, Pierre Jean; DE Cremoux, Patricia

2018-05-01

Genomic signatures are needed for the determination of prognosis in patients with early stage, estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancers. EndoPredict test is a RNA-based multigene assay that assesses the risk of 10-year relapse in this context. Quality assessment is a mandatory requirement for a laboratory to address the analytical quality of these molecular analyses. The aim of the study was to demonstrate the robustness of this prognostic test, its usefulness for the patient's treatment strategy, at the national level. This study presents a pilot quality assessment (QA) of the EndoPredict test using composite design, including the follow-up of internal control values (qREF) of the 12 genes of the assay for 151 independent tests and one formalin-fixed paraffin embedded (FFPE) breast cancer sample. The evaluation of the test was performed by comparing the results of six independent medical laboratories. All measures were highly reproducible and quantification of the qREF showed a standard deviation of less than 0.50 and a coefficient of variation always of <2%. All laboratories found concordant results for the breast cancer samples. The mean EndoPredict (EP) score for the breast cancer sample was 4.97±0.24. The mean of EPclin score was 3.07±0.05. This first French independent reported QA assessed the robustness and reproducibility of the EndoPredict test. Such a simple composite design could represent an adapted QA for an expensive diagnostic test. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Robust Learning Control Design for Quantum Unitary Transformations.

PubMed

Wu, Chengzhi; Qi, Bo; Chen, Chunlin; Dong, Daoyi

2017-12-01

Robust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the experimental implementation of quantum operations. In this paper, we extend the systematic methodology of sampling-based learning control (SLC) approach with a gradient flow algorithm for the design of robust quantum unitary transformations. The SLC approach first uses a "training" process to find an optimal control strategy robust against certain ranges of uncertainties. Then a number of randomly selected samples are tested and the performance is evaluated according to their average fidelity. The approach is applied to three typical examples of robust quantum transformation problems including robust quantum transformations in a three-level quantum system, in a superconducting quantum circuit, and in a spin chain system. Numerical results demonstrate the effectiveness of the SLC approach and show its potential applications in various implementation of quantum unitary transformations.

Robust Control Systems.

DTIC Science & Technology

1981-12-01

time control system algorithms that will perform adequately (i.e., at least maintain closed-loop system stability) when ucertain parameters in the...system design models vary significantly. Such a control algorithm is said to have stability robustness-or more simply is said to be "robust". This...cas6s above, the performance is analyzed using a covariance analysis. The development of all the controllers and the performance analysis algorithms is

A Descent Rate Control Approach to Developing an Autonomous Descent Vehicle

NASA Astrophysics Data System (ADS)

Fields, Travis D.

Circular parachutes have been used for aerial payload/personnel deliveries for over 100 years. In the past two decades, significant work has been done to improve the landing accuracies of cargo deliveries for humanitarian and military applications. This dissertation discusses the approach developed in which a circular parachute is used in conjunction with an electro-mechanical reefing system to manipulate the landing location. Rather than attempt to steer the autonomous descent vehicle directly, control of the landing location is accomplished by modifying the amount of time spent in a particular wind layer. Descent rate control is performed by reversibly reefing the parachute canopy. The first stage of the research investigated the use of a single actuation during descent (with periodic updates), in conjunction with a curvilinear target. Simulation results using real-world wind data are presented, illustrating the utility of the methodology developed. Additionally, hardware development and flight-testing of the single actuation autonomous descent vehicle are presented. The next phase of the research focuses on expanding the single actuation descent rate control methodology to incorporate a multi-actuation path-planning system. By modifying the parachute size throughout the descent, the controllability of the system greatly increases. The trajectory planning methodology developed provides a robust approach to accurately manipulate the landing location of the vehicle. The primary benefits of this system are the inherent robustness to release location errors and the ability to overcome vehicle uncertainties (mass, parachute size, etc.). A separate application of the path-planning methodology is also presented. An in-flight path-prediction system was developed for use in high-altitude ballooning by utilizing the path-planning methodology developed for descent vehicles. The developed onboard system improves landing location predictions in-flight using collected flight information during the ascent and descent. Simulation and real-world flight tests (using the developed low-cost hardware) demonstrate the significance of the improvements achievable when flying the developed system.

Model reference tracking control of an aircraft: a robust adaptive approach

NASA Astrophysics Data System (ADS)

Tanyer, Ilker; Tatlicioglu, Enver; Zergeroglu, Erkan

2017-05-01

This work presents the design and the corresponding analysis of a nonlinear robust adaptive controller for model reference tracking of an aircraft that has parametric uncertainties in its system matrices and additive state- and/or time-dependent nonlinear disturbance-like terms in its dynamics. Specifically, robust integral of the sign of the error feedback term and an adaptive term is fused with a proportional integral controller. Lyapunov-based stability analysis techniques are utilised to prove global asymptotic convergence of the output tracking error. Extensive numerical simulations are presented to illustrate the performance of the proposed robust adaptive controller.

Modification and Validation of Conceptual Design Aerodynamic Prediction Method HASC95 With VTXCHN

NASA Technical Reports Server (NTRS)

Albright, Alan E.; Dixon, Charles J.; Hegedus, Martin C.

1996-01-01

A conceptual/preliminary design level subsonic aerodynamic prediction code HASC (High Angle of Attack Stability and Control) has been improved in several areas, validated, and documented. The improved code includes improved methodologies for increased accuracy and robustness, and simplified input/output files. An engineering method called VTXCHN (Vortex Chine) for prediciting nose vortex shedding from circular and non-circular forebodies with sharp chine edges has been improved and integrated into the HASC code. This report contains a summary of modifications, description of the code, user's guide, and validation of HASC. Appendices include discussion of a new HASC utility code, listings of sample input and output files, and a discussion of the application of HASC to buffet analysis.

Uncertainty analysis and robust trajectory linearization control of a flexible air-breathing hypersonic vehicle

NASA Astrophysics Data System (ADS)

Pu, Zhiqiang; Tan, Xiangmin; Fan, Guoliang; Yi, Jianqiang

2014-08-01

Flexible air-breathing hypersonic vehicles feature significant uncertainties which pose huge challenges to robust controller designs. In this paper, four major categories of uncertainties are analyzed, that is, uncertainties associated with flexible effects, aerodynamic parameter variations, external environmental disturbances, and control-oriented modeling errors. A uniform nonlinear uncertainty model is explored for the first three uncertainties which lumps all uncertainties together and consequently is beneficial for controller synthesis. The fourth uncertainty is additionally considered in stability analysis. Based on these analyses, the starting point of the control design is to decompose the vehicle dynamics into five functional subsystems. Then a robust trajectory linearization control (TLC) scheme consisting of five robust subsystem controllers is proposed. In each subsystem controller, TLC is combined with the extended state observer (ESO) technique for uncertainty compensation. The stability of the overall closed-loop system with the four aforementioned uncertainties and additional singular perturbations is analyzed. Particularly, the stability of nonlinear ESO is also discussed from a Liénard system perspective. At last, simulations demonstrate the great control performance and the uncertainty rejection ability of the robust scheme.

A distributed model predictive control scheme for leader-follower multi-agent systems

NASA Astrophysics Data System (ADS)

Franzè, Giuseppe; Lucia, Walter; Tedesco, Francesco

2018-02-01

In this paper, we present a novel receding horizon control scheme for solving the formation problem of leader-follower configurations. The algorithm is based on set-theoretic ideas and is tuned for agents described by linear time-invariant (LTI) systems subject to input and state constraints. The novelty of the proposed framework relies on the capability to jointly use sequences of one-step controllable sets and polyhedral piecewise state-space partitions in order to online apply the 'better' control action in a distributed receding horizon fashion. Moreover, we prove that the design of both robust positively invariant sets and one-step-ahead controllable regions is achieved in a distributed sense. Simulations and numerical comparisons with respect to centralised and local-based strategies are finally performed on a group of mobile robots to demonstrate the effectiveness of the proposed control strategy.

Decentralized adaptive robust control based on sliding mode and nonlinear compensator for the control of ankle movement using functional electrical stimulation of agonist-antagonist muscles

NASA Astrophysics Data System (ADS)

Kobravi, Hamid-Reza; Erfanian, Abbas

2009-08-01

A decentralized control methodology is designed for the control of ankle dorsiflexion and plantarflexion in paraplegic subjects with electrical stimulation of tibialis anterior and calf muscles. Each muscle joint is considered as a subsystem and individual controllers are designed for each subsystem. Each controller operates solely on its associated subsystem, with no exchange of information between the subsystems. The interactions between the subsystems are taken as external disturbances for each isolated subsystem. In order to achieve robustness with respect to external disturbances, unmodeled dynamics, model uncertainty and time-varying properties of muscle-joint dynamics, a robust control framework is proposed which is based on the synergistic combination of an adaptive nonlinear compensator with a sliding mode control and is referred to as an adaptive robust control. Extensive simulations and experiments on healthy and paraplegic subjects were performed to demonstrate the robustness against the time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, fast convergence, stability and tracking accuracy of the proposed method. The results indicate that the decentralized robust control provides excellent tracking control for different reference trajectories and can generate control signals to compensate the muscle fatigue and reject the external disturbance. Moreover, the controller is able to automatically regulate the interaction between agonist and antagonist muscles under different conditions of operating without any preprogrammed antagonist activities.

Decentralized adaptive robust control based on sliding mode and nonlinear compensator for the control of ankle movement using functional electrical stimulation of agonist-antagonist muscles.

PubMed

Kobravi, Hamid-Reza; Erfanian, Abbas

2009-08-01

A decentralized control methodology is designed for the control of ankle dorsiflexion and plantarflexion in paraplegic subjects with electrical stimulation of tibialis anterior and calf muscles. Each muscle joint is considered as a subsystem and individual controllers are designed for each subsystem. Each controller operates solely on its associated subsystem, with no exchange of information between the subsystems. The interactions between the subsystems are taken as external disturbances for each isolated subsystem. In order to achieve robustness with respect to external disturbances, unmodeled dynamics, model uncertainty and time-varying properties of muscle-joint dynamics, a robust control framework is proposed which is based on the synergistic combination of an adaptive nonlinear compensator with a sliding mode control and is referred to as an adaptive robust control. Extensive simulations and experiments on healthy and paraplegic subjects were performed to demonstrate the robustness against the time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, fast convergence, stability and tracking accuracy of the proposed method. The results indicate that the decentralized robust control provides excellent tracking control for different reference trajectories and can generate control signals to compensate the muscle fatigue and reject the external disturbance. Moreover, the controller is able to automatically regulate the interaction between agonist and antagonist muscles under different conditions of operating without any preprogrammed antagonist activities.

Anticipatory control: A software retrofit for current plant controllers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parthasarathy, S.; Parlos, A.G.; Atiya, A.F.

1993-01-01

The design and simulated testing of an artificial neural network (ANN)-based self-adapting controller for complex process systems are presented in this paper. The proposed controller employs concepts based on anticipatory systems, which have been widely used in the petroleum and chemical industries, and they are slowly finding their way into the power industry. In particular, model predictive control (MPC) is used for the systematic adaptation of the controller parameters to achieve desirable plant performance over the entire operating envelope. The versatile anticipatory control algorithm developed in this study is projected to enhance plant performance and lend robustness to drifts inmore » plant parameters and to modeling uncertainties. This novel technique of integrating recurrent ANNs with a conventional controller structure appears capable of controlling complex, nonlinear, and nonminimum phase process systems. The direct, on-line adaptive control algorithm presented in this paper considers the plant response over a finite time horizon, diminishing the need for manual control or process interruption for controller gain tuning.« less

The Influence of Parental Control and Parent-Child Relational Qualities on Adolescent Internet Addiction: A 3-Year Longitudinal Study in Hong Kong

PubMed Central

Shek, Daniel T. L.; Zhu, Xiaoqin; Ma, Cecilia M. S.

2018-01-01

This study investigated how parental behavioral control, parental psychological control, and parent-child relational qualities predicted the initial level and rate of change in adolescent internet addiction (IA) across the junior high school years. The study also investigated the concurrent and longitudinal effects of different parenting factors on adolescent IA. Starting from the 2009/2010 academic year, 3,328 Grade 7 students (Mage = 12.59 ± 0.74 years) from 28 randomly selected secondary schools in Hong Kong responded on a yearly basis to a questionnaire measuring multiple constructs including socio-demographic characteristics, perceived parenting characteristics, and IA. Individual growth curve (IGC) analyses showed that adolescent IA slightly decreased during junior high school years. While behavioral control of both parents was negatively related to the initial level of adolescent IA, only paternal behavioral control showed a significant positive relationship with the rate of linear change in IA, suggesting that higher paternal behavioral control predicted a slower decrease in IA. In addition, fathers' and mothers' psychological control was positively associated with the initial level of adolescent IA, but increase in maternal psychological control predicted a faster drop in IA. Finally, parent-child relational qualities negatively and positively predicted the initial level and the rate of change in IA, respectively. When all parenting factors were considered simultaneously, multiple regression analyses revealed that paternal behavioral control and psychological control as well as maternal psychological control and mother-child relational quality were significant concurrent predictors of adolescent IA at Wave 2 and Wave 3. Regarding the longitudinal predicting effects, paternal psychological control and mother-child relational quality at Wave 1 were the two most robust predictors of later adolescent IA at Wave 2 and Wave 3. The above findings underscore the importance of the parent-child subsystem qualities in influencing adolescent IA in the junior high school years. In particular, these findings shed light on the different impacts of fathering and mothering which are neglected in the scientific literature. While the findings based on the levels of IA are consistent with the existing theoretical models, findings on the rate of change are novel. PMID:29765349

The Influence of Parental Control and Parent-Child Relational Qualities on Adolescent Internet Addiction: A 3-Year Longitudinal Study in Hong Kong.

PubMed

Shek, Daniel T L; Zhu, Xiaoqin; Ma, Cecilia M S

2018-01-01

This study investigated how parental behavioral control, parental psychological control, and parent-child relational qualities predicted the initial level and rate of change in adolescent internet addiction (IA) across the junior high school years. The study also investigated the concurrent and longitudinal effects of different parenting factors on adolescent IA. Starting from the 2009/2010 academic year, 3,328 Grade 7 students ( M age = 12.59 ± 0.74 years) from 28 randomly selected secondary schools in Hong Kong responded on a yearly basis to a questionnaire measuring multiple constructs including socio-demographic characteristics, perceived parenting characteristics, and IA. Individual growth curve (IGC) analyses showed that adolescent IA slightly decreased during junior high school years. While behavioral control of both parents was negatively related to the initial level of adolescent IA, only paternal behavioral control showed a significant positive relationship with the rate of linear change in IA, suggesting that higher paternal behavioral control predicted a slower decrease in IA. In addition, fathers' and mothers' psychological control was positively associated with the initial level of adolescent IA, but increase in maternal psychological control predicted a faster drop in IA. Finally, parent-child relational qualities negatively and positively predicted the initial level and the rate of change in IA, respectively. When all parenting factors were considered simultaneously, multiple regression analyses revealed that paternal behavioral control and psychological control as well as maternal psychological control and mother-child relational quality were significant concurrent predictors of adolescent IA at Wave 2 and Wave 3. Regarding the longitudinal predicting effects, paternal psychological control and mother-child relational quality at Wave 1 were the two most robust predictors of later adolescent IA at Wave 2 and Wave 3. The above findings underscore the importance of the parent-child subsystem qualities in influencing adolescent IA in the junior high school years. In particular, these findings shed light on the different impacts of fathering and mothering which are neglected in the scientific literature. While the findings based on the levels of IA are consistent with the existing theoretical models, findings on the rate of change are novel.

Robust dynamic inversion controller design and analysis (using the X-38 vehicle as a case study)

NASA Astrophysics Data System (ADS)

Ito, Daigoro

A new way to approach robust Dynamic Inversion controller synthesis is addressed in this paper. A Linear Quadratic Gaussian outer-loop controller improves the robustness of a Dynamic Inversion inner-loop controller in the presence of uncertainties. Desired dynamics are given by the dynamic compensator, which shapes the loop. The selected dynamics are based on both performance and stability robustness requirements. These requirements are straightforwardly formulated as frequency-dependent singular value bounds during synthesis of the controller. Performance and robustness of the designed controller is tested using a worst case time domain quadratic index, which is a simple but effective way to measure robustness due to parameter variation. Using this approach, a lateral-directional controller for the X-38 vehicle is designed and its robustness to parameter variations and disturbances is analyzed. It is found that if full state measurements are available, the performance of the designed lateral-directional control system, measured by the chosen cost function, improves by approximately a factor of four. Also, it is found that the designed system is stable up to a parametric variation of 1.65 standard deviation with the set of uncertainty considered. The system robustness is determined to be highly sensitive to the dihedral derivative and the roll damping coefficients. The controller analysis is extended to the nonlinear system where both control input displacements and rates are bounded. In this case, the considered nonlinear system is stable up to 48.1° in bank angle and 1.59° in sideslip angle variations, indicating it is more sensitive to variations in sideslip angle than in bank angle. This nonlinear approach is further extended for the actuator failure mode analysis. The results suggest that the designed system maintains a high level of stability in the event of aileron failure. However, only 35% or less of the original stability range is maintained for the rudder failure case. Overall, this combination of controller synthesis and robustness criteria compares well with the mu-synthesis technique. It also is readily accessible to the practicing engineer, in terms of understanding and use.

Practical robustness measures in multivariable control system analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Lehtomaki, N. A.

1981-01-01

The robustness of the stability of multivariable linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria. Available robustness tests are unified under a common framework based on the nature and structure of model errors. These results are derived using a multivariable version of Nyquist's stability theorem in which the minimum singular value of the return difference transfer matrix is shown to be the multivariable generalization of the distance to the critical point on a single input, single output Nyquist diagram. Using the return difference transfer matrix, a very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived. The robustness tests that explicitly utilized model error structure are able to guarantee feedback system stability in the face of model errors of larger magnitude than those robustness tests that do not. The robustness of linear quadratic Gaussian control systems are analyzed.

A novel robust speed controller scheme for PMBLDC motor.

PubMed

Thirusakthimurugan, P; Dananjayan, P

2007-10-01

The design of speed and position controllers for permanent magnet brushless DC motor (PMBLDC) drive remains as an open problem in the field of motor drives. A precise speed control of PMBLDC motor is complex due to nonlinear coupling between winding currents and rotor speed. In addition, the nonlinearity present in the developed torque due to magnetic saturation of the rotor further complicates this issue. This paper presents a novel control scheme to the conventional PMBLDC motor drive, which aims at improving the robustness by complete decoupling of the design besides minimizing the mutual influence among the speed and current control loops. The interesting feature of this robust control scheme is its suitability for both static and dynamic aspects. The effectiveness of the proposed robust speed control scheme is verified through simulations.

Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems.

PubMed

Wang, Ding; Liu, Derong; Zhang, Yun; Li, Hongyi

2018-01-01

In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for nonlinear systems involving matched uncertainties. The augmented system considering the tracking error and the reference trajectory is formulated and then addressed under adaptive critic optimal control formulation, where the initial stabilizing controller is not needed. The approximate control law is derived via solving the Hamilton-Jacobi-Bellman equation related to the nominal augmented system, followed by closed-loop stability analysis. The robust tracking control performance is guaranteed theoretically via Lyapunov approach and also verified through simulation illustration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Self-assembled lipid--polymer hybrid nanoparticles: a robust drug delivery platform.

PubMed

Zhang, Liangfang; Chan, Juliana M; Gu, Frank X; Rhee, June-Wha; Wang, Andrew Z; Radovic-Moreno, Aleksandar F; Alexis, Frank; Langer, Robert; Farokhzad, Omid C

2008-08-01

We report the engineering of a novel lipid-polymer hybrid nanoparticle (NP) as a robust drug delivery platform, with high drug encapsulation yield, tunable and sustained drug release profile, excellent serum stability, and potential for differential targeting of cells or tissues. The NP comprises three distinct functional components: (i) a hydrophobic polymeric core where poorly water-soluble drugs can be encapsulated; (ii) a hydrophilic polymeric shell with antibiofouling properties to enhance NP stability and systemic circulation half-life; and (iii) a lipid monolayer at the interface of the core and the shell that acts as a molecular fence to promote drug retention inside the polymeric core, thereby enhancing drug encapsulation efficiency, increasing drug loading yield, and controlling drug release. The NP is prepared by self-assembly through a single-step nanoprecipitation method in a reproducible and predictable manner, making it potentially suitable for scale-up.

Self-Assembled Lipid-Polymer Hybrid Nanoparticles: A Robust Drug Delivery Platform

PubMed Central

Zhang, Liangfang; Chan, Juliana M; Gu, Frank X; Rhee, June-Wha; Wang, Andrew Z; Radovic-Moreno, Aleksandar F; Alexis, Frank; Langer, Robert; Farokhzad, Omid C

2014-01-01

We report the engineering of a novel lipid-polymer hybrid nanoparticle (NP) as a robust drug delivery platform, with high drug encapsulation yield, tunable and sustained drug release profile, excellent serum stability, and potential for differential targeting of cells or tissues. The NP is comprised of three distinct functional components: i) a hydrophobic polymeric core where poorly water-soluble drugs can be encapsulated; ii) a hydrophilic polymeric shell with anti-biofouling properties to enhance NP stability and systemic circulation half-life; and iii) a lipid monolayer at the interface of the core and the shell that acts as a molecular fence to promote drug retention inside the polymeric core, thereby enhancing drug encapsulation efficiency, increasing drug loading yield, and controlling drug release. The NP is prepared by self-assembly through a single-step nanoprecipitation method in a reproducible and predictable manner, making it potentially suitable for scale-up PMID:19206374

Robust singlet fission in pentacene thin films with tuned charge transfer interactions.

PubMed

Broch, K; Dieterle, J; Branchi, F; Hestand, N J; Olivier, Y; Tamura, H; Cruz, C; Nichols, V M; Hinderhofer, A; Beljonne, D; Spano, F C; Cerullo, G; Bardeen, C J; Schreiber, F

2018-03-05

Singlet fission, the spin-allowed photophysical process converting an excited singlet state into two triplet states, has attracted significant attention for device applications. Research so far has focused mainly on the understanding of singlet fission in pure materials, yet blends offer the promise of a controlled tuning of intermolecular interactions, impacting singlet fission efficiencies. Here we report a study of singlet fission in mixtures of pentacene with weakly interacting spacer molecules. Comparison of experimentally determined stationary optical properties and theoretical calculations indicates a reduction of charge-transfer interactions between pentacene molecules with increasing spacer molecule fraction. Theory predicts that the reduced interactions slow down singlet fission in these blends, but surprisingly we find that singlet fission occurs on a timescale comparable to that in pure crystalline pentacene. We explain the observed robustness of singlet fission in such mixed films by a mechanism of exciton diffusion to hot spots with closer intermolecular spacings.

Numerical Algorithms for Acoustic Integrals - The Devil is in the Details

NASA Technical Reports Server (NTRS)

Brentner, Kenneth S.

1996-01-01

The accurate prediction of the aeroacoustic field generated by aerospace vehicles or nonaerospace machinery is necessary for designers to control and reduce source noise. Powerful computational aeroacoustic methods, based on various acoustic analogies (primarily the Lighthill acoustic analogy) and Kirchhoff methods, have been developed for prediction of noise from complicated sources, such as rotating blades. Both methods ultimately predict the noise through a numerical evaluation of an integral formulation. In this paper, we consider three generic acoustic formulations and several numerical algorithms that have been used to compute the solutions to these formulations. Algorithms for retarded-time formulations are the most efficient and robust, but they are difficult to implement for supersonic-source motion. Collapsing-sphere and emission-surface formulations are good alternatives when supersonic-source motion is present, but the numerical implementations of these formulations are more computationally demanding. New algorithms - which utilize solution adaptation to provide a specified error level - are needed.

Body Dysmorphic, Obsessive-Compulsive, and Social Anxiety Disorder Beliefs as Predictors of In Vivo Stressor Responding.

PubMed

Parsons, E Marie; Straub, Kelsey T; Smith, April R; Clerkin, Elise M

2017-06-01

This study tested the potential transdiagnostic nature of body dysmorphic disorder (BDD), obsessive-compulsive disorder (OCD), and social anxiety disorder (SAD) beliefs, in addition to testing the specificity of those beliefs, in predicting how individuals responded to symptom-specific stressors. Participants included 127 adults (75% women) with a broad range of symptom severity. Path analysis was used to evaluate whether specific maladaptive beliefs predicted distress in response to symptom-relevant stressors over and above other beliefs and baseline distress. SAD beliefs emerged as a significant predictor of distress in response to a mirror gazing (BDD-relevant), a thought (OCD-relevant), and a public speaking (SAD-relevant) task, controlling for other disorder beliefs and baseline distress. BDD beliefs were also a robust predictor of BDD stressor responding. Results suggest that social anxiety-relevant beliefs may function as a transdiagnostic risk factor that predicts in vivo symptoms across a range of problem areas.

Ultra-fast consensus of discrete-time multi-agent systems with multi-step predictive output feedback

NASA Astrophysics Data System (ADS)

Zhang, Wenle; Liu, Jianchang

2016-04-01

This article addresses the ultra-fast consensus problem of high-order discrete-time multi-agent systems based on a unified consensus framework. A novel multi-step predictive output mechanism is proposed under a directed communication topology containing a spanning tree. By predicting the outputs of a network several steps ahead and adding this information into the consensus protocol, it is shown that the asymptotic convergence factor is improved by a power of q + 1 compared to the routine consensus. The difficult problem of selecting the optimal control gain is solved well by introducing a variable called convergence step. In addition, the ultra-fast formation achievement is studied on the basis of this new consensus protocol. Finally, the ultra-fast consensus with respect to a reference model and robust consensus is discussed. Some simulations are performed to illustrate the effectiveness of the theoretical results.

Chill Down Process of Hydrogen Transport Pipelines

NASA Technical Reports Server (NTRS)

Mei, Renwei; Klausner, James

2006-01-01

A pseudo-steady model has been developed to predict the chilldown history of pipe wall temperature in the horizontal transport pipeline for cryogenic fluids. A new film boiling heat transfer model is developed by incorporating the stratified flow structure for cryogenic chilldown. A modified nucleate boiling heat transfer correlation for cryogenic chilldown process inside a horizontal pipe is proposed. The efficacy of the correlations is assessed by comparing the model predictions with measured values of wall temperature in several azimuthal positions in a well controlled experiment by Chung et al. (2004). The computed pipe wall temperature histories match well with the measured results. The present model captures important features of thermal interaction between the pipe wall and the cryogenic fluid, provides a simple and robust platform for predicting pipe wall chilldown history in long horizontal pipe at relatively low computational cost, and builds a foundation to incorporate the two-phase hydrodynamic interaction in the chilldown process.

A Statistics-Based Cracking Criterion of Resin-Bonded Silica Sand for Casting Process Simulation

NASA Astrophysics Data System (ADS)

Wang, Huimin; Lu, Yan; Ripplinger, Keith; Detwiler, Duane; Luo, Alan A.

2017-02-01

Cracking of sand molds/cores can result in many casting defects such as veining. A robust cracking criterion is needed in casting process simulation for predicting/controlling such defects. A cracking probability map, relating to fracture stress and effective volume, was proposed for resin-bonded silica sand based on Weibull statistics. Three-point bending test results of sand samples were used to generate the cracking map and set up a safety line for cracking criterion. Tensile test results confirmed the accuracy of the safety line for cracking prediction. A laboratory casting experiment was designed and carried out to predict cracking of a cup mold during aluminum casting. The stress-strain behavior and the effective volume of the cup molds were calculated using a finite element analysis code ProCAST®. Furthermore, an energy dispersive spectroscopy fractographic examination of the sand samples confirmed the binder cracking in resin-bonded silica sand.

Robust approximation-free prescribed performance control for nonlinear systems and its application

NASA Astrophysics Data System (ADS)

Sun, Ruisheng; Na, Jing; Zhu, Bin

2018-02-01

This paper presents a robust prescribed performance control approach and its application to nonlinear tail-controlled missile systems with unknown dynamics and uncertainties. The idea of prescribed performance function (PPF) is incorporated into the control design, such that both the steady-state and transient control performance can be strictly guaranteed. Unlike conventional PPF-based control methods, we further tailor a recently proposed systematic control design procedure (i.e. approximation-free control) using the transformed tracking error dynamics, which provides a proportional-like control action. Hence, the function approximators (e.g. neural networks, fuzzy systems) that are widely used to address the unknown nonlinearities in the nonlinear control designs are not needed. The proposed control design leads to a robust yet simplified function approximation-free control for nonlinear systems. The closed-loop system stability and the control error convergence are all rigorously proved. Finally, comparative simulations are conducted based on nonlinear missile systems to validate the improved response and the robustness of the proposed control method.

Gap-metric-based robustness analysis of nonlinear systems with full and partial feedback linearisation

NASA Astrophysics Data System (ADS)

Al-Gburi, A.; Freeman, C. T.; French, M. C.

2018-06-01

This paper uses gap metric analysis to derive robustness and performance margins for feedback linearising controllers. Distinct from previous robustness analysis, it incorporates the case of output unstructured uncertainties, and is shown to yield general stability conditions which can be applied to both stable and unstable plants. It then expands on existing feedback linearising control schemes by introducing a more general robust feedback linearising control design which classifies the system nonlinearity into stable and unstable components and cancels only the unstable plant nonlinearities. This is done in order to preserve the stabilising action of the inherently stabilising nonlinearities. Robustness and performance margins are derived for this control scheme, and are expressed in terms of bounds on the plant nonlinearities and the accuracy of the cancellation of the unstable plant nonlinearity by the controller. Case studies then confirm reduced conservatism compared with standard methods.

Achievement Emotions and Academic Performance: Longitudinal Models of Reciprocal Effects.

PubMed

Pekrun, Reinhard; Lichtenfeld, Stephanie; Marsh, Herbert W; Murayama, Kou; Goetz, Thomas

2017-09-01

A reciprocal effects model linking emotion and achievement over time is proposed. The model was tested using five annual waves of the Project for the Analysis of Learning and Achievement in Mathematics (PALMA) longitudinal study, which investigated adolescents' development in mathematics (Grades 5-9; N = 3,425 German students; mean starting age = 11.7 years; representative sample). Structural equation modeling showed that positive emotions (enjoyment, pride) positively predicted subsequent achievement (math end-of-the-year grades and test scores), and that achievement positively predicted these emotions, controlling for students' gender, intelligence, and family socioeconomic status. Negative emotions (anger, anxiety, shame, boredom, hopelessness) negatively predicted achievement, and achievement negatively predicted these emotions. The findings were robust across waves, achievement indicators, and school tracks, highlighting the importance of emotions for students' achievement and of achievement for the development of emotions. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.

Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

PubMed Central

Svečko, Rajko

2014-01-01

This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

Neural robust stabilization via event-triggering mechanism and adaptive learning technique.

PubMed

Wang, Ding; Liu, Derong

2018-06-01

The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties. Copyright © 2018 Elsevier Ltd. All rights reserved.

Optimization of robustness of interdependent network controllability by redundant design

PubMed Central

2018-01-01

Controllability of complex networks has been a hot topic in recent years. Real networks regarded as interdependent networks are always coupled together by multiple networks. The cascading process of interdependent networks including interdependent failure and overload failure will destroy the robustness of controllability for the whole network. Therefore, the optimization of the robustness of interdependent network controllability is of great importance in the research area of complex networks. In this paper, based on the model of interdependent networks constructed first, we determine the cascading process under different proportions of node attacks. Then, the structural controllability of interdependent networks is measured by the minimum driver nodes. Furthermore, we propose a parameter which can be obtained by the structure and minimum driver set of interdependent networks under different proportions of node attacks and analyze the robustness for interdependent network controllability. Finally, we optimize the robustness of interdependent network controllability by redundant design including node backup and redundancy edge backup and improve the redundant design by proposing different strategies according to their cost. Comparative strategies of redundant design are conducted to find the best strategy. Results shows that node backup and redundancy edge backup can indeed decrease those nodes suffering from failure and improve the robustness of controllability. Considering the cost of redundant design, we should choose BBS (betweenness-based strategy) or DBS (degree based strategy) for node backup and HDF(high degree first) for redundancy edge backup. Above all, our proposed strategies are feasible and effective at improving the robustness of interdependent network controllability. PMID:29438426

Robust control of electrostatic torsional micromirrors using adaptive sliding-mode control

NASA Astrophysics Data System (ADS)

Sane, Harshad S.; Yazdi, Navid; Mastrangelo, Carlos H.

2005-01-01

This paper presents high-resolution control of torsional electrostatic micromirrors beyond their inherent pull-in instability using robust sliding-mode control (SMC). The objectives of this paper are two-fold - firstly, to demonstrate the applicability of SMC for MEMS devices; secondly - to present a modified SMC algorithm that yields improved control accuracy. SMC enables compact realization of a robust controller tolerant of device characteristic variations and nonlinearities. Robustness of the control loop is demonstrated through extensive simulations and measurements on MEMS with a wide range in their characteristics. Control of two-axis gimbaled micromirrors beyond their pull-in instability with overall 10-bit pointing accuracy is confirmed experimentally. In addition, this paper presents an analysis of the sources of errors in discrete-time implementation of the control algorithm. To minimize these errors, we present an adaptive version of the SMC algorithm that yields substantial performance improvement without considerably increasing implementation complexity.

Achieving Robustness to Uncertainty for Financial Decision-making

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barnum, George M.; Van Buren, Kendra L.; Hemez, Francois M.

2014-01-10

This report investigates the concept of robustness analysis to support financial decision-making. Financial models, that forecast future stock returns or market conditions, depend on assumptions that might be unwarranted and variables that might exhibit large fluctuations from their last-known values. The analysis of robustness explores these sources of uncertainty, and recommends model settings such that the forecasts used for decision-making are as insensitive as possible to the uncertainty. A proof-of-concept is presented with the Capital Asset Pricing Model. The robustness of model predictions is assessed using info-gap decision theory. Info-gaps are models of uncertainty that express the “distance,” or gapmore » of information, between what is known and what needs to be known in order to support the decision. The analysis yields a description of worst-case stock returns as a function of increasing gaps in our knowledge. The analyst can then decide on the best course of action by trading-off worst-case performance with “risk”, which is how much uncertainty they think needs to be accommodated in the future. The report also discusses the Graphical User Interface, developed using the MATLAB® programming environment, such that the user can control the analysis through an easy-to-navigate interface. Three directions of future work are identified to enhance the present software. First, the code should be re-written using the Python scientific programming software. This change will achieve greater cross-platform compatibility, better portability, allow for a more professional appearance, and render it independent from a commercial license, which MATLAB® requires. Second, a capability should be developed to allow users to quickly implement and analyze their own models. This will facilitate application of the software to the evaluation of proprietary financial models. The third enhancement proposed is to add the ability to evaluate multiple models simultaneously. When two models reflect past data with similar accuracy, the more robust of the two is preferable for decision-making because its predictions are, by definition, less sensitive to the uncertainty.« less

Direct adaptive robust tracking control for 6 DOF industrial robot with enhanced accuracy.

PubMed

Yin, Xiuxing; Pan, Li

2018-01-01

A direct adaptive robust tracking control is proposed for trajectory tracking of 6 DOF industrial robot in the presence of parametric uncertainties, external disturbances and uncertain nonlinearities. The controller is designed based on the dynamic characteristics in the working space of the end-effector of the 6 DOF robot. The controller includes robust control term and model compensation term that is developed directly based on the input reference or desired motion trajectory. A projection-type parametric adaptation law is also designed to compensate for parametric estimation errors for the adaptive robust control. The feasibility and effectiveness of the proposed direct adaptive robust control law and the associated projection-type parametric adaptation law have been comparatively evaluated based on two 6 DOF industrial robots. The test results demonstrate that the proposed control can be employed to better maintain the desired trajectory tracking even in the presence of large parametric uncertainties and external disturbances as compared with PD controller and nonlinear controller. The parametric estimates also eventually converge to the real values along with the convergence of tracking errors, which further validate the effectiveness of the proposed parametric adaption law. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Experimental validation of a Lyapunov-based controller for the plasma safety factor and plasma pressure in the TCV tokamak

NASA Astrophysics Data System (ADS)

Mavkov, B.; Witrant, E.; Prieur, C.; Maljaars, E.; Felici, F.; Sauter, O.; the TCV-Team

2018-05-01

In this paper, model-based closed-loop algorithms are derived for distributed control of the inverse of the safety factor profile and the plasma pressure parameter β of the TCV tokamak. The simultaneous control of the two plasma quantities is performed by combining two different control methods. The control design of the plasma safety factor is based on an infinite-dimensional setting using Lyapunov analysis for partial differential equations, while the control of the plasma pressure parameter is designed using control techniques for single-input and single-output systems. The performance and robustness of the proposed controller is analyzed in simulations using the fast plasma transport simulator RAPTOR. The control is then implemented and tested in experiments in TCV L-mode discharges using the RAPTOR model predicted estimates for the q-profile. The distributed control in TCV is performed using one co-current and one counter-current electron cyclotron heating actuation.

Robust control algorithms for Mars aerobraking

NASA Technical Reports Server (NTRS)

Shipley, Buford W., Jr.; Ward, Donald T.

1992-01-01

Four atmospheric guidance concepts have been adapted to control an interplanetary vehicle aerobraking in the Martian atmosphere. The first two offer improvements to the Analytic Predictor Corrector (APC) to increase its robustness to density variations. The second two are variations of a new Liapunov tracking exit phase algorithm, developed to guide the vehicle along a reference trajectory. These four new controllers are tested using a six degree of freedom computer simulation to evaluate their robustness. MARSGRAM is used to develop realistic atmospheres for the study. When square wave density pulses perturb the atmosphere all four controllers are successful. The algorithms are tested against atmospheres where the inbound and outbound density functions are different. Square wave density pulses are again used, but only for the outbound leg of the trajectory. Additionally, sine waves are used to perturb the density function. The new algorithms are found to be more robust than any previously tested and a Liapunov controller is selected as the most robust control algorithm overall examined.

A Data-Driven Solution for Performance Improvement

NASA Technical Reports Server (NTRS)

2002-01-01

Marketed as the "Software of the Future," Optimal Engineering Systems P.I. EXPERT(TM) technology offers statistical process control and optimization techniques that are critical to businesses looking to restructure or accelerate operations in order to gain a competitive edge. Kennedy Space Center granted Optimal Engineering Systems the funding and aid necessary to develop a prototype of the process monitoring and improvement software. Completion of this prototype demonstrated that it was possible to integrate traditional statistical quality assurance tools with robust optimization techniques in a user- friendly format that is visually compelling. Using an expert system knowledge base, the software allows the user to determine objectives, capture constraints and out-of-control processes, predict results, and compute optimal process settings.

A frequency-domain estimator for use in adaptive control systems

NASA Technical Reports Server (NTRS)

Lamaire, Richard O.; Valavani, Lena; Athans, Michael; Stein, Gunter

1991-01-01

This paper presents a frequency-domain estimator that can identify both a parametrized nominal model of a plant as well as a frequency-domain bounding function on the modeling error associated with this nominal model. This estimator, which we call a robust estimator, can be used in conjunction with a robust control-law redesign algorithm to form a robust adaptive controller.

The use of singular value gradients and optimization techniques to design robust controllers for multiloop systems

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two-input/two-output drone flight control system.

The use of singular value gradients and optimization techniques to design robust controllers for multiloop systems

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two output drone flight control system.

A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures

PubMed Central

2014-01-01

Background Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict. Incorporating known structural information can improve prediction accuracy; however, there are not many methods for prediction of pseudoknotted structures that can incorporate structural information as input. There is even less understanding of the relative robustness of these methods with respect to partial information. Results We present a new method, Iterative HFold, for pseudoknotted RNA secondary structure prediction. Iterative HFold takes as input a pseudoknot-free structure, and produces a possibly pseudoknotted structure whose energy is at least as low as that of any (density-2) pseudoknotted structure containing the input structure. Iterative HFold leverages strengths of earlier methods, namely the fast running time of HFold, a method that is based on the hierarchical folding hypothesis, and the energy parameters of HotKnots V2.0. Our experimental evaluation on a large data set shows that Iterative HFold is robust with respect to partial information, with average accuracy on pseudoknotted structures steadily increasing from roughly 54% to 79% as the user provides up to 40% of the input structure. Iterative HFold is much faster than HotKnots V2.0, while having comparable accuracy. Iterative HFold also has significantly better accuracy than IPknot on our HK-PK and IP-pk168 data sets. Conclusions Iterative HFold is a robust method for prediction of pseudoknotted RNA secondary structures, whose accuracy with more than 5% information about true pseudoknot-free structures is better than that of IPknot, and with about 35% information about true pseudoknot-free structures compares well with that of HotKnots V2.0 while being significantly faster. Iterative HFold and all data used in this work are freely available at http://www.cs.ubc.ca/~hjabbari/software.php. PMID:24884954

Spray combustion experiments and numerical predictions

NASA Technical Reports Server (NTRS)

Mularz, Edward J.; Bulzan, Daniel L.; Chen, Kuo-Huey

1993-01-01

The next generation of commercial aircraft will include turbofan engines with performance significantly better than those in the current fleet. Control of particulate and gaseous emissions will also be an integral part of the engine design criteria. These performance and emission requirements present a technical challenge for the combustor: control of the fuel and air mixing and control of the local stoichiometry will have to be maintained much more rigorously than with combustors in current production. A better understanding of the flow physics of liquid fuel spray combustion is necessary. This paper describes recent experiments on spray combustion where detailed measurements of the spray characteristics were made, including local drop-size distributions and velocities. Also, an advanced combustor CFD code has been under development and predictions from this code are compared with experimental results. Studies such as these will provide information to the advanced combustor designer on fuel spray quality and mixing effectiveness. Validation of new fast, robust, and efficient CFD codes will also enable the combustor designer to use them as additional design tools for optimization of combustor concepts for the next generation of aircraft engines.

The effect of paternal age on offspring intelligence and personality when controlling for paternal trait level.

PubMed

Arslan, Ruben C; Penke, Lars; Johnson, Wendy; Iacono, William G; McGue, Matt

2014-01-01

Paternal age at conception has been found to predict the number of new genetic mutations. We examined the effect of father's age at birth on offspring intelligence, head circumference and personality traits. Using the Minnesota Twin Family Study sample we tested paternal age effects while controlling for parents' trait levels measured with the same precision as offspring's. From evolutionary genetic considerations we predicted a negative effect of paternal age on offspring intelligence, but not on other traits. Controlling for parental intelligence (IQ) had the effect of turning an initially positive association non-significantly negative. We found paternal age effects on offspring IQ and Multidimensional Personality Questionnaire Absorption, but they were not robustly significant, nor replicable with additional covariates. No other noteworthy effects were found. Parents' intelligence and personality correlated with their ages at twin birth, which may have obscured a small negative effect of advanced paternal age (<1% of variance explained) on intelligence. We discuss future avenues for studies of paternal age effects and suggest that stronger research designs are needed to rule out confounding factors involving birth order and the Flynn effect.

Predicting Stroop Effect from Spontaneous Neuronal Activity: A Study of Regional Homogeneity

PubMed Central

Liu, Congcong; Chen, Zhencai; Wang, Ting; Tang, Dandan; Hitchman, Glenn; Sun, Jiangzhou; Zhao, Xiaoyue; Wang, Lijun; Chen, Antao

2015-01-01

The Stroop effect is one of the most robust and well-studied phenomena in cognitive psychology and cognitive neuroscience. However, little is known about the relationship between intrinsic brain activity and the individual differences of this effect. In the present study, we explored this issue by examining whether resting-state functional magnetic resonance imaging (rs-fMRI) signals could predict individual differences in the Stroop effect of healthy individuals. A partial correlation analysis was calculated to examine the relationship between regional homogeneity (ReHo) and Stroop effect size, while controlling for age, sex, and framewise displacement (FD). The results showed positive correlations in the left inferior frontal gyrus (LIFG), the left insula, the ventral anterior cingulate cortex (vACC), and the medial frontal gyrus (MFG), and negative correlation in the left precentral gyrus (LPG). These results indicate the possible influences of the LIFG, the left insula, and the LPG on the efficiency of cognitive control, and demonstrate that the key nodes of default mode network (DMN) may be important in goal-directed behavior and/or mental effort during cognitive control tasks. PMID:25938442

A dynamic feedforward neural network based on gaussian particle swarm optimization and its application for predictive control.

PubMed

Han, Min; Fan, Jianchao; Wang, Jun

2011-09-01

A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays.

An Imbalance of Approach and Effortful Control Predicts Externalizing Problems: Support for Extending the Dual-Systems Model into Early Childhood.

PubMed

Jonas, Katherine; Kochanska, Grazyna

2018-01-25

Although the association between deficits in effortful control and later externalizing behavior is well established, many researchers (Nigg Journal of Child Psychology and Psychiatry, 47(3-4), 395-422, 2006; Steinberg Developmental Review, 28(1), 78-106, 2008) have hypothesized this association is actually the product of the imbalance of dual systems, or two underlying traits: approach and self-regulation. Very little research, however, has deployed a statistically robust strategy to examine that compelling model; further, no research has done so using behavioral measures, particularly in longitudinal studies. We examined the imbalance of approach and self-regulation (effortful control, EC) as predicting externalizing problems. Latent trait models of approach and EC were derived from behavioral measures collected from 102 children in a community sample at 25, 38, 52, and 67 months (2 to 5 ½ years), and used to predict externalizing behaviors, modeled as a latent trait derived from parent-reported measures at 80, 100, 123, and 147 months (6 ½ to 12 years). The imbalance hypothesis was supported: Children with an imbalance of approach and EC had more externalizing behavior problems in middle childhood and early preadolescence, relative to children with equal levels of the two traits.

Robust stabilization of the Space Station in the presence of inertia matrix uncertainty

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang; Sunkel, John

1993-01-01

This paper presents a robust H-infinity full-state feedback control synthesis method for uncertain systems with D11 not equal to 0. The method is applied to the robust stabilization problem of the Space Station in the face of inertia matrix uncertainty. The control design objective is to find a robust controller that yields the largest stable hypercube in uncertain parameter space, while satisfying the nominal performance requirements. The significance of employing an uncertain plant model with D11 not equal 0 is demonstrated.

A plasma rotation control scheme for NSTX and NSTX-U

NASA Astrophysics Data System (ADS)

Goumiri, Imene

2016-10-01

Plasma rotation has been proven to play a key role in stabilizing large scale instabilities and improving plasma confinement by suppressing micro-turbulence. A model-based feedback system which controls the plasma rotation profile on the National Spherical Torus Experiment (NSTX) and its upgrade (NSTX-U) is presented. The first part of this work uses experimental measurements from NSTX as a starting point and models the control of plasma rotation using two different types of actuation: momentum from injected neutral beams and neoclassical toroidal viscosity generated by three-dimensional applied magnetic fields. Whether based on the data-driven model for NSTX or purely predictive modeling for NSTX-U, a reduced order model based feedback controller was designed. Predictive simulations using the TRANSP plasma transport code with the actuator input determined by the controller (controller-in-the-loop) show that the controller drives the plasma's rotation to the desired profiles in less than 100 ms given practical constraints on the actuators and the available real-time rotation measurements. This is the first time that TRANSP has been used as a plasma in simulator in a closed feedback loop test. Another approach to control simultaneously the toroidal rotation profile as well as βN is then shown for NSTX-U. For this case, the neutral beams (actuators) have been augmented in the modeling to match the upgrade version which spread the injection throughout the edge of the plasma. Control robustness in stability and performance has then been tested and used to predict the limits of the resulting controllers when the energy confinement time (τE) and the momentum diffusivity coefficient (χϕ) vary.

Robust Constrained Optimization Approach to Control Design for International Space Station Centrifuge Rotor Auto Balancing Control System

NASA Technical Reports Server (NTRS)

Postma, Barry Dirk

2005-01-01

This thesis discusses application of a robust constrained optimization approach to control design to develop an Auto Balancing Controller (ABC) for a centrifuge rotor to be implemented on the International Space Station. The design goal is to minimize a performance objective of the system, while guaranteeing stability and proper performance for a range of uncertain plants. The Performance objective is to minimize the translational response of the centrifuge rotor due to a fixed worst-case rotor imbalance. The robustness constraints are posed with respect to parametric uncertainty in the plant. The proposed approach to control design allows for both of these objectives to be handled within the framework of constrained optimization. The resulting controller achieves acceptable performance and robustness characteristics.

Functional Manipulation of Root Endophyte Populations for Feedstock Improvement- Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dangl, Jeffery L.

This study provides a systemic analysis of the influence of the abiotic environment on the assembly of plant microbiomes. We show that under controlled conditions, community assembly cues are robust and predictable across multiple abiotic gradients. Plant colonization patterns are largely driven by phylogeny, and colonization phenotypes are ubiquitous across different specimens of the same phylogenetic class. Subsets of the full synthetic community were shown to induce different root morphologies, and the morphology observed with the full community is an outcome of epistasis between two functional guilds.

A dynamic method to forecast the wheel slip for antilock braking system and its experimental evaluation.

PubMed

Oniz, Yesim; Kayacan, Erdal; Kaynak, Okyay

2009-04-01

The control of an antilock braking system (ABS) is a difficult problem due to its strongly nonlinear and uncertain characteristics. To overcome this difficulty, the integration of gray-system theory and sliding-mode control is proposed in this paper. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. The control algorithm is derived and subsequently tested on a quarter vehicle model. Encouraged by the simulation results indicating the ability to overcome the stated difficulties with fast convergence, experimental results are carried out on a laboratory setup. The results presented indicate the potential of the approach in handling difficult real-time control problems.

Robust human body model injury prediction in simulated side impact crashes.

PubMed

Golman, Adam J; Danelson, Kerry A; Stitzel, Joel D

2016-01-01

This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.

Vehicle lateral motion regulation under unreliable communication links based on robust H∞ output-feedback control schema

NASA Astrophysics Data System (ADS)

Li, Cong; Jing, Hui; Wang, Rongrong; Chen, Nan

2018-05-01

This paper presents a robust control schema for vehicle lateral motion regulation under unreliable communication links via controller area network (CAN). The communication links between the system plant and the controller are assumed to be imperfect and therefore the data packet dropouts occur frequently. The paper takes the form of parallel distributed compensation and treats the dropouts as random binary numbers that form Bernoulli distribution. Both of the tire cornering stiffness uncertainty and external disturbances are considered to enhance the robustness of the controller. In addition, a robust H∞ static output-feedback control approach is proposed to realize the lateral motion control with relative low cost sensors. The stochastic stability of the closed-loop system and conservation of the guaranteed H∞ performance are investigated. Simulation results based on CarSim platform using a high-fidelity and full-car model verify the effectiveness of the proposed control approach.

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space

NASA Astrophysics Data System (ADS)

Hong, S.-M.; Jung, B.-H.; Ruan, D.

2011-03-01

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively low observation rate. Sensitivity analysis indicates its robustness toward the choice of parameters. Its simplicity, robustness and low computation cost makes the proposed local dynamic model an attractive tool for real-time prediction with system latencies below 0.4 s.

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space.

PubMed

Hong, S-M; Jung, B-H; Ruan, D

2011-03-21

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively low observation rate. Sensitivity analysis indicates its robustness toward the choice of parameters. Its simplicity, robustness and low computation cost makes the proposed local dynamic model an attractive tool for real-time prediction with system latencies below 0.4 s.

Measurement of pH in whole blood by near-infrared spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alam, M. Kathleen; Maynard, John D.; Robinson, M. Ries

1999-03-01

Whole blood pH has been determined {ital in vitro} by using near-infrared spectroscopy over the wavelength range of 1500 to 1785 nm with multivariate calibration modeling of the spectral data obtained from two different sample sets. In the first sample set, the pH of whole blood was varied without controlling cell size and oxygen saturation (O{sub 2} Sat) variation. The result was that the red blood cell (RBC) size and O{sub 2} Sat correlated with pH. Although the partial least-squares (PLS) multivariate calibration of these data produced a good pH prediction cross-validation standard error of prediction (CVSEP)=0.046, R{sup 2}=0.982, themore » spectral data were dominated by scattering changes due to changing RBC size that correlated with the pH changes. A second experiment was carried out where the RBC size and O{sub 2} Sat were varied orthogonally to the pH variation. A PLS calibration of the spectral data obtained from these samples produced a pH prediction with an R{sup 2} of 0.954 and a cross-validated standard error of prediction of 0.064 pH units. The robustness of the PLS calibration models was tested by predicting the data obtained from the other sets. The predicted pH values obtained from both data sets yielded R{sup 2} values greater than 0.9 once the data were corrected for differences in hemoglobin concentration. For example, with the use of the calibration produced from the second sample set, the pH values from the first sample set were predicted with an R{sup 2} of 0.92 after the predictions were corrected for bias and slope. It is shown that spectral information specific to pH-induced chemical changes in the hemoglobin molecule is contained within the PLS loading vectors developed for both the first and second data sets. It is this pH specific information that allows the spectra dominated by pH-correlated scattering changes to provide robust pH predictive ability in the uncorrelated data, and visa versa. {copyright} {ital 1999} {ital Society for Applied Spectroscopy}« less

Pilot-in-the-Loop Analysis of Propulsive-Only Flight Control Systems

NASA Technical Reports Server (NTRS)

Chou, Hwei-Lan; Biezad, Daniel J.

1996-01-01

Longitudinal control system architectures are presented which directly couple flight stick motions to throttle commands for a multi-engine aircraft. This coupling enables positive attitude control with complete failure of the flight control system. The architectures chosen vary from simple feedback gains to classical lead-lag compensators with and without prefilters. Each architecture is reviewed for its appropriateness for piloted flight. The control systems are then analyzed with pilot-in-the-loop metrics related to bandwidth required for landing. Results indicate that current and proposed bandwidth requirements should be modified for throttles only flight control. Pilot ratings consistently showed better ratings than predicted by analysis. Recommendations are made for more robust design and implementation. The use of Quantitative Feedback Theory for compensator design is discussed. Although simple and effective augmented control can be achieved in a wide variety of failed configurations, a few configuration characteristics are dominant for pilot-in-the-loop control. These characteristics will be tested in a simulator study involving failed flight controls for a multi-engine aircraft.

Robust H∞ output-feedback control for path following of autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Hu, Chuan; Jing, Hui; Wang, Rongrong; Yan, Fengjun; Chadli, Mohammed

2016-03-01

This paper presents a robust H∞ output-feedback control strategy for the path following of autonomous ground vehicles (AGVs). Considering the vehicle lateral velocity is usually hard to measure with low cost sensor, a robust H∞ static output-feedback controller based on the mixed genetic algorithms (GA)/linear matrix inequality (LMI) approach is proposed to realize the path following without the information of the lateral velocity. The proposed controller is robust to the parametric uncertainties and external disturbances, with the parameters including the tire cornering stiffness, vehicle longitudinal velocity, yaw rate and road curvature. Simulation results based on CarSim-Simulink joint platform using a high-fidelity and full-car model have verified the effectiveness of the proposed control approach.

Scaling for Robust Empirical Modeling and Predictions of Net Ecosystem Exchange (NEE) from Diverse Wetland Ecosystems

NASA Astrophysics Data System (ADS)

Ishtiaq, K. S.; Abdul-Aziz, O. I.

2014-12-01

We developed a scaling-based, simple empirical model for spatio-temporally robust prediction of the diurnal cycles of wetland net ecosystem exchange (NEE) by using an extended stochastic harmonic algorithm (ESHA). A reference-time observation from each diurnal cycle was utilized as the scaling parameter to normalize and collapse hourly observed NEE of different days into a single, dimensionless diurnal curve. The modeling concept was tested by parameterizing the unique diurnal curve and predicting hourly NEE of May to October (summer growing and fall seasons) between 2002-12 for diverse wetland ecosystems, as available in the U.S. AmeriFLUX network. As an example, the Taylor Slough short hydroperiod marsh site in the Florida Everglades had data for four consecutive growing seasons from 2009-12; results showed impressive modeling efficiency (coefficient of determination, R2 = 0.66) and accuracy (ratio of root-mean-square-error to the standard deviation of observations, RSR = 0.58). Model validation was performed with an independent year of NEE data, indicating equally impressive performance (R2 = 0.68, RSR = 0.57). The model included a parsimonious set of estimated parameters, which exhibited spatio-temporal robustness by collapsing onto narrow ranges. Model robustness was further investigated by analytically deriving and quantifying parameter sensitivity coefficients and a first-order uncertainty measure. The relatively robust, empirical NEE model can be applied for simulating continuous (e.g., hourly) NEE time-series from a single reference observation (or a set of limited observations) at different wetland sites of comparable hydro-climatology, biogeochemistry, and ecology. The method can also be used for a robust gap-filling of missing data in observed time-series of periodic ecohydrological variables for wetland or other ecosystems.

A new look at the robust control of discrete-time Markov jump linear systems

NASA Astrophysics Data System (ADS)

Todorov, M. G.; Fragoso, M. D.

2016-03-01

In this paper, we make a foray in the role played by a set of four operators on the study of robust H2 and mixed H2/H∞ control problems for discrete-time Markov jump linear systems. These operators appear in the study of mean square stability for this class of systems. By means of new linear matrix inequality (LMI) characterisations of controllers, which include slack variables that, to some extent, separate the robustness and performance objectives, we introduce four alternative approaches to the design of controllers which are robustly stabilising and at the same time provide a guaranteed level of H2 performance. Since each operator provides a different degree of conservatism, the results are unified in the form of an iterative LMI technique for designing robust H2 controllers, whose convergence is attained in a finite number of steps. The method yields a new way of computing mixed H2/H∞ controllers, whose conservatism decreases with iteration. Two numerical examples illustrate the applicability of the proposed results for the control of a small unmanned aerial vehicle, and for an underactuated robotic arm.

Robust control for a biaxial servo with time delay system based on adaptive tuning technique.

PubMed

Chen, Tien-Chi; Yu, Chih-Hsien

2009-07-01

A robust control method for synchronizing a biaxial servo system motion is proposed in this paper. A new network based cross-coupled control and adaptive tuning techniques are used together to cancel out the skew error. The conventional fixed gain PID cross-coupled controller (CCC) is replaced with the adaptive cross-coupled controller (ACCC) in the proposed control scheme to maintain biaxial servo system synchronization motion. Adaptive-tuning PID (APID) position and velocity controllers provide the necessary control actions to maintain synchronization while following a variable command trajectory. A delay-time compensator (DTC) with an adaptive controller was augmented to set the time delay element, effectively moving it outside the closed loop, enhancing the stability of the robust controlled system. This scheme provides strong robustness with respect to uncertain dynamics and disturbances. The simulation and experimental results reveal that the proposed control structure adapts to a wide range of operating conditions and provides promising results under parameter variations and load changes.

Synthesis Methods for Robust Passification and Control

NASA Technical Reports Server (NTRS)

Kelkar, Atul G.; Joshi, Suresh M. (Technical Monitor)

2000-01-01

The research effort under this cooperative agreement has been essentially the continuation of the work from previous grants. The ongoing work has primarily focused on developing passivity-based control techniques for Linear Time-Invariant (LTI) systems. During this period, there has been a significant progress made in the area of passivity-based control of LTI systems and some preliminary results have also been obtained for nonlinear systems, as well. The prior work has addressed optimal control design for inherently passive as well as non- passive linear systems. For exploiting the robustness characteristics of passivity-based controllers the passification methodology was developed for LTI systems that are not inherently passive. Various methods of passification were first proposed in and further developed. The robustness of passification was addressed for multi-input multi-output (MIMO) systems for certain classes of uncertainties using frequency-domain methods. For MIMO systems, a state-space approach using Linear Matrix Inequality (LMI)-based formulation was presented, for passification of non-passive LTI systems. An LMI-based robust passification technique was presented for systems with redundant actuators and sensors. The redundancy in actuators and sensors was used effectively for robust passification using the LMI formulation. The passification was designed to be robust to an interval-type uncertainties in system parameters. The passification techniques were used to design a robust controller for Benchmark Active Control Technology wing under parametric uncertainties. The results on passive nonlinear systems, however, are very limited to date. Our recent work in this area was presented, wherein some stability results were obtained for passive nonlinear systems that are affine in control.

Robust and real-time control of magnetic bearings for space engines

NASA Technical Reports Server (NTRS)

Sinha, Alok; Wang, Kon-Well; Mease, K.; Lewis, S.

1991-01-01

Currently, NASA Lewis Research Center is developing magnetic bearings for Space Shuttle Main Engine (SSME) turbopumps. The control algorithms which have been used are based on either the proportional-intergral-derivative control (PID) approach or the linear quadratic (LQ) state space approach. These approaches lead to an acceptable performance only when the system model is accurately known, which is seldom true in practice. For example, the rotor eccentricity, which is a major source of vibration at high speeds, cannot be predicted accurately. Furthermore, the dynamics of a rotor shaft, which must be treated as a flexible system to model the elastic rotor shaft, is infinite dimensional in theory and the controller can only be developed on the basis of a finite number of modes. Therefore, the development of the control system is further complicated by the possibility of closed loop system instability because of residual or uncontrolled modes, the so called spillover problem. Consequently, novel control algorithms for magnetic bearings are being developed to be robust to inevitable parametric uncertainties, external disturbances, spillover phenomenon and noise. Also, as pointed out earlier, magnetic bearings must exhibit good performance at a speed over 30,000 rpm. This implies that the sampling period available for the design of a digital control system has to be of the order of 0.5 milli-seconds. Therefore, feedback coefficients and other required controller parameters have to be computed off-line so that the on-line computational burden is extremely small. The development of the robust and real-time control algorithms is based on the sliding mode control theory. In this method, a dynamic system is made to move along a manifold of sliding hyperplanes to the origin of the state space. The number of sliding hyperplanes equals that of actuators. The sliding mode controller has two parts; linear state feedback and nonlinear terms. The nonlinear terms guarantee that the systems would reach the intersection of all sliding hyperplanes and remain on it when bounds on the errors in the system parameters and external disturbances are known. The linear part of the control drives the system to the origin of state space. Another important feature is that the controller parameter can be computed off-line. Consequently, on-line computational burden is small.

Research in robust control for hypersonic aircraft

NASA Technical Reports Server (NTRS)

Calise, A. J.

1993-01-01

The research during the second reporting period has focused on robust control design for hypersonic vehicles. An already existing design for the Hypersonic Winged-Cone Configuration has been enhanced. Uncertainty models for the effects of propulsion system perturbations due to angle of attack variations, structural vibrations, and uncertainty in control effectiveness were developed. Using H(sub infinity) and mu-synthesis techniques, various control designs were performed in order to investigate the impact of these effects on achievable robust performance.

Predicting the Intention to Use Condoms and Actual Condom Use Behaviour: A Three-Wave Longitudinal Study in Ghana

PubMed Central

Teye-Kwadjo, Enoch; Kagee, Ashraf; Swart, Hermann

2017-01-01

Background Growing cross-sectional research shows that the theory of planned behaviour (TPB) is robust in predicting intentions to use condoms and condom use behaviour. Yet, little is known about the TPB’s utility in explaining intentions to use condoms and condom use behaviour over time. Methods This study used a longitudinal design and latent variable structural equation modelling to test the longitudinal relationships postulated by the TPB. School-going youths in Ghana provided data on attitudes, subjective norms, perceived control, intentions, and behaviour regarding condom use at three-time points, spaced approximately three-months apart. Results As predicted by the TPB, the results showed that attitudes were significantly positively associated with intentions to use condoms over time. Contrary to the TPB, subjective norms were not significantly associated with intentions to use condoms over time. Perceived control did not predict intentions to use condoms over time. Moreover, intentions to use condoms were not significantly associated with self-reported condom use over time. Conclusion These results suggest that school-going youths in Ghana may benefit from sex education programmes that focus on within-subject attitude formation and activation. The theoretical and methodological implications of these results are discussed. PMID:27925435

Preserving privacy whilst maintaining robust epidemiological predictions.

PubMed

Werkman, Marleen; Tildesley, Michael J; Brooks-Pollock, Ellen; Keeling, Matt J

2016-12-01

Mathematical models are invaluable tools for quantifying potential epidemics and devising optimal control strategies in case of an outbreak. State-of-the-art models increasingly require detailed individual farm-based and sensitive data, which may not be available due to either lack of capacity for data collection or privacy concerns. However, in many situations, aggregated data are available for use. In this study, we systematically investigate the accuracy of predictions made by mathematical models initialised with varying data aggregations, using the UK 2001 Foot-and-Mouth Disease Epidemic as a case study. We consider the scenario when the only data available are aggregated into spatial grid cells, and develop a metapopulation model where individual farms in a single subpopulation are assumed to behave uniformly and transmit randomly. We also adapt this standard metapopulation model to capture heterogeneity in farm size and composition, using farm census data. Our results show that homogeneous models based on aggregated data overestimate final epidemic size but can perform well for predicting spatial spread. Recognising heterogeneity in farm sizes improves predictions of the final epidemic size, identifying risk areas, determining the likelihood of epidemic take-off and identifying the optimal control strategy. In conclusion, in cases where individual farm-based data are not available, models can still generate meaningful predictions, although care must be taken in their interpretation and use. Copyright © 2016. Published by Elsevier B.V.

Robust Covariate-Adjusted Log-Rank Statistics and Corresponding Sample Size Formula for Recurrent Events Data

PubMed Central

Song, Rui; Kosorok, Michael R.; Cai, Jianwen

2009-01-01

Summary Recurrent events data are frequently encountered in clinical trials. This article develops robust covariate-adjusted log-rank statistics applied to recurrent events data with arbitrary numbers of events under independent censoring and the corresponding sample size formula. The proposed log-rank tests are robust with respect to different data-generating processes and are adjusted for predictive covariates. It reduces to the Kong and Slud (1997, Biometrika 84, 847–862) setting in the case of a single event. The sample size formula is derived based on the asymptotic normality of the covariate-adjusted log-rank statistics under certain local alternatives and a working model for baseline covariates in the recurrent event data context. When the effect size is small and the baseline covariates do not contain significant information about event times, it reduces to the same form as that of Schoenfeld (1983, Biometrics 39, 499–503) for cases of a single event or independent event times within a subject. We carry out simulations to study the control of type I error and the comparison of powers between several methods in finite samples. The proposed sample size formula is illustrated using data from an rhDNase study. PMID:18162107

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sarovar, Mohan; Zhang, Jun; Zeng, Lishan

Analog quantum simulators (AQS) will likely be the first nontrivial application of quantum technology for predictive simulation. However, there remain questions regarding the degree of confidence that can be placed in the results of AQS since they do not naturally incorporate error correction. Specifically, how do we know whether an analog simulation of a quantum model will produce predictions that agree with the ideal model in the presence of inevitable imperfections? At the same time there is a widely held expectation that certain quantum simulation questions will be robust to errors and perturbations in the underlying hardware. Resolving these twomore » points of view is a critical step in making the most of this promising technology. In this paper we formalize the notion of AQS reliability by determining sensitivity of AQS outputs to underlying parameters, and formulate conditions for robust simulation. Our approach naturally reveals the importance of model symmetries in dictating the robust properties. Finally, to demonstrate the approach, we characterize the robust features of a variety of quantum many-body models.« less

Characterizing metabolic pathway diversification in the context of perturbation size.

PubMed

Yang, Laurence; Srinivasan, Shyamsundhar; Mahadevan, Radhakrishnan; Cluett, William R

2015-03-01

Cell metabolism is an important platform for sustainable biofuel, chemical and pharmaceutical production but its complexity presents a major challenge for scientists and engineers. Although in silico strains have been designed in the past with predicted performances near the theoretical maximum, real-world performance is often sub-optimal. Here, we simulate how strain performance is impacted when subjected to many randomly varying perturbations, including discrepancies between gene expression and in vivo flux, osmotic stress, and substrate uptake perturbations due to concentration gradients in bioreactors. This computational study asks whether robust performance can be achieved by adopting robustness-enhancing mechanisms from naturally evolved organisms-in particular, redundancy. Our study shows that redundancy, typically perceived as a ubiquitous robustness-enhancing strategy in nature, can either improve or undermine robustness depending on the magnitude of the perturbations. We also show that the optimal number of redundant pathways used can be predicted for a given perturbation size. Copyright © 2015. Published by Elsevier Inc.

Reliability of analog quantum simulation

DOE PAGES

Sarovar, Mohan; Zhang, Jun; Zeng, Lishan

2017-01-03

Analog quantum simulators (AQS) will likely be the first nontrivial application of quantum technology for predictive simulation. However, there remain questions regarding the degree of confidence that can be placed in the results of AQS since they do not naturally incorporate error correction. Specifically, how do we know whether an analog simulation of a quantum model will produce predictions that agree with the ideal model in the presence of inevitable imperfections? At the same time there is a widely held expectation that certain quantum simulation questions will be robust to errors and perturbations in the underlying hardware. Resolving these twomore » points of view is a critical step in making the most of this promising technology. In this paper we formalize the notion of AQS reliability by determining sensitivity of AQS outputs to underlying parameters, and formulate conditions for robust simulation. Our approach naturally reveals the importance of model symmetries in dictating the robust properties. Finally, to demonstrate the approach, we characterize the robust features of a variety of quantum many-body models.« less

Practice and play as determinants of self-determined motivation in youth soccer players.

PubMed

Hendry, David T; Crocker, Peter R E; Hodges, Nicola J

2014-01-01

Based upon predictions derived from the Developmental Model of Sports Participation, we tested whether hours in domain-specific play (self-led activities) and practice (coach-led activities) during childhood (~5-12 year) in an elite group of youth soccer players from the UK (N = 144) were related to motivation. Independent analysis of three different age groups (Under 13, 15 and 17 year) did not show relations between play and practice activities during childhood and global measures of motivation. However, secondary analysis showed that when controlling for years in soccer, years in the UK Academy system were negatively related to global indices of self-determined motivation (SDI) and positively related to controlled motivation for the oldest players. Despite predictions, there was no evidence that play during childhood was positively related to more SDI. Prospective research is recommended to enable more robust conclusions about the role of early developmental practice activities, especially early specialisation in a high-performance system, on both skill and psychosocial development.

Integral control of plant gravitropism through the interplay of hormone signaling and gene regulation.

PubMed

Rodrigo, Guillermo; Jaramillo, Alfonso; Blázquez, Miguel A

2011-08-17

The interplay between hormone signaling and gene regulatory networks is instrumental in promoting the development of living organisms. In particular, plants have evolved mechanisms to sense gravity and orient themselves accordingly. Here, we present a mathematical model that reproduces plant gravitropic responses based on known molecular genetic interactions for auxin signaling coupled with a physical description of plant reorientation. The model allows one to analyze the spatiotemporal dynamics of the system, triggered by an auxin gradient that induces differential growth of the plant with respect to the gravity vector. Our model predicts two important features with strong biological implications: 1), robustness of the regulatory circuit as a consequence of integral control; and 2), a higher degree of plasticity generated by the molecular interplay between two classes of hormones. Our model also predicts the ability of gibberellins to modulate the tropic response and supports the integration of the hormonal role at the level of gene regulation. Copyright © 2011 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Dose-specific efficacy of Haemophilus influenzae type b conjugate vaccines: a systematic review and meta-analysis of controlled clinical trials

PubMed Central

GRIFFITHS, U. K.; CLARK, A.; GESSNER, B.; MINERS, A.; SANDERSON, C.; SEDYANINGSIH, E. R.; MULHOLLAND, K. E.

2012-01-01

SUMMARY Global coverage of infant Haemophilus influenzae type b (Hib) vaccination has increased considerably during the past decade, partly due to GAVI Alliance donations of the vaccine to low-income countries. In settings where large numbers of children receive only one or two vaccine doses rather than the recommended three doses, dose-specific efficacy estimates are needed to predict impact. The objective of this meta-analysis is to determine Hib vaccine efficacy against different clinical outcomes after receiving one, two or three doses of vaccine. Studies were eligible for inclusion if a prospective, controlled design had been used to evaluate commercially available Hib conjugate vaccines. Eight studies were included. Pooled vaccine efficacies against invasive Hib disease after one, two or three doses of vaccine were 59%, 92% and 93%, respectively. The meta-analysis provides robust estimates for use in decision-analytical models designed to predict the impact of Hib vaccine. PMID:22583474

SU-F-R-44: Modeling Lung SBRT Tumor Response Using Bayesian Network Averaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Diamant, A; Ybarra, N; Seuntjens, J

2016-06-15

Purpose: The prediction of tumor control after a patient receives lung SBRT (stereotactic body radiation therapy) has proven to be challenging, due to the complex interactions between an individual’s biology and dose-volume metrics. Many of these variables have predictive power when combined, a feature that we exploit using a graph modeling approach based on Bayesian networks. This provides a probabilistic framework that allows for accurate and visually intuitive predictive modeling. The aim of this study is to uncover possible interactions between an individual patient’s characteristics and generate a robust model capable of predicting said patient’s treatment outcome. Methods: We investigatedmore » a cohort of 32 prospective patients from multiple institutions whom had received curative SBRT to the lung. The number of patients exhibiting tumor failure was observed to be 7 (event rate of 22%). The serum concentration of 5 biomarkers previously associated with NSCLC (non-small cell lung cancer) was measured pre-treatment. A total of 21 variables were analyzed including: dose-volume metrics with BED (biologically effective dose) correction and clinical variables. A Markov Chain Monte Carlo technique estimated the posterior probability distribution of the potential graphical structures. The probability of tumor failure was then estimated by averaging the top 100 graphs and applying Baye’s rule. Results: The optimal Bayesian model generated throughout this study incorporated the PTV volume, the serum concentration of the biomarker EGFR (epidermal growth factor receptor) and prescription BED. This predictive model recorded an area under the receiver operating characteristic curve of 0.94(1), providing better performance compared to competing methods in other literature. Conclusion: The use of biomarkers in conjunction with dose-volume metrics allows for the generation of a robust predictive model. The preliminary results of this report demonstrate that it is possible to accurately model the prognosis of an individual lung SBRT patient’s treatment.« less

pKa prediction of monoprotic small molecules the SMARTS way.

PubMed

Lee, Adam C; Yu, Jing-Yu; Crippen, Gordon M

2008-10-01

Realizing favorable absorption, distribution, metabolism, elimination, and toxicity profiles is a necessity due to the high attrition rate of lead compounds in drug development today. The ability to accurately predict bioavailability can help save time and money during the screening and optimization processes. As several robust programs already exist for predicting logP, we have turned our attention to the fast and robust prediction of pK(a) for small molecules. Using curated data from the Beilstein Database and Lange's Handbook of Chemistry, we have created a decision tree based on a novel set of SMARTS strings that can accurately predict the pK(a) for monoprotic compounds with R(2) of 0.94 and root mean squared error of 0.68. Leave-some-out (10%) cross-validation achieved Q(2) of 0.91 and root mean squared error of 0.80.

Flight Control Laws for NASA's Hyper-X Research Vehicle

NASA Technical Reports Server (NTRS)

Davidson, J.; Lallman, F.; McMinn, J. D.; Martin, J.; Pahle, J.; Stephenson, M.; Selmon, J.; Bose, D.

1999-01-01

The goal of the Hyper-X program is to demonstrate and validate technology for design and performance predictions of hypersonic aircraft with an airframe-integrated supersonic-combustion ramjet propulsion system. Accomplishing this goal requires flight demonstration of a hydrogen-fueled scramjet powered hypersonic aircraft. A key enabling technology for this flight demonstration is flight controls. Closed-loop flight control is required to enable a successful stage separation, to achieve and maintain the design condition during the engine test, and to provide a controlled descent. Before the contract award, NASA developed preliminary flight control laws for the Hyper-X to evaluate the feasibility of the proposed scramjet test sequence and descent trajectory. After the contract award, a Boeing/NASA partnership worked to develop the current control laws. This paper presents a description of the Hyper-X Research Vehicle control law architectures with performance and robustness analyses. Assessments of simulated flight trajectories and stability margin analyses demonstrate that these control laws meet the flight test requirements.

Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution.

PubMed

Slater, Graham J; Pennell, Matthew W

2014-05-01

A central prediction of much theory on adaptive radiations is that traits should evolve rapidly during the early stages of a clade's history and subsequently slowdown in rate as niches become saturated--a so-called "Early Burst." Although a common pattern in the fossil record, evidence for early bursts of trait evolution in phylogenetic comparative data has been equivocal at best. We show here that this may not necessarily be due to the absence of this pattern in nature. Rather, commonly used methods to infer its presence perform poorly when when the strength of the burst--the rate at which phenotypic evolution declines--is small, and when some morphological convergence is present within the clade. We present two modifications to existing comparative methods that allow greater power to detect early bursts in simulated datasets. First, we develop posterior predictive simulation approaches and show that they outperform maximum likelihood approaches at identifying early bursts at moderate strength. Second, we use a robust regression procedure that allows for the identification and down-weighting of convergent taxa, leading to moderate increases in method performance. We demonstrate the utility and power of these approach by investigating the evolution of body size in cetaceans. Model fitting using maximum likelihood is equivocal with regards the mode of cetacean body size evolution. However, posterior predictive simulation combined with a robust node height test return low support for Brownian motion or rate shift models, but not the early burst model. While the jury is still out on whether early bursts are actually common in nature, our approach will hopefully facilitate more robust testing of this hypothesis. We advocate the adoption of similar posterior predictive approaches to improve the fit and to assess the adequacy of macroevolutionary models in general.

Robustness results in LQG based multivariable control designs

NASA Technical Reports Server (NTRS)

Lehtomaki, N. A.; Sandell, N. R., Jr.; Athans, M.

1980-01-01

The robustness of control systems with respect to model uncertainty is considered using simple frequency domain criteria. Results are derived under a common framework in which the minimum singular value of the return difference transfer matrix is the key quantity. In particular, the LQ and LQG robustness results are discussed.

Robust Temperature Control of a Thermoelectric Cooler via μ -Synthesis

NASA Astrophysics Data System (ADS)

Kürkçü, Burak; Kasnakoğlu, Coşku

2018-02-01

In this work robust temperature control of a thermoelectric cooler (TEC) via μ -synthesis is studied. An uncertain dynamical model for the TEC that is suitable for robust control methods is derived. The model captures variations in operating point due to current, load and temperature changes. A temperature controller is designed utilizing μ -synthesis, a powerful method guaranteeing robust stability and performance. For comparison two well-known control methods, namely proportional-integral-derivative (PID) and internal model control (IMC), are also realized to benchmark the proposed approach. It is observed that the stability and performance on the nominal model are satisfactory for all cases. On the other hand, under perturbations the responses of PID and IMC deteriorate and even become unstable. In contrast, the μ -synthesis controller succeeds in keeping system stability and achieving good performance under all perturbations within the operating range, while at the same time providing good disturbance rejection.

Robust Stabilization Control Based on Guardian Maps Theory for a Longitudinal Model of Hypersonic Vehicle

PubMed Central

Liu, Mengying; Sun, Peihua

2014-01-01

A typical model of hypersonic vehicle has the complicated dynamics such as the unstable states, the nonminimum phases, and the strong coupling input-output relations. As a result, designing a robust stabilization controller is essential to implement the anticipated tasks. This paper presents a robust stabilization controller based on the guardian maps theory for hypersonic vehicle. First, the guardian maps theories are provided to explain the constraint relations between the open subsets of complex plane and the eigenvalues of the state matrix of closed-loop control system. Then, a general control structure in relation to the guardian maps theories is proposed to achieve the respected design demands. Furthermore, the robust stabilization control law depending on the given general control structure is designed for the longitudinal model of hypersonic vehicle. Finally, a simulation example is provided to verify the effectiveness of the proposed methods. PMID:24795535

Fractional Control of An Active Four-wheel-steering Vehicle

NASA Astrophysics Data System (ADS)

Wang, Tianting; Tong, Jun; Chen, Ning; Tian, Jie

2018-03-01

A four-wheel-steering (4WS) vehicle model and reference model with a drop filter are constructed. The decoupling of 4WS vehicle model is carried out. And a fractional PIλDμ controller is introduced into the decoupling strategy to reduce the effects of the uncertainty of the vehicle parameters as well as the unmodelled dynamics on the system performance. Based on optimization techniques, the design of fractional controller are obtained to ensure the robustness of 4WS vehicle during the special range of frequencies through proper choice of the constraints. In order to compare with fractional robust controller, an optimal controller for the same vehicle is also designed. The simulations of the two control systems are carried out and it reveals that the decoupling and fractional robust controller is able to make vehicle model trace the reference model very well with better robustness.

Robust stabilization control based on guardian maps theory for a longitudinal model of hypersonic vehicle.

PubMed

Liu, Yanbin; Liu, Mengying; Sun, Peihua

2014-01-01

A typical model of hypersonic vehicle has the complicated dynamics such as the unstable states, the nonminimum phases, and the strong coupling input-output relations. As a result, designing a robust stabilization controller is essential to implement the anticipated tasks. This paper presents a robust stabilization controller based on the guardian maps theory for hypersonic vehicle. First, the guardian maps theories are provided to explain the constraint relations between the open subsets of complex plane and the eigenvalues of the state matrix of closed-loop control system. Then, a general control structure in relation to the guardian maps theories is proposed to achieve the respected design demands. Furthermore, the robust stabilization control law depending on the given general control structure is designed for the longitudinal model of hypersonic vehicle. Finally, a simulation example is provided to verify the effectiveness of the proposed methods.

Robust fast controller design via nonlinear fractional differential equations.

PubMed

Zhou, Xi; Wei, Yiheng; Liang, Shu; Wang, Yong

2017-07-01

A new method for linear system controller design is proposed whereby the closed-loop system achieves both robustness and fast response. The robustness performance considered here means the damping ratio of closed-loop system can keep its desired value under system parameter perturbation, while the fast response, represented by rise time of system output, can be improved by tuning the controller parameter. We exploit techniques from both the nonlinear systems control and the fractional order systems control to derive a novel nonlinear fractional order controller. For theoretical analysis of the closed-loop system performance, two comparison theorems are developed for a class of fractional differential equations. Moreover, the rise time of the closed-loop system can be estimated, which facilitates our controller design to satisfy the fast response performance and maintain the robustness. Finally, numerical examples are given to illustrate the effectiveness of our methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

An advanced robust method for speed control of switched reluctance motor

NASA Astrophysics Data System (ADS)

Zhang, Chao; Ming, Zhengfeng; Su, Zhanping; Cai, Zhuang

2018-05-01

This paper presents an advanced robust controller for the speed system of a switched reluctance motor (SRM) in the presence of nonlinearities, speed ripple, and external disturbances. It proposes that the adaptive fuzzy control is applied to regulate the motor speed in the outer loop, and the detector is used to obtain rotor detection in the inner loop. The new fuzzy logic tuning rules are achieved from the experience of the operator and the knowledge of the specialist. The fuzzy parameters are automatically adjusted online according to the error and its change of speed in the transient period. The designed detector can obtain the rotor's position accurately in each phase module. Furthermore, a series of contrastive simulations are completed between the proposed controller and proportion integration differentiation controller including low speed, medium speed, and high speed. Simulations show that the proposed robust controller enables the system reduced by at least 3% in overshoot, 6% in rise time, and 20% in setting time, respectively, and especially under external disturbances. Moreover, an actual SRM control system is constructed at 220 V 370 W. The experiment results further prove that the proposed robust controller has excellent dynamic performance and strong robustness.

A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter.

PubMed

Shabbouei Hagh, Yashar; Mohammadi Asl, Reza; Cocquempot, Vincent

2017-01-01

In this paper, a new hybrid robust fault tolerant control scheme is proposed. A robust H ∞ control law is used in non-faulty situation, while a Non-Singular Terminal Sliding Mode (NTSM) controller is activated as soon as an actuator fault is detected. Since a linear robust controller is designed, the system is first linearized through the feedback linearization method. To switch from one controller to the other, a fuzzy based switching system is used. An Adaptive Joint Unscented Kalman Filter (AJUKF) is used for fault detection and diagnosis. The proposed method is based on the simultaneous estimation of the system states and parameters. In order to show the efficiency of the proposed scheme, a simulated 3-DOF robotic manipulator is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Robust control for fractional variable-order chaotic systems with non-singular kernel

NASA Astrophysics Data System (ADS)

Zuñiga-Aguilar, C. J.; Gómez-Aguilar, J. F.; Escobar-Jiménez, R. F.; Romero-Ugalde, H. M.

2018-01-01

This paper investigates the chaos control for a class of variable-order fractional chaotic systems using robust control strategy. The variable-order fractional models of the non-autonomous biological system, the King Cobra chaotic system, the Halvorsen's attractor and the Burke-Shaw system, have been derived using the fractional-order derivative with Mittag-Leffler in the Liouville-Caputo sense. The fractional differential equations and the control law were solved using the Adams-Bashforth-Moulton algorithm. To test the control stability efficiency, different statistical indicators were introduced. Finally, simulation results demonstrate the effectiveness of the proposed robust control.

Robust control synthesis for uncertain dynamical systems

NASA Technical Reports Server (NTRS)

Byun, Kuk-Whan; Wie, Bong; Sunkel, John

1989-01-01

This paper presents robust control synthesis techniques for uncertain dynamical systems subject to structured parameter perturbation. Both QFT (quantitative feedback theory) and H-infinity control synthesis techniques are investigated. Although most H-infinity-related control techniques are not concerned with the structured parameter perturbation, a new way of incorporating the parameter uncertainty in the robust H-infinity control design is presented. A generic model of uncertain dynamical systems is used to illustrate the design methodologies investigated in this paper. It is shown that, for a certain noncolocated structural control problem, use of both techniques results in nonminimum phase compensation.

SU-F-R-31: Identification of Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease

DOE Office of Scientific and Technical Information (OSTI.GOV)

Choi, W; Riyahi, S; Lu, W

Purpose: Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (radiation pneumonitis and radiation fibrosis). For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume. Methods: The free-breathing CTs of 14 lung SBRT patients were studied. Different sizes of GTVs were simulated with spheres placed at the upper lobe and lower lobe respectively in the normal lung (contralateral to tumor). 27 texture features (9 from intensity histogram, 8 from grey-level co-occurrence matrix [GLCM] and 10 from grey-level run-length matrix [GLRM])more » were extracted from [normal lung-GTV]. To measure the variability of a feature F, the relative difference D=|Fref -Fsim|/Fref*100% was calculated, where Fref was for the entire normal lung and Fsim was for [normal lung-GTV]. A feature was considered as robust if the largest non-outlier (Q3+1.5*IQR) D was less than 5%, and considered as not correlated with normal lung volume when their Pearson correlation was lower than 0.50. Results: Only 11 features were robust. All first-order intensity-histogram features (mean, max, etc.) were robust, while most higher-order features (skewness, kurtosis, etc.) were unrobust. Only two of the GLCM and four of the GLRM features were robust. Larger GTV resulted greater feature variation, this was particularly true for unrobust features. All robust features were not correlated with normal lung volume while three unrobust features showed high correlation. Excessive variations were observed in two low grey-level run features and were later identified to be from one patient with local lung diseases (atelectasis) in the normal lung. There was no dependence on GTV location. Conclusion: We identified 11 robust normal lung CT texture features that can be further examined for the prediction of radiation-induced lung disease. Interestingly, low grey-level run features identified normal lung diseases. This work was supported in part by the National Cancer Institute Grants R01CA172638.« less

Design of permanent magnet synchronous motor speed loop controller based on sliding mode control algorithm

NASA Astrophysics Data System (ADS)

Qiang, Jiang; Meng-wei, Liao; Ming-jie, Luo

2018-03-01

Abstract.The control performance of Permanent Magnet Synchronous Motor will be affected by the fluctuation or changes of mechanical parameters when PMSM is applied as driving motor in actual electric vehicle,and external disturbance would influence control robustness.To improve control dynamic quality and robustness of PMSM speed control system, a new second order integral sliding mode control algorithm is introduced into PMSM vector control.The simulation results show that, compared with the traditional PID control,the modified control scheme optimized has better control precision and dynamic response ability and perform better with a stronger robustness facing external disturbance,it can effectively solve the traditional sliding mode variable structure control chattering problems as well.

Robust tracking and distributed synchronization control of a multi-motor servomechanism with H-infinity performance.

PubMed

Wang, Minlin; Ren, Xuemei; Chen, Qiang

2018-01-01

The multi-motor servomechanism (MMS) is a multi-variable, high coupling and nonlinear system, which makes the controller design challenging. In this paper, an adaptive robust H-infinity control scheme is proposed to achieve both the load tracking and multi-motor synchronization of MMS. This control scheme consists of two parts: a robust tracking controller and a distributed synchronization controller. The robust tracking controller is constructed by incorporating a neural network (NN) K-filter observer into the dynamic surface control, while the distributed synchronization controller is designed by combining the mean deviation coupling control strategy with the distributed technique. The proposed control scheme has several merits: 1) by using the mean deviation coupling synchronization control strategy, the tracking controller and the synchronization controller can be designed individually without any coupling problem; 2) the immeasurable states and unknown nonlinearities are handled by a NN K-filter observer, where the number of NN weights is largely reduced by using the minimal learning parameter technique; 3) the H-infinity performances of tracking error and synchronization error are guaranteed by introducing a robust term into the tracking controller and the synchronization controller, respectively. The stabilities of the tracking and synchronization control systems are analyzed by the Lyapunov theory. Simulation and experimental results based on a four-motor servomechanism are conducted to demonstrate the effectiveness of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Robust synchronization control scheme of a population of nonlinear stochastic synthetic genetic oscillators under intrinsic and extrinsic molecular noise via quorum sensing.

PubMed

Chen, Bor-Sen; Hsu, Chih-Yuan

2012-10-26

Collective rhythms of gene regulatory networks have been a subject of considerable interest for biologists and theoreticians, in particular the synchronization of dynamic cells mediated by intercellular communication. Synchronization of a population of synthetic genetic oscillators is an important design in practical applications, because such a population distributed over different host cells needs to exploit molecular phenomena simultaneously in order to emerge a biological phenomenon. However, this synchronization may be corrupted by intrinsic kinetic parameter fluctuations and extrinsic environmental molecular noise. Therefore, robust synchronization is an important design topic in nonlinear stochastic coupled synthetic genetic oscillators with intrinsic kinetic parameter fluctuations and extrinsic molecular noise. Initially, the condition for robust synchronization of synthetic genetic oscillators was derived based on Hamilton Jacobi inequality (HJI). We found that if the synchronization robustness can confer enough intrinsic robustness to tolerate intrinsic parameter fluctuation and extrinsic robustness to filter the environmental noise, then robust synchronization of coupled synthetic genetic oscillators is guaranteed. If the synchronization robustness of a population of nonlinear stochastic coupled synthetic genetic oscillators distributed over different host cells could not be maintained, then robust synchronization could be enhanced by external control input through quorum sensing molecules. In order to simplify the analysis and design of robust synchronization of nonlinear stochastic synthetic genetic oscillators, the fuzzy interpolation method was employed to interpolate several local linear stochastic coupled systems to approximate the nonlinear stochastic coupled system so that the HJI-based synchronization design problem could be replaced by a simple linear matrix inequality (LMI)-based design problem, which could be solved with the help of LMI toolbox in MATLAB easily. If the synchronization robustness criterion, i.e. the synchronization robustness ≥ intrinsic robustness + extrinsic robustness, then the stochastic coupled synthetic oscillators can be robustly synchronized in spite of intrinsic parameter fluctuation and extrinsic noise. If the synchronization robustness criterion is violated, external control scheme by adding inducer can be designed to improve synchronization robustness of coupled synthetic genetic oscillators. The investigated robust synchronization criteria and proposed external control method are useful for a population of coupled synthetic networks with emergent synchronization behavior, especially for multi-cellular, engineered networks.

Robust synchronization control scheme of a population of nonlinear stochastic synthetic genetic oscillators under intrinsic and extrinsic molecular noise via quorum sensing

PubMed Central

2012-01-01

Background Collective rhythms of gene regulatory networks have been a subject of considerable interest for biologists and theoreticians, in particular the synchronization of dynamic cells mediated by intercellular communication. Synchronization of a population of synthetic genetic oscillators is an important design in practical applications, because such a population distributed over different host cells needs to exploit molecular phenomena simultaneously in order to emerge a biological phenomenon. However, this synchronization may be corrupted by intrinsic kinetic parameter fluctuations and extrinsic environmental molecular noise. Therefore, robust synchronization is an important design topic in nonlinear stochastic coupled synthetic genetic oscillators with intrinsic kinetic parameter fluctuations and extrinsic molecular noise. Results Initially, the condition for robust synchronization of synthetic genetic oscillators was derived based on Hamilton Jacobi inequality (HJI). We found that if the synchronization robustness can confer enough intrinsic robustness to tolerate intrinsic parameter fluctuation and extrinsic robustness to filter the environmental noise, then robust synchronization of coupled synthetic genetic oscillators is guaranteed. If the synchronization robustness of a population of nonlinear stochastic coupled synthetic genetic oscillators distributed over different host cells could not be maintained, then robust synchronization could be enhanced by external control input through quorum sensing molecules. In order to simplify the analysis and design of robust synchronization of nonlinear stochastic synthetic genetic oscillators, the fuzzy interpolation method was employed to interpolate several local linear stochastic coupled systems to approximate the nonlinear stochastic coupled system so that the HJI-based synchronization design problem could be replaced by a simple linear matrix inequality (LMI)-based design problem, which could be solved with the help of LMI toolbox in MATLAB easily. Conclusion If the synchronization robustness criterion, i.e. the synchronization robustness ≥ intrinsic robustness + extrinsic robustness, then the stochastic coupled synthetic oscillators can be robustly synchronized in spite of intrinsic parameter fluctuation and extrinsic noise. If the synchronization robustness criterion is violated, external control scheme by adding inducer can be designed to improve synchronization robustness of coupled synthetic genetic oscillators. The investigated robust synchronization criteria and proposed external control method are useful for a population of coupled synthetic networks with emergent synchronization behavior, especially for multi-cellular, engineered networks. PMID:23101662

Robust high-performance control for robotic manipulators

NASA Technical Reports Server (NTRS)

Seraji, H.

1989-01-01

A robust control scheme to accomplish accurate trajectory tracking for an integrated system of manipulator-plus-actuators is proposed. The control scheme comprises a feedforward and a feedback controller. The feedforward controller contains any known part of the manipulator dynamics that can be used for online control. The feedback controller consists of adaptive position and velocity feedback gains and an auxiliary signal which is simply generated by a fixed-gain proportional/integral/derivative controller. The feedback controller is updated by very simple adaptation laws which contain both proportional and integral adaptation terms. By introduction of a simple sigma modification to the adaptation laws, robustness is guaranteed in the presence of unmodeled dynamics and disturbances.

Research on Robust Control Strategies for VSC-HVDC

NASA Astrophysics Data System (ADS)

Zhu, Kaicheng; Bao, Hai

2018-01-01

In the control system of VSC-HVDC, the phase locked loop provides phase signals to voltage vector control and trigger pulses to generate the required reference phase. The PLL is a typical second-order system. When the system is in unstable state, it will oscillate, make the trigger angle shift, produce harmonic, and make active power and reactive power coupled. Thus, considering the external disturbances introduced by the PLL in VSC-HVDC control system, the parameter perturbations of the controller and the model uncertainties, a H∞ robust controller of mixed sensitivity optimization problem is designed by using the Hinf function provided by the robust control toolbox. Then, compare it with the proportional integral controller through the MATLAB simulation experiment. By contrast, when the H∞ robust controller is added, active and reactive power of the converter station can track the change of reference values more accurately and quickly, and reduce overshoot. When the step change of active and reactive power occurs, mutual influence is reduced and better independent regulation is achieved.

The Robust Beauty of Ordinary Information

ERIC Educational Resources Information Center

Katsikopoulos, Konstantinos V.; Schooler, Lael J.; Hertwig, Ralph

2010-01-01

Heuristics embodying limited information search and noncompensatory processing of information can yield robust performance relative to computationally more complex models. One criticism raised against heuristics is the argument that complexity is hidden in the calculation of the cue order used to make predictions. We discuss ways to order cues…

Robust reliable sampled-data control for switched systems with application to flight control

NASA Astrophysics Data System (ADS)

Sakthivel, R.; Joby, Maya; Shi, P.; Mathiyalagan, K.

2016-11-01

This paper addresses the robust reliable stabilisation problem for a class of uncertain switched systems with random delays and norm bounded uncertainties. The main aim of this paper is to obtain the reliable robust sampled-data control design which involves random time delay with an appropriate gain control matrix for achieving the robust exponential stabilisation for uncertain switched system against actuator failures. In particular, the involved delays are assumed to be randomly time-varying which obeys certain mutually uncorrelated Bernoulli distributed white noise sequences. By constructing an appropriate Lyapunov-Krasovskii functional (LKF) and employing an average-dwell time approach, a new set of criteria is derived for ensuring the robust exponential stability of the closed-loop switched system. More precisely, the Schur complement and Jensen's integral inequality are used in derivation of stabilisation criteria. By considering the relationship among the random time-varying delay and its lower and upper bounds, a new set of sufficient condition is established for the existence of reliable robust sampled-data control in terms of solution to linear matrix inequalities (LMIs). Finally, an illustrative example based on the F-18 aircraft model is provided to show the effectiveness of the proposed design procedures.

Robust control of dielectric elastomer diaphragm actuator for human pulse signal tracking

NASA Astrophysics Data System (ADS)

Ye, Zhihang; Chen, Zheng; Asmatulu, Ramazan; Chan, Hoyin

2017-08-01

Human pulse signal tracking is an emerging technology that is needed in traditional Chinese medicine. However, soft actuation with multi-frequency tracking capability is needed for tracking human pulse signal. Dielectric elastomer (DE) is one type of soft actuating that has great potential in human pulse signal tracking. In this paper, a DE diaphragm actuator was designed and fabricated to track human pulse pressure signal. A physics-based and control-oriented model has been developed to capture the dynamic behavior of DE diaphragm actuator. Using the physical model, an H-infinity robust control was designed for the actuator to reject high-frequency sensing noises and disturbances. The robust control was then implemented in real-time to track a multi-frequency signal, which verified the tracking capability and robustness of the control system. In the human pulse signal tracking test, a human pulse signal was measured at the City University of Hong Kong and then was tracked using DE actuator at Wichita State University in the US. Experimental results have verified that the DE actuator with its robust control is capable of tracking human pulse signal.

Designing for Damage: Robust Flight Control Design using Sliding Mode Techniques

NASA Technical Reports Server (NTRS)

Vetter, T. K.; Wells, S. R.; Hess, Ronald A.; Bacon, Barton (Technical Monitor); Davidson, John (Technical Monitor)

2002-01-01

A brief review of sliding model control is undertaken, with particular emphasis upon the effects of neglected parasitic dynamics. Sliding model control design is interpreted in the frequency domain. The inclusion of asymptotic observers and control 'hedging' is shown to reduce the effects of neglected parasitic dynamics. An investigation into the application of observer-based sliding mode control to the robust longitudinal control of a highly unstable is described. The sliding mode controller is shown to exhibit stability and performance robustness superior to that of a classical loop-shaped design when significant changes in vehicle and actuator dynamics are employed to model airframe damage.

Characterization and quenching of friction-induced limit cycles of electro-hydraulic servovalve control systems with transport delay.

PubMed

Wang, Yuan-Jay

2010-10-01

This paper develops a systematic and straightforward methodology to characterize and quench the friction-induced limit cycle conditions in electro-hydraulic servovalve control systems with transport delay in the transmission line. The nonlinear friction characteristic is linearized by using its corresponding describing function. The delay time in the transmission line, which could accelerate the generation of limit cycles is particularly considered. The stability equation method together with parameter plane method provides a useful tool for the establishment of necessary conditions to sustain a limit cycle directly in the constructed controller coefficient plane. Also, the stable region, the unstable region, and the limit cycle region are identified in the parameter plane. The parameter plane characterizes a clear relationship between limit cycle amplitude, frequency, transport delay, and the controller coefficients to be designed. The stability of the predicted limit cycle is checked by plotting stability curves. The stability of the system is examined when the viscous gain changes with respect to the temperature of the working fluid. A feasible stable region is characterized in the parameter plane to allow a flexible choice of controller gains. The robust prevention of limit cycle is achieved by selecting controller gains from the asymptotic stability region. The predicted results are verified by simulations. It is seen that the friction-induced limit cycles can be effectively predicted, removed, and quenched via the design of the compensator even in the case of viscous gain and delay time variations unconditionally. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Sliding Mode Control of the X-33 with an Engine Failure

NASA Technical Reports Server (NTRS)

Shtessel, Yuri B.; Hall, Charles E.

2000-01-01

Ascent flight control of the X-3 is performed using two XRS-2200 linear aerospike engines. in addition to aerosurfaces. The baseline control algorithms are PID with gain scheduling. Flight control using an innovative method. Sliding Mode Control. is presented for nominal and engine failed modes of flight. An easy to implement, robust controller. requiring no reconfiguration or gain scheduling is demonstrated through high fidelity flight simulations. The proposed sliding mode controller utilizes a two-loop structure and provides robust. de-coupled tracking of both orientation angle command profiles and angular rate command profiles in the presence of engine failure, bounded external disturbances (wind gusts) and uncertain matrix of inertia. Sliding mode control causes the angular rate and orientation angle tracking error dynamics to be constrained to linear, de-coupled, homogeneous, and vector valued differential equations with desired eigenvalues. Conditions that restrict engine failures to robustness domain of the sliding mode controller are derived. Overall stability of a two-loop flight control system is assessed. Simulation results show that the designed controller provides robust, accurate, de-coupled tracking of the orientation angle command profiles in the presence of external disturbances and vehicle inertia uncertainties, as well as the single engine failed case. The designed robust controller will significantly reduce the time and cost associated with flying new trajectory profiles or orbits, with new payloads, and with modified vehicles

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

DOE PAGES

Tapia, Gustavo; Khairallah, Saad A.; Matthews, Manyalibo J.; ...

2017-09-22

Here, Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagrammore » delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.« less

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tapia, Gustavo; Khairallah, Saad A.; Matthews, Manyalibo J.

Here, Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagrammore » delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.« less

Active Fault Tolerant Control for Ultrasonic Piezoelectric Motor

NASA Astrophysics Data System (ADS)

Boukhnifer, Moussa

2012-07-01

Ultrasonic piezoelectric motor technology is an important system component in integrated mechatronics devices working on extreme operating conditions. Due to these constraints, robustness and performance of the control interfaces should be taken into account in the motor design. In this paper, we apply a new architecture for a fault tolerant control using Youla parameterization for an ultrasonic piezoelectric motor. The distinguished feature of proposed controller architecture is that it shows structurally how the controller design for performance and robustness may be done separately which has the potential to overcome the conflict between performance and robustness in the traditional feedback framework. A fault tolerant control architecture includes two parts: one part for performance and the other part for robustness. The controller design works in such a way that the feedback control system will be solely controlled by the proportional plus double-integral PI2 performance controller for a nominal model without disturbances and H∞ robustification controller will only be activated in the presence of the uncertainties or an external disturbances. The simulation results demonstrate the effectiveness of the proposed fault tolerant control architecture.

Early Improvement in Work Productivity Predicts Future Clinical Course in Depressed Outpatients: Findings from the CO-MED Trial

PubMed Central

Jha, Manish K.; Minhajuddin, Abu; Greer, Tracy L.; Carmody, Thomas; Rush, A. John; Trivedi, Madhukar H.

2018-01-01

Objective Depression symptom severity, the most commonly studied outcome in antidepressant treatment trials, accounts for only a small portion of burden related to major depression. While lost work productivity is the biggest contributor to depression’s economic burden, few studies have systematically evaluated the independent effect of treatment on work productivity and the relationship between changes in work productivity and longer-term clinical course. Method Work productivity was measured repeatedly by the Work Productivity and Activity Impairment (WPAI) self-report in 331 employed participants with major depression enrolled in the Combining Medications to Enhance Depression Outcomes (CO-MED) trial. Trajectories of change in work productivity during the first 6 weeks of treatment were identified and used to predict remission at 3 and 7 months. Results Participants reported reduced absence from work and increased work productivity with antidepressant treatment even after controlling for changes in depression severity. Three distinct trajectories of changes in work productivity were identified: 1) robust early improvement (24%), 2) minimal change (49%), and 3) high-impairment slight reduction (27%). As compared to other participants, those with robust improvement had 3–5 times higher remission rates at 3 months and 2–5 times higher remission rates at 7 months, even after controlling for select baseline variables and remission status at week 6. Conclusions In this secondary analysis, self-reported work productivity improved in depressed patients with antidepressant treatment even after accounting for depressive symptom reduction. Early improvement in work productivity is associated with much higher remission rates after 3 and 7 months of treatment. PMID:27523501

Early Improvement in Work Productivity Predicts Future Clinical Course in Depressed Outpatients: Findings From the CO-MED Trial.

PubMed

Jha, Manish K; Minhajuddin, Abu; Greer, Tracy L; Carmody, Thomas; Rush, A John; Trivedi, Madhukar H

2016-12-01

Depression symptom severity, the most commonly studied outcome in antidepressant treatment trials, accounts for only a small portion of burden related to major depression. While lost work productivity is the biggest contributor to depression's economic burden, few studies have systematically evaluated the independent effect of treatment on work productivity and the relationship between changes in work productivity and longer-term clinical course. Work productivity was measured repeatedly by the Work Productivity and Activity Impairment self-report questionnaire in 331 employed participants with major depression enrolled in the Combining Medications to Enhance Depression Outcomes trial. Trajectories of change in work productivity during the first 6 weeks of treatment were identified and used to predict remission at 3 and 7 months. Participants reported reduced absence from work and increased work productivity with antidepressant treatment even after controlling for changes in depression severity. Three distinct trajectories of changes in work productivity were identified: 1) robust early improvement (24%), 2) minimal change (49%), and 3) high-impairment slight reduction (27%). Compared with other participants, those with robust improvement had 3-5 times higher remission rates at 3 months and 2-5 times higher remission rates at 7 months, even after controlling for select baseline variables and remission status at week 6. In this secondary analysis, self-reported work productivity improved in depressed patients with antidepressant treatment even after accounting for depressive symptom reduction. Early improvement in work productivity is associated with much higher remission rates after 3 and 7 months of treatment.

Observed ground-motion variabilities and implication for source properties

NASA Astrophysics Data System (ADS)

Cotton, F.; Bora, S. S.; Bindi, D.; Specht, S.; Drouet, S.; Derras, B.; Pina-Valdes, J.

2016-12-01

One of the key challenges of seismology is to be able to calibrate and analyse the physical factors that control earthquake and ground-motion variabilities. Within the framework of empirical ground-motion prediction equation (GMPE) developments, ground-motions residuals (differences between recorded ground motions and the values predicted by a GMPE) are computed. The exponential growth of seismological near-field records and modern regression algorithms allow to decompose these residuals into between-event and a within-event residual components. The between-event term quantify all the residual effects of the source (e.g. stress-drops) which are not accounted by magnitude term as the only source parameter of the model. Between-event residuals provide a new and rather robust way to analyse the physical factors that control earthquake source properties and associated variabilities. We first will show the correlation between classical stress-drops and between-event residuals. We will also explain why between-event residuals may be a more robust way (compared to classical stress-drop analysis) to analyse earthquake source-properties. We will finally calibrate between-events variabilities using recent high-quality global accelerometric datasets (NGA-West 2, RESORCE) and datasets from recent earthquakes sequences (Aquila, Iquique, Kunamoto). The obtained between-events variabilities will be used to evaluate the variability of earthquake stress-drops but also the variability of source properties which cannot be explained by a classical Brune stress-drop variations. We will finally use the between-event residual analysis to discuss regional variations of source properties, differences between aftershocks and mainshocks and potential magnitude dependencies of source characteristics.

Modelling and Simulation of the Dynamics of the Antigen-Specific T Cell Response Using Variable Structure Control Theory.

PubMed

Anelone, Anet J N; Spurgeon, Sarah K

2016-01-01

Experimental and mathematical studies in immunology have revealed that the dynamics of the programmed T cell response to vigorous infection can be conveniently modelled using a sigmoidal or a discontinuous immune response function. This paper hypothesizes strong synergies between this existing work and the dynamical behaviour of engineering systems with a variable structure control (VSC) law. These findings motivate the interpretation of the immune system as a variable structure control system. It is shown that dynamical properties as well as conditions to analytically assess the transition from health to disease can be developed for the specific T cell response from the theory of variable structure control. In particular, it is shown that the robustness properties of the specific T cell response as observed in experiments can be explained analytically using a VSC perspective. Further, the predictive capacity of the VSC framework to determine the T cell help required to overcome chronic Lymphocytic Choriomeningitis Virus (LCMV) infection is demonstrated. The findings demonstrate that studying the immune system using variable structure control theory provides a new framework for evaluating immunological dynamics and experimental observations. A modelling and simulation tool results with predictive capacity to determine how to modify the immune response to achieve healthy outcomes which may have application in drug development and vaccine design.

Developmental Pathways from Prenatal Tobacco and Stress Exposure to Behavioral Disinhibition

PubMed Central

Clark, C.A.C.; Espy, K.A.; Wakschlag, L.

2016-01-01

Prenatal tobacco exposure (PTE) and prenatal stress exposure (PSE) each have been linked to externalizing behavior, although their effects generally have been considered in isolation. Here, we aimed to characterize the joint or interactive roles of PTE and PSE in early developmental pathways to behavioral disinhibition, a profile of cognitive and behavioral under-control that presages severe externalizing behavior. As part of a prospective, longitudinal study, 296 children were assessed at a mean age of 5 years. Exposures were assessed via repeated interviews across the prenatal period and bioassays of cotinine were obtained. Behavioral disinhibition was assessed using temperament measures in infancy, performance-based executive control tasks and measures of disruptive and inattentive behavior. PSE was associated with a higher probability of difficult temperament in infancy. Each exposure independently predicted poorer executive control at age 5 years. Difficult temperament and executive control difficulties in turn predicted elevated levels of disruptive behavior, although links from PTE and PSE to parent-reported attention problems were less robust. Children who experienced these prenatal exposures in conjunction with higher postnatal stress exposure showed the lowest executive control and highest levels of disruptive behavior. Findings highlight the compounding adverse impact of PTE and PSE on children’s behavioral trajectories. Given their high concordance, prenatal health campaigns should target these exposures in tandem. PMID:26628107

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

Chemometric compositional analysis of phenolic compounds in fermenting samples and wines using different infrared spectroscopy techniques.

PubMed

Aleixandre-Tudo, Jose Luis; Nieuwoudt, Helene; Aleixandre, Jose Luis; du Toit, Wessel

2018-01-01

The wine industry requires reliable methods for the quantification of phenolic compounds during the winemaking process. Infrared spectroscopy appears as a suitable technique for process control and monitoring. The ability of Fourier transform near infrared (FT-NIR), attenuated total reflectance mid infrared (ATR-MIR) and Fourier transform infrared (FT-IR) spectroscopies to predict compositional phenolic levels during red wine fermentation and aging was investigated. Prediction models containing a large number of samples collected over two vintages from several industrial fermenting tanks as well as wine samples covering a varying number of vintages were validated. FT-NIR appeared as the most accurate technique to predict the phenolic content. Although slightly less accurate models were observed, ATR-MIR and FT-IR can also be used for the prediction of the majority of phenolic measurements. Additionally, the slope and intercept test indicated a systematic error for the three spectroscopies which seems to be slightly more pronounced for HPLC generated phenolics data than for the spectrophotometric parameters. However, the results also showed that the predictions made with the three instruments are statistically comparable. The robustness of the prediction models was also investigated and discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Robust Control Design for Uncertain Nonlinear Dynamic Systems

NASA Technical Reports Server (NTRS)

Kenny, Sean P.; Crespo, Luis G.; Andrews, Lindsey; Giesy, Daniel P.

2012-01-01

Robustness to parametric uncertainty is fundamental to successful control system design and as such it has been at the core of many design methods developed over the decades. Despite its prominence, most of the work on robust control design has focused on linear models and uncertainties that are non-probabilistic in nature. Recently, researchers have acknowledged this disparity and have been developing theory to address a broader class of uncertainties. This paper presents an experimental application of robust control design for a hybrid class of probabilistic and non-probabilistic parametric uncertainties. The experimental apparatus is based upon the classic inverted pendulum on a cart. The physical uncertainty is realized by a known additional lumped mass at an unknown location on the pendulum. This unknown location has the effect of substantially altering the nominal frequency and controllability of the nonlinear system, and in the limit has the capability to make the system neutrally stable and uncontrollable. Another uncertainty to be considered is a direct current motor parameter. The control design objective is to design a controller that satisfies stability, tracking error, control power, and transient behavior requirements for the largest range of parametric uncertainties. This paper presents an overview of the theory behind the robust control design methodology and the experimental results.

Effects of temporal and spatial resolution of calibration data on integrated hydrologic water quality model identification

NASA Astrophysics Data System (ADS)

Jiang, Sanyuan; Jomaa, Seifeddine; Büttner, Olaf; Rode, Michael

2014-05-01

Hydrological water quality modeling is increasingly used for investigating runoff and nutrient transport processes as well as watershed management but it is mostly unclear how data availablity determins model identification. In this study, the HYPE (HYdrological Predictions for the Environment) model, which is a process-based, semi-distributed hydrological water quality model, was applied in two different mesoscale catchments (Selke (463 km2) and Weida (99 km2)) located in central Germany to simulate discharge and inorganic nitrogen (IN) transport. PEST and DREAM(ZS) were combined with the HYPE model to conduct parameter calibration and uncertainty analysis. Split-sample test was used for model calibration (1994-1999) and validation (1999-2004). IN concentration and daily IN load were found to be highly correlated with discharge, indicating that IN leaching is mainly controlled by runoff. Both dynamics and balances of water and IN load were well captured with NSE greater than 0.83 during validation period. Multi-objective calibration (calibrating hydrological and water quality parameters simultaneously) was found to outperform step-wise calibration in terms of model robustness. Multi-site calibration was able to improve model performance at internal sites, decrease parameter posterior uncertainty and prediction uncertainty. Nitrogen-process parameters calibrated using continuous daily averages of nitrate-N concentration observations produced better and more robust simulations of IN concentration and load, lower posterior parameter uncertainty and IN concentration prediction uncertainty compared to the calibration against uncontinuous biweekly nitrate-N concentration measurements. Both PEST and DREAM(ZS) are efficient in parameter calibration. However, DREAM(ZS) is more sound in terms of parameter identification and uncertainty analysis than PEST because of its capability to evolve parameter posterior distributions and estimate prediction uncertainty based on global search and Bayesian inference schemes.

Silicon quantum processor with robust long-distance qubit couplings.

PubMed

Tosi, Guilherme; Mohiyaddin, Fahd A; Schmitt, Vivien; Tenberg, Stefanie; Rahman, Rajib; Klimeck, Gerhard; Morello, Andrea

2017-09-06

Practical quantum computers require a large network of highly coherent qubits, interconnected in a design robust against errors. Donor spins in silicon provide state-of-the-art coherence and quantum gate fidelities, in a platform adapted from industrial semiconductor processing. Here we present a scalable design for a silicon quantum processor that does not require precise donor placement and leaves ample space for the routing of interconnects and readout devices. We introduce the flip-flop qubit, a combination of the electron-nuclear spin states of a phosphorus donor that can be controlled by microwave electric fields. Two-qubit gates exploit a second-order electric dipole-dipole interaction, allowing selective coupling beyond the nearest-neighbor, at separations of hundreds of nanometers, while microwave resonators can extend the entanglement to macroscopic distances. We predict gate fidelities within fault-tolerance thresholds using realistic noise models. This design provides a realizable blueprint for scalable spin-based quantum computers in silicon.Quantum computers will require a large network of coherent qubits, connected in a noise-resilient way. Tosi et al. present a design for a quantum processor based on electron-nuclear spins in silicon, with electrical control and coupling schemes that simplify qubit fabrication and operation.

Natural Diversity in Heat Resistance of Bacteria and Bacterial Spores: Impact on Food Safety and Quality.

PubMed

den Besten, Heidy M W; Wells-Bennik, Marjon H J; Zwietering, Marcel H

2018-03-25

Heat treatments are widely used in food processing often with the aim of reducing or eliminating spoilage microorganisms and pathogens in food products. The efficacy of applying heat to control microorganisms is challenged by the natural diversity of microorganisms with respect to their heat robustness. This review gives an overview of the variations in heat resistances of various species and strains, describes modeling approaches to quantify heat robustness, and addresses the relevance and impact of the natural diversity of microorganisms when assessing heat inactivation. This comparison of heat resistances of microorganisms facilitates the evaluation of which (groups of) organisms might be troublesome in a production process in which heat treatment is critical to reducing the microbial contaminants, and also allows fine-tuning of the process parameters. Various sources of microbiological variability are discussed and compared for a range of species, including spore-forming and non-spore-forming pathogens and spoilage organisms. This benchmarking of variability factors gives crucial information about the most important factors that should be included in risk assessments to realistically predict heat inactivation of bacteria and spores as part of the measures for controlling shelf life and safety of food products.

A quality-by-design approach to risk reduction and optimization for human embryonic stem cell cryopreservation processes.

PubMed

Mitchell, Peter D; Ratcliffe, Elizabeth; Hourd, Paul; Williams, David J; Thomas, Robert J

2014-12-01

It is well documented that cryopreservation and resuscitation of human embryonic stem cells (hESCs) is complex and ill-defined, and often suffers poor cell recovery and increased levels of undesirable cell differentiation. In this study we have applied Quality-by-Design (QbD) concepts to the critical processes of slow-freeze cryopreservation and resuscitation of hESC colony cultures. Optimized subprocesses were linked together to deliver a controlled complete process. We have demonstrated a rapid, high-throughput, and stable system for measurement of cell adherence and viability as robust markers of in-process and postrecovery cell state. We observed that measurement of adherence and viability of adhered cells at 1 h postseeding was predictive of cell proliferative ability up to 96 h in this system. Application of factorial design defined the operating spaces for cryopreservation and resuscitation, critically linking the performance of these two processes. Optimization of both processes resulted in enhanced reattachment and post-thaw viability, resulting in substantially greater recovery of cryopreserved, pluripotent cell colonies. This study demonstrates the importance of QbD concepts and tools for rapid, robust, and low-risk process design that can inform manufacturing controls and logistics.

Active vibration absorber for CSI evolutionary model: Design and experimental results

NASA Technical Reports Server (NTRS)

Bruner, Anne M.; Belvin, W. Keith; Horta, Lucas G.; Juang, Jer-Nan

1991-01-01

The development of control of large flexible structures technology must include practical demonstration to aid in the understanding and characterization of controlled structures in space. To support this effort, a testbed facility was developed to study practical implementation of new control technologies under realistic conditions. The design is discussed of a second order, acceleration feedback controller which acts as an active vibration absorber. This controller provides guaranteed stability margins for collocated sensor/actuator pairs in the absence of sensor/actuator dynamics and computational time delay. The primary performance objective considered is damping augmentation of the first nine structural modes. Comparison of experimental and predicted closed loop damping is presented, including test and simulation time histories for open and closed loop cases. Although the simulation and test results are not in full agreement, robustness of this design under model uncertainty is demonstrated. The basic advantage of this second order controller design is that the stability of the controller is model independent.

NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction.

PubMed

Pardoe, Heath R; Kuzniecky, Ruben

2018-01-01

The availability of cloud computing services has enabled the widespread adoption of the "software as a service" (SaaS) approach for software distribution, which utilizes network-based access to applications running on centralized servers. In this paper we apply the SaaS approach to neuroimaging-based age prediction. Our system, named "NAPR" (Neuroanatomical Age Prediction using R), provides access to predictive modeling software running on a persistent cloud-based Amazon Web Services (AWS) compute instance. The NAPR framework allows external users to estimate the age of individual subjects using cortical thickness maps derived from their own locally processed T1-weighted whole brain MRI scans. As a demonstration of the NAPR approach, we have developed two age prediction models that were trained using healthy control data from the ABIDE, CoRR, DLBS and NKI Rockland neuroimaging datasets (total N = 2367, age range 6-89 years). The provided age prediction models were trained using (i) relevance vector machines and (ii) Gaussian processes machine learning methods applied to cortical thickness surfaces obtained using Freesurfer v5.3. We believe that this transparent approach to out-of-sample evaluation and comparison of neuroimaging age prediction models will facilitate the development of improved age prediction models and allow for robust evaluation of the clinical utility of these methods.

Robust control of multi-jointed arm with a decentralized autonomous control mechanism

NASA Technical Reports Server (NTRS)

Kimura, Shinichi; Miyazaki, Ken; Suzuki, Yoshiaki

1994-01-01

A decentralized autonomous control mechanism applied to the control of three dimensional manipulators and its robustness to partial damage was assessed by computer simulation. Decentralized control structures are believed to be quite robust to time delay between the operator and the target system. A 10-jointed manipulator based on our control mechanism was able to continue its positioning task in three-dimensional space without revision of the control program, even after some of its joints were damaged. These results suggest that this control mechanism can be effectively applied to space telerobots, which are associated with serious time delay between the operator and the target system, and which cannot be easily repaired after being partially damaged.

Power oscillation suppression by robust SMES in power system with large wind power penetration

NASA Astrophysics Data System (ADS)

Ngamroo, Issarachai; Cuk Supriyadi, A. N.; Dechanupaprittha, Sanchai; Mitani, Yasunori

2009-01-01

The large penetration of wind farm into interconnected power systems may cause the severe problem of tie-line power oscillations. To suppress power oscillations, the superconducting magnetic energy storage (SMES) which is able to control active and reactive powers simultaneously, can be applied. On the other hand, several generating and loading conditions, variation of system parameters, etc., cause uncertainties in the system. The SMES controller designed without considering system uncertainties may fail to suppress power oscillations. To enhance the robustness of SMES controller against system uncertainties, this paper proposes a robust control design of SMES by taking system uncertainties into account. The inverse additive perturbation is applied to represent the unstructured system uncertainties and included in power system modeling. The configuration of active and reactive power controllers is the first-order lead-lag compensator with single input feedback. To tune the controller parameters, the optimization problem is formulated based on the enhancement of robust stability margin. The particle swarm optimization is used to solve the problem and achieve the controller parameters. Simulation studies in the six-area interconnected power system with wind farms confirm the robustness of the proposed SMES under various operating conditions.

Compensating for intersegmental dynamics across the shoulder, elbow, and wrist joints during feedforward and feedback control.

PubMed

Maeda, Rodrigo S; Cluff, Tyler; Gribble, Paul L; Pruszynski, J Andrew

2017-10-01

Moving the arm is complicated by mechanical interactions that arise between limb segments. Such intersegmental dynamics cause torques applied at one joint to produce movement at multiple joints, and in turn, the only way to create single joint movement is by applying torques at multiple joints. We investigated whether the nervous system accounts for intersegmental limb dynamics across the shoulder, elbow, and wrist joints during self-initiated planar reaching and when countering external mechanical perturbations. Our first experiment tested whether the timing and amplitude of shoulder muscle activity account for interaction torques produced during single-joint elbow movements from different elbow initial orientations and over a range of movement speeds. We found that shoulder muscle activity reliably preceded movement onset and elbow agonist activity, and was scaled to compensate for the magnitude of interaction torques arising because of forearm rotation. Our second experiment tested whether elbow muscles compensate for interaction torques introduced by single-joint wrist movements. We found that elbow muscle activity preceded movement onset and wrist agonist muscle activity, and thus the nervous system predicted interaction torques arising because of hand rotation. Our third and fourth experiments tested whether shoulder muscles compensate for interaction torques introduced by different hand orientations during self-initiated elbow movements and to counter mechanical perturbations that caused pure elbow motion. We found that the nervous system predicted the amplitude and direction of interaction torques, appropriately scaling the amplitude of shoulder muscle activity during self-initiated elbow movements and rapid feedback control. Taken together, our results demonstrate that the nervous system robustly accounts for intersegmental dynamics and that the process is similar across the proximal to distal musculature of the arm as well as between feedforward (i.e., self-initiated) and feedback (i.e., reflexive) control. NEW & NOTEWORTHY Intersegmental dynamics complicate the mapping between applied joint torques and the resulting joint motions. We provide evidence that the nervous system robustly predicts these intersegmental limb dynamics across the shoulder, elbow, and wrist joints during reaching and when countering external perturbations. Copyright © 2017 the American Physiological Society.

Robustness of Controllability for Networks Based on Edge-Attack

PubMed Central

Nie, Sen; Wang, Xuwen; Zhang, Haifeng; Li, Qilang; Wang, Binghong

2014-01-01

We study the controllability of networks in the process of cascading failures under two different attacking strategies, random and intentional attack, respectively. For the highest-load edge attack, it is found that the controllability of Erdős-Rényi network, that with moderate average degree, is less robust, whereas the Scale-free network with moderate power-law exponent shows strong robustness of controllability under the same attack strategy. The vulnerability of controllability under random and intentional attacks behave differently with the increasing of removal fraction, especially, we find that the robustness of control has important role in cascades for large removal fraction. The simulation results show that for Scale-free networks with various power-law exponents, the network has larger scale of cascades do not mean that there will be more increments of driver nodes. Meanwhile, the number of driver nodes in cascading failures is also related to the edges amount in strongly connected components. PMID:24586507

Robustness of controllability for networks based on edge-attack.

PubMed

Nie, Sen; Wang, Xuwen; Zhang, Haifeng; Li, Qilang; Wang, Binghong

2014-01-01

We study the controllability of networks in the process of cascading failures under two different attacking strategies, random and intentional attack, respectively. For the highest-load edge attack, it is found that the controllability of Erdős-Rényi network, that with moderate average degree, is less robust, whereas the Scale-free network with moderate power-law exponent shows strong robustness of controllability under the same attack strategy. The vulnerability of controllability under random and intentional attacks behave differently with the increasing of removal fraction, especially, we find that the robustness of control has important role in cascades for large removal fraction. The simulation results show that for Scale-free networks with various power-law exponents, the network has larger scale of cascades do not mean that there will be more increments of driver nodes. Meanwhile, the number of driver nodes in cascading failures is also related to the edges amount in strongly connected components.

Robust, nonlinear, high angle-of-attack control design for a supermaneuverable vehicle

NASA Technical Reports Server (NTRS)

Adams, Richard J.

1993-01-01

High angle-of-attack flight control laws are developed for a supermaneuverable fighter aircraft. The methods of dynamic inversion and structured singular value synthesis are combined into an approach which addresses both the nonlinearity and robustness problems of flight at extreme operating conditions. The primary purpose of the dynamic inversion control elements is to linearize the vehicle response across the flight envelope. Structured singular value synthesis is used to design a dynamic controller which provides robust tracking to pilot commands. The resulting control system achieves desired flying qualities and guarantees a large margin of robustness to uncertainties for high angle-of-attack flight conditions. The results of linear simulation and structured singular value stability analysis are presented to demonstrate satisfaction of the design criteria. High fidelity nonlinear simulation results show that the combined dynamics inversion/structured singular value synthesis control law achieves a high level of performance in a realistic environment.

A Computational Framework to Control Verification and Robustness Analysis

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2010-01-01

This paper presents a methodology for evaluating the robustness of a controller based on its ability to satisfy the design requirements. The framework proposed is generic since it allows for high-fidelity models, arbitrary control structures and arbitrary functional dependencies between the requirements and the uncertain parameters. The cornerstone of this contribution is the ability to bound the region of the uncertain parameter space where the degradation in closed-loop performance remains acceptable. The size of this bounding set, whose geometry can be prescribed according to deterministic or probabilistic uncertainty models, is a measure of robustness. The robustness metrics proposed herein are the parametric safety margin, the reliability index, the failure probability and upper bounds to this probability. The performance observed at the control verification setting, where the assumptions and approximations used for control design may no longer hold, will fully determine the proposed control assessment.

Control of a small working robot on a large flexible manipulator for suppressing vibrations: Development of a robust control law for flexible robot and it's stability analysis

NASA Technical Reports Server (NTRS)

Soo, Han Lee

1991-01-01

Researchers developed a robust control law for slow motions for the accurate trajectory control of a flexible robot. The control law does not need larger velocity gains than position gains, which some researchers need to ensure the stability of a rigid robot. Initial experimentation for the Small Articulated Manipulator (SAM) shows that control laws that use smaller velocity gains are more robust to signal noise than the control laws that use larger velocity gains. Researchers analyzed the stability of the composite control law, the robust control for the slow motion, and the strain rate feedback for the fast control. The stability analysis was done by using a quadratic Liapunov function. Researchers found that the flexible motion of links could be controlled by relating the input force to the flexible signals which are sensed at the near tip of each link. The signals are contaminated by the time delayed input force. However, the effect of the time delayed input force can be reduced by giving a certain configuration to the SAM.

Manufacturing Execution Systems: Examples of Performance Indicator and Operational Robustness Tools.

PubMed

Gendre, Yannick; Waridel, Gérard; Guyon, Myrtille; Demuth, Jean-François; Guelpa, Hervé; Humbert, Thierry

Manufacturing Execution Systems (MES) are computerized systems used to measure production performance in terms of productivity, yield, and quality. In the first part, performance indicator and overall equipment effectiveness (OEE), process robustness tools and statistical process control are described. The second part details some tools to help process robustness and control by operators by preventing deviations from target control charts. MES was developed by Syngenta together with CIMO for automation.

Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

NASA Technical Reports Server (NTRS)

Burken, John J.

2005-01-01

This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

A robust control scheme for flexible arms with friction in the joints

NASA Technical Reports Server (NTRS)

Rattan, Kuldip S.; Feliu, Vicente; Brown, H. Benjamin, Jr.

1988-01-01

A general control scheme to control flexible arms with friction in the joints is proposed in this paper. This scheme presents the advantage of being robust in the sense that it minimizes the effects of the Coulomb friction existing in the motor and the effects of changes in the dynamic friction coefficient. A justification of the robustness properties of the scheme is given in terms of the sensitivity analysis.

Optimal and robust control of quantum state transfer by shaping the spectral phase of ultrafast laser pulses.

PubMed

Guo, Yu; Dong, Daoyi; Shu, Chuan-Cun

2018-04-04

Achieving fast and efficient quantum state transfer is a fundamental task in physics, chemistry and quantum information science. However, the successful implementation of the perfect quantum state transfer also requires robustness under practically inevitable perturbative defects. Here, we demonstrate how an optimal and robust quantum state transfer can be achieved by shaping the spectral phase of an ultrafast laser pulse in the framework of frequency domain quantum optimal control theory. Our numerical simulations of the single dibenzoterrylene molecule as well as in atomic rubidium show that optimal and robust quantum state transfer via spectral phase modulated laser pulses can be achieved by incorporating a filtering function of the frequency into the optimization algorithm, which in turn has potential applications for ultrafast robust control of photochemical reactions.

Robust linear quadratic designs with respect to parameter uncertainty

NASA Technical Reports Server (NTRS)

Douglas, Joel; Athans, Michael

1992-01-01

The authors derive a linear quadratic regulator (LQR) which is robust to parametric uncertainty by using the overbounding method of I. R. Petersen and C. V. Hollot (1986). The resulting controller is determined from the solution of a single modified Riccati equation. It is shown that, when applied to a structural system, the controller gains add robustness by minimizing the potential energy of uncertain stiffness elements, and minimizing the rate of dissipation of energy through uncertain damping elements. A worst-case disturbance in the direction of the uncertainty is also considered. It is proved that performance robustness has been increased with the robust LQR when compared to a mismatched LQR design where the controller is designed on the nominal system, but applied to the actual uncertain system.

Polarization control of isolated high-harmonic pulses

NASA Astrophysics Data System (ADS)

Huang, Pei-Chi; Hernández-García, Carlos; Huang, Jen-Ting; Huang, Po-Yao; Lu, Chih-Hsuan; Rego, Laura; Hickstein, Daniel D.; Ellis, Jennifer L.; Jaron-Becker, Agnieszka; Becker, Andreas; Yang, Shang-Da; Durfee, Charles G.; Plaja, Luis; Kapteyn, Henry C.; Murnane, Margaret M.; Kung, A. H.; Chen, Ming-Chang

2018-06-01

High-harmonic generation driven by femtosecond lasers makes it possible to capture the fastest dynamics in molecules and materials. However, thus far, the shortest isolated attosecond pulses have only been produced with linear polarization, which limits the range of physics that can be explored. Here, we demonstrate robust polarization control of isolated extreme-ultraviolet pulses by exploiting non-collinear high-harmonic generation driven by two counter-rotating few-cycle laser beams. The circularly polarized supercontinuum is produced at a central photon energy of 33 eV with a transform limit of 190 as and a predicted linear chirp of 330 as. By adjusting the ellipticity of the two counter-rotating driving pulses simultaneously, we control the polarization state of isolated extreme-ultraviolet pulses—from circular through elliptical to linear polarization—without sacrificing conversion efficiency. Access to the purely circularly polarized supercontinuum, combined with full helicity and ellipticity control, paves the way towards attosecond metrology of circular dichroism.

Development of a combined feed forward-feedback system for an electron Linac

NASA Astrophysics Data System (ADS)

Meier, E.; Biedron, S. G.; LeBlanc, G.; Morgan, M. J.; Wu, J.

2009-10-01

This paper describes the results of an advanced control algorithm for the stabilization of electron beam energy in a Linac. The approach combines a conventional Proportional-Integral (PI) controller with a neural network (NNET) feed forward algorithm; it utilizes the robustness of PI control and the ability of a feed forward system in order to exert control over a wider range of frequencies. The NNET is trained to recognize jitter occurring in the phase and voltage of one of the klystrons, based on a record of these parameters, and predicts future energy deviations. A systematic approach is developed to determine the optimal NNET parameters that are then applied to the Australian Synchrotron Linac. The system's capability to fully cancel multi-frequency jitter is demonstrated. The NNET system is then augmented with the PI algorithm, and further jitter attenuation is achieved when the NNET is not operating optimally.

ENSO controls interannual fire activity in southeast Australia

NASA Astrophysics Data System (ADS)

Mariani, M.; Fletcher, M.-S.; Holz, A.; Nyman, P.

2016-10-01

El Niño-Southern Oscillation (ENSO) is the main mode controlling the variability in the ocean-atmosphere system in the South Pacific. While the ENSO influence on rainfall regimes in the South Pacific is well documented, its role in driving spatiotemporal trends in fire activity in this region has not been rigorously investigated. This is particularly the case for the highly flammable and densely populated southeast Australian sector, where ENSO is a major control over climatic variability. Here we conduct the first region-wide analysis of how ENSO controls fire activity in southeast Australia. We identify a significant relationship between ENSO and both fire frequency and area burnt. Critically, wavelet analyses reveal that despite substantial temporal variability in the ENSO system, ENSO exerts a persistent and significant influence on southeast Australian fire activity. Our analysis has direct application for developing robust predictive capacity for the increasingly important efforts at fire management.

A network property necessary for concentration robustness

NASA Astrophysics Data System (ADS)

Eloundou-Mbebi, Jeanne M. O.; Küken, Anika; Omranian, Nooshin; Kleessen, Sabrina; Neigenfind, Jost; Basler, Georg; Nikoloski, Zoran

2016-10-01

Maintenance of functionality of complex cellular networks and entire organisms exposed to environmental perturbations often depends on concentration robustness of the underlying components. Yet, the reasons and consequences of concentration robustness in large-scale cellular networks remain largely unknown. Here, we derive a necessary condition for concentration robustness based only on the structure of networks endowed with mass action kinetics. The structural condition can be used to design targeted experiments to study concentration robustness. We show that metabolites satisfying the necessary condition are present in metabolic networks from diverse species, suggesting prevalence of this property across kingdoms of life. We also demonstrate that our predictions about concentration robustness of energy-related metabolites are in line with experimental evidence from Escherichia coli. The necessary condition is applicable to mass action biological systems of arbitrary size, and will enable understanding the implications of concentration robustness in genetic engineering strategies and medical applications.

A network property necessary for concentration robustness.

PubMed

Eloundou-Mbebi, Jeanne M O; Küken, Anika; Omranian, Nooshin; Kleessen, Sabrina; Neigenfind, Jost; Basler, Georg; Nikoloski, Zoran

2016-10-19

Maintenance of functionality of complex cellular networks and entire organisms exposed to environmental perturbations often depends on concentration robustness of the underlying components. Yet, the reasons and consequences of concentration robustness in large-scale cellular networks remain largely unknown. Here, we derive a necessary condition for concentration robustness based only on the structure of networks endowed with mass action kinetics. The structural condition can be used to design targeted experiments to study concentration robustness. We show that metabolites satisfying the necessary condition are present in metabolic networks from diverse species, suggesting prevalence of this property across kingdoms of life. We also demonstrate that our predictions about concentration robustness of energy-related metabolites are in line with experimental evidence from Escherichia coli. The necessary condition is applicable to mass action biological systems of arbitrary size, and will enable understanding the implications of concentration robustness in genetic engineering strategies and medical applications.

A network property necessary for concentration robustness

PubMed Central

Eloundou-Mbebi, Jeanne M. O.; Küken, Anika; Omranian, Nooshin; Kleessen, Sabrina; Neigenfind, Jost; Basler, Georg; Nikoloski, Zoran

2016-01-01

Maintenance of functionality of complex cellular networks and entire organisms exposed to environmental perturbations often depends on concentration robustness of the underlying components. Yet, the reasons and consequences of concentration robustness in large-scale cellular networks remain largely unknown. Here, we derive a necessary condition for concentration robustness based only on the structure of networks endowed with mass action kinetics. The structural condition can be used to design targeted experiments to study concentration robustness. We show that metabolites satisfying the necessary condition are present in metabolic networks from diverse species, suggesting prevalence of this property across kingdoms of life. We also demonstrate that our predictions about concentration robustness of energy-related metabolites are in line with experimental evidence from Escherichia coli. The necessary condition is applicable to mass action biological systems of arbitrary size, and will enable understanding the implications of concentration robustness in genetic engineering strategies and medical applications. PMID:27759015

The cardiovascular robustness hypothesis: Unmasking young adults' hidden risk for premature cardiovascular death.

PubMed

Kraushaar, Lutz E; Dressel, Alexander

2018-03-01

An undetected high risk for premature death of cardiovascular disease (CVD) among individuals with low-to-moderate risk factor levels is an acknowledged obstacle to CVD prevention. In this paper, we present the hypothesis that the vasculature's robustness against risk factor load will complement conventional risk factor models as a novel stratifier of risk. Figuratively speaking, mortality risk prediction without robustness scoring is akin to predicting the breaking risk of a lake's ice sheet considering load only while disregarding the sheet's bearing strength. Taking the cue from systems biology, which defines robustness as the ability to maintain function against internal and external challenges, we develop a robustness score from the physical parameters that comprehensively quantitate cardiovascular function. We derive the functional parameters using a recently introduced novel system, VascAssist 2 (iSYMED GmbH, Butzbach, Germany). VascAssist 2 (VA) applies the electronic-hydraulic analogy to a digital model of the arterial tree, replicating non-invasively acquired pule pressure waves by modulating the electronic equivalents of the physical parameters that describe in vivo arterial hemodynamics. As the latter is also subject to aging-associated degeneration which (a) progresses at inter-individually different rates, and which (b) affects the biomarker-mortality association, we express the robustness score as a correction factor to calendar age (CA), the dominant risk factor in all CVD risk factor models. We then propose a method for the validation of the score against known time-to-event data in reference populations. Our conceptualization of robustness implies that risk factor-challenged individuals with low robustness scores will face preferential elimination from the population resulting in a significant robustness-CA correlation in this strata absent in the unchallenged stratum. Hence, we also present an outline of a cross-sectional study design suitable to test this hypothesis. We finally discuss the objections that may validly be raised against our robustness hypothesis, and how available evidence encourages us to refute these objections. Copyright © 2018 Elsevier Ltd. All rights reserved.

Utility of genetic and non-genetic risk factors in predicting coronary heart disease in Singaporean Chinese.

PubMed

Chang, Xuling; Salim, Agus; Dorajoo, Rajkumar; Han, Yi; Khor, Chiea-Chuen; van Dam, Rob M; Yuan, Jian-Min; Koh, Woon-Puay; Liu, Jianjun; Goh, Daniel Yt; Wang, Xu; Teo, Yik-Ying; Friedlander, Yechiel; Heng, Chew-Kiat

2017-01-01

Background Although numerous phenotype based equations for predicting risk of 'hard' coronary heart disease are available, data on the utility of genetic information for such risk prediction is lacking in Chinese populations. Design Case-control study nested within the Singapore Chinese Health Study. Methods A total of 1306 subjects comprising 836 men (267 incident cases and 569 controls) and 470 women (128 incident cases and 342 controls) were included. A Genetic Risk Score comprising 156 single nucleotide polymorphisms that have been robustly associated with coronary heart disease or its risk factors ( p < 5 × 10 -8 ) in at least two independent cohorts of genome-wide association studies was built. For each gender, three base models were used: recalibrated Adult Treatment Panel III (ATPIII) Model (M 1 ); ATP III model fitted using Singapore Chinese Health Study data (M 2 ) and M 3 : M 2 + C-reactive protein + creatinine. Results The Genetic Risk Score was significantly associated with incident 'hard' coronary heart disease ( p for men: 1.70 × 10 -10 -1.73 × 10 -9 ; p for women: 0.001). The inclusion of the Genetic Risk Score in the prediction models improved discrimination in both genders (c-statistics: 0.706-0.722 vs. 0.663-0.695 from base models for men; 0.788-0.790 vs. 0.765-0.773 for women). In addition, the inclusion of the Genetic Risk Score also improved risk classification with a net gain of cases being reclassified to higher risk categories (men: 12.4%-16.5%; women: 10.2% (M 3 )), while not significantly reducing the classification accuracy in controls. Conclusions The Genetic Risk Score is an independent predictor for incident 'hard' coronary heart disease in our ethnic Chinese population. Inclusion of genetic factors into coronary heart disease prediction models could significantly improve risk prediction performance.

A homotopy algorithm for synthesizing robust controllers for flexible structures via the maximum entropy design equations

NASA Technical Reports Server (NTRS)

Collins, Emmanuel G., Jr.; Richter, Stephen

1990-01-01

One well known deficiency of LQG compensators is that they do not guarantee any measure of robustness. This deficiency is especially highlighted when considering control design for complex systems such as flexible structures. There has thus been a need to generalize LQG theory to incorporate robustness constraints. Here we describe the maximum entropy approach to robust control design for flexible structures, a generalization of LQG theory, pioneered by Hyland, which has proved useful in practice. The design equations consist of a set of coupled Riccati and Lyapunov equations. A homotopy algorithm that is used to solve these design equations is presented.

Robust adaptive tracking control for nonholonomic mobile manipulator with uncertainties.

PubMed

Peng, Jinzhu; Yu, Jie; Wang, Jie

2014-07-01

In this paper, mobile manipulator is divided into two subsystems, that is, nonholonomic mobile platform subsystem and holonomic manipulator subsystem. First, the kinematic controller of the mobile platform is derived to obtain a desired velocity. Second, regarding the coupling between the two subsystems as disturbances, Lyapunov functions of the two subsystems are designed respectively. Third, a robust adaptive tracking controller is proposed to deal with the unknown upper bounds of parameter uncertainties and disturbances. According to the Lyapunov stability theory, the derived robust adaptive controller guarantees global stability of the closed-loop system, and the tracking errors and adaptive coefficient errors are all bounded. Finally, simulation results show that the proposed robust adaptive tracking controller for nonholonomic mobile manipulator is effective and has good tracking capacity. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining

PubMed Central

Liang, Qiaokang; Zhang, Dan; Wu, Wanneng; Zou, Kunlin

2016-01-01

Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC) tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing. PMID:27854322

Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining.

PubMed

Liang, Qiaokang; Zhang, Dan; Wu, Wanneng; Zou, Kunlin

2016-11-16

Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC) tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing.

Flight-determined stability analysis of multiple-input-multiple-output control systems

NASA Technical Reports Server (NTRS)

Burken, John J.

1992-01-01

Singular value analysis can give conservative stability margin results. Applying structure to the uncertainty can reduce this conservatism. This paper presents flight-determined stability margins for the X-29A lateral-directional, multiloop control system. These margins are compared with the predicted unscaled singular values and scaled structured singular values. The algorithm was further evaluated with flight data by changing the roll-rate-to-aileron command-feedback gain by +/- 20 percent. Minimum eigenvalues of the return difference matrix which bound the singular values are also presented. Extracting multiloop singular values from flight data and analyzing the feedback gain variations validates this technique as a measure of robustness. This analysis can be used for near-real-time flight monitoring and safety testing.

Flight-determined stability analysis of multiple-input-multiple-output control systems

NASA Technical Reports Server (NTRS)

Burken, John J.

1992-01-01

Singular value analysis can give conservative stability margin results. Applying structure to the uncertainty can reduce this conservatism. This paper presents flight-determined stability margins for the X-29A lateral-directional, multiloop control system. These margins are compared with the predicted unscaled singular values and scaled structured singular values. The algorithm was further evaluated with flight data by changing the roll-rate-to-aileron-command-feedback gain by +/- 20 percent. Also presented are the minimum eigenvalues of the return difference matrix which bound the singular values. Extracting multiloop singular values from flight data and analyzing the feedback gain variations validates this technique as a measure of robustness. This analysis can be used for near-real-time flight monitoring and safety testing.

How Reliable is Bayesian Model Averaging Under Noisy Data? Statistical Assessment and Implications for Robust Model Selection

NASA Astrophysics Data System (ADS)

Schöniger, Anneli; Wöhling, Thomas; Nowak, Wolfgang

2014-05-01

Bayesian model averaging ranks the predictive capabilities of alternative conceptual models based on Bayes' theorem. The individual models are weighted with their posterior probability to be the best one in the considered set of models. Finally, their predictions are combined into a robust weighted average and the predictive uncertainty can be quantified. This rigorous procedure does, however, not yet account for possible instabilities due to measurement noise in the calibration data set. This is a major drawback, since posterior model weights may suffer a lack of robustness related to the uncertainty in noisy data, which may compromise the reliability of model ranking. We present a new statistical concept to account for measurement noise as source of uncertainty for the weights in Bayesian model averaging. Our suggested upgrade reflects the limited information content of data for the purpose of model selection. It allows us to assess the significance of the determined posterior model weights, the confidence in model selection, and the accuracy of the quantified predictive uncertainty. Our approach rests on a brute-force Monte Carlo framework. We determine the robustness of model weights against measurement noise by repeatedly perturbing the observed data with random realizations of measurement error. Then, we analyze the induced variability in posterior model weights and introduce this "weighting variance" as an additional term into the overall prediction uncertainty analysis scheme. We further determine the theoretical upper limit in performance of the model set which is imposed by measurement noise. As an extension to the merely relative model ranking, this analysis provides a measure of absolute model performance. To finally decide, whether better data or longer time series are needed to ensure a robust basis for model selection, we resample the measurement time series and assess the convergence of model weights for increasing time series length. We illustrate our suggested approach with an application to model selection between different soil-plant models following up on a study by Wöhling et al. (2013). Results show that measurement noise compromises the reliability of model ranking and causes a significant amount of weighting uncertainty, if the calibration data time series is not long enough to compensate for its noisiness. This additional contribution to the overall predictive uncertainty is neglected without our approach. Thus, we strongly advertise to include our suggested upgrade in the Bayesian model averaging routine.

Event-Based Robust Control for Uncertain Nonlinear Systems Using Adaptive Dynamic Programming.

PubMed

Zhang, Qichao; Zhao, Dongbin; Wang, Ding

2018-01-01

In this paper, the robust control problem for a class of continuous-time nonlinear system with unmatched uncertainties is investigated using an event-based control method. First, the robust control problem is transformed into a corresponding optimal control problem with an augmented control and an appropriate cost function. Under the event-based mechanism, we prove that the solution of the optimal control problem can asymptotically stabilize the uncertain system with an adaptive triggering condition. That is, the designed event-based controller is robust to the original uncertain system. Note that the event-based controller is updated only when the triggering condition is satisfied, which can save the communication resources between the plant and the controller. Then, a single network adaptive dynamic programming structure with experience replay technique is constructed to approach the optimal control policies. The stability of the closed-loop system with the event-based control policy and the augmented control policy is analyzed using the Lyapunov approach. Furthermore, we prove that the minimal intersample time is bounded by a nonzero positive constant, which excludes Zeno behavior during the learning process. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

Temperament and Parenting during the First Year of Life Predict Future Child Conduct Problems

PubMed Central

Lahey, Benjamin B.; Van Hulle, Carol A.; Keenan, Kate; Rathouz, Paul J.; D’Onofrio, Brian M.; Rodgers, Joseph Lee; Waldman, Irwin D.

2010-01-01

Predictive associations between parenting and temperament during the first year of life and child conduct problems were assessed longitudinally in 1,863 offspring of a representative sample of women. Maternal ratings of infant fussiness, activity level, predictability, and positive affect each independently predicted maternal ratings of conduct problems during ages 4–13 years. Furthermore, a significant interaction indicated that infants who were both low in fussiness and high in predictability were at very low risk for future conduct problems. Fussiness was a stronger predictor of conduct problems in boys whereas fearfulness was a stronger predictor in girls. Conduct problems also were robustly predicted by low levels of early mother-report cognitive stimulation. Interviewer-rated maternal responsiveness was a robust predictor of conduct problems, but only among infants low in fearfulness. Spanking during infancy predicted slightly more severe conduct problems, but the prediction was moderated by infant fussiness and positive affect. Thus, individual differences in risk for mother-rated conduct problems across childhood are already partly evident in maternal ratings of temperament during the first year of life and are predicted by early parenting and parenting-by-temperament interactions. PMID:18568397

Brain network connectivity in individuals with schizophrenia and their siblings.

PubMed

Repovs, Grega; Csernansky, John G; Barch, Deanna M

2011-05-15

Research on brain activity in schizophrenia has shown that changes in the function of any single region cannot explain the range of cognitive and affective impairments in this illness. Rather, neural circuits that support sensory, cognitive, and emotional processes are now being investigated as substrates for cognitive and affective impairments in schizophrenia, a shift in focus consistent with long-standing hypotheses about schizophrenia as a disconnection syndrome. Our goal was to further examine alterations in functional connectivity within and between the default mode network and three cognitive control networks (frontal-parietal, cingulo-opercular, and cerebellar) as a basis for such impairments. Resting state functional magnetic resonance imaging was collected from 40 individuals with DSM-IV-TR schizophrenia, 31 siblings of individuals with schizophrenia, 15 healthy control subjects, and 18 siblings of healthy control subjects while they rested quietly with their eyes closed. Connectivity metrics were compared between patients and control subjects for both within- and between-network connections and were used to predict clinical symptoms and cognitive function. Individuals with schizophrenia showed reduced distal and somewhat enhanced local connectivity between the cognitive control networks compared with control subjects. Additionally, greater connectivity between the frontal-parietal and cerebellar regions was robustly predictive of better cognitive performance across groups and predictive of fewer disorganization symptoms among patients. These results are consistent with the hypothesis that impairments of executive function and cognitive control result from disruption in the coordination of activity across brain networks and additionally suggest that these might reflect impairments in normal pattern of brain connectivity development. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Feedback system design with an uncertain plant

NASA Technical Reports Server (NTRS)

Milich, D.; Valavani, L.; Athans, M.

1986-01-01

A method is developed to design a fixed-parameter compensator for a linear, time-invariant, SISO (single-input single-output) plant model characterized by significant structured, as well as unstructured, uncertainty. The controller minimizes the H(infinity) norm of the worst-case sensitivity function over the operating band and the resulting feedback system exhibits robust stability and robust performance. It is conjectured that such a robust nonadaptive control design technique can be used on-line in an adaptive control system.

A Robust Inner and Outer Loop Control Method for Trajectory Tracking of a Quadrotor

PubMed Central

Xia, Dunzhu; Cheng, Limei; Yao, Yanhong

2017-01-01

In order to achieve the complicated trajectory tracking of quadrotor, a geometric inner and outer loop control scheme is presented. The outer loop generates the desired rotation matrix for the inner loop. To improve the response speed and robustness, a geometric SMC controller is designed for the inner loop. The outer loop is also designed via sliding mode control (SMC). By Lyapunov theory and cascade theory, the closed-loop system stability is guaranteed. Next, the tracking performance is validated by tracking three representative trajectories. Then, the robustness of the proposed control method is illustrated by trajectory tracking in presence of model uncertainty and disturbances. Subsequently, experiments are carried out to verify the method. In the experiment, ultra wideband (UWB) is used for indoor positioning. Extended Kalman Filter (EKF) is used for fusing inertial measurement unit (IMU) and UWB measurements. The experimental results show the feasibility of the designed controller in practice. The comparative experiments with PD and PD loop demonstrate the robustness of the proposed control method. PMID:28925984

Robust Frequency-Domain Constrained Feedback Design via a Two-Stage Heuristic Approach.

PubMed

Li, Xianwei; Gao, Huijun

2015-10-01

Based on a two-stage heuristic method, this paper is concerned with the design of robust feedback controllers with restricted frequency-domain specifications (RFDSs) for uncertain linear discrete-time systems. Polytopic uncertainties are assumed to enter all the system matrices, while RFDSs are motivated by the fact that practical design specifications are often described in restricted finite frequency ranges. Dilated multipliers are first introduced to relax the generalized Kalman-Yakubovich-Popov lemma for output feedback controller synthesis and robust performance analysis. Then a two-stage approach to output feedback controller synthesis is proposed: at the first stage, a robust full-information (FI) controller is designed, which is used to construct a required output feedback controller at the second stage. To improve the solvability of the synthesis method, heuristic iterative algorithms are further formulated for exploring the feedback gain and optimizing the initial FI controller at the individual stage. The effectiveness of the proposed design method is finally demonstrated by the application to active control of suspension systems.

Design of a robust control law for the Vega launcher ballistic phase

NASA Astrophysics Data System (ADS)

Valli, Monica; Lavagna, Michèle R.; Panozzo, Thomas

2012-02-01

This work presents the design of a robust control law, and the related control system architecture, for the Vega launcher ballistic phase, taking into account the complete six degrees of freedom dynamics. To gain robustness a non-linear control approach has been preferred: more specifically the Lyapunov's second stability theorem has been exploited, being a very powerful tool to guarantee asymptotic stability of the controlled dynamics. The dynamics of Vega's actuators has also been taken into account. The system performance has been checked and analyzed by numerical simulations run on real mission data for different operational and configuration scenarios, and the effectiveness of the synthesized control highlighted: in particular scenarios including a wide range of composite's inertial configurations performing various typologies of maneuvers have been run. The robustness of the controlled dynamics has been validated by 100 cases Monte Carlo analysis campaign: the containment of the dispersion for the controlled variables - say the composite roll, yaw and pitch angles - confirmed the wide validity and generality of the proposed control law. This paper will show the theoretical approach and discuss the obtained results.

Robust control of systems with real parameter uncertainty and unmodelled dynamics

NASA Technical Reports Server (NTRS)

Chang, Bor-Chin; Fischl, Robert

1991-01-01

During this research period we have made significant progress in the four proposed areas: (1) design of robust controllers via H infinity optimization; (2) design of robust controllers via mixed H2/H infinity optimization; (3) M-delta structure and robust stability analysis for structured uncertainties; and (4) a study on controllability and observability of perturbed plant. It is well known now that the two-Riccati-equation solution to the H infinity control problem can be used to characterize all possible stabilizing optimal or suboptimal H infinity controllers if the optimal H infinity norm or gamma, an upper bound of a suboptimal H infinity norm, is given. In this research, we discovered some useful properties of these H infinity Riccati solutions. Among them, the most prominent one is that the spectral radius of the product of these two Riccati solutions is a continuous, nonincreasing, convex function of gamma in the domain of interest. Based on these properties, quadratically convergent algorithms are developed to compute the optimal H infinity norm. We also set up a detailed procedure for applying the H infinity theory to robust control systems design. The desire to design controllers with H infinity robustness but H(exp 2) performance has recently resulted in mixed H(exp 2) and H infinity control problem formulation. The mixed H(exp 2)/H infinity problem have drawn the attention of many investigators. However, solution is only available for special cases of this problem. We formulated a relatively realistic control problem with H(exp 2) performance index and H infinity robustness constraint into a more general mixed H(exp 2)/H infinity problem. No optimal solution yet is available for this more general mixed H(exp 2)/H infinity problem. Although the optimal solution for this mixed H(exp 2)/H infinity control has not yet been found, we proposed a design approach which can be used through proper choice of the available design parameters to influence both robustness and performance. For a large class of linear time-invariant systems with real parametric perturbations, the coefficient vector of the characteristic polynomial is a multilinear function of the real parameter vector. Based on this multilinear mapping relationship together with the recent developments for polytopic polynomials and parameter domain partition technique, we proposed an iterative algorithm for coupling the real structured singular value.

Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output

DOE PAGES

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

2017-09-10

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

Robust Economic Control Decision Method of Uncertain System on Urban Domestic Water Supply.

PubMed

Li, Kebai; Ma, Tianyi; Wei, Guo

2018-03-31

As China quickly urbanizes, urban domestic water generally presents the circumstances of both rising tendency and seasonal cycle fluctuation. A robust economic control decision method for dynamic uncertain systems is proposed in this paper. It is developed based on the internal model principle and pole allocation method, and it is applied to an urban domestic water supply system with rising tendency and seasonal cycle fluctuation. To achieve this goal, first a multiplicative model is used to describe the urban domestic water demand. Then, a capital stock and a labor stock are selected as the state vector, and the investment and labor are designed as the control vector. Next, the compensator subsystem is devised in light of the internal model principle. Finally, by using the state feedback control strategy and pole allocation method, the multivariable robust economic control decision method is implemented. The implementation with this model can accomplish the urban domestic water supply control goal, with the robustness for the variation of parameters. The methodology presented in this study may be applied to the water management system in other parts of the world, provided all data used in this study are available. The robust control decision method in this paper is also applicable to deal with tracking control problems as well as stabilization control problems of other general dynamic uncertain systems.

Robust Economic Control Decision Method of Uncertain System on Urban Domestic Water Supply

PubMed Central

Li, Kebai; Ma, Tianyi; Wei, Guo

2018-01-01

As China quickly urbanizes, urban domestic water generally presents the circumstances of both rising tendency and seasonal cycle fluctuation. A robust economic control decision method for dynamic uncertain systems is proposed in this paper. It is developed based on the internal model principle and pole allocation method, and it is applied to an urban domestic water supply system with rising tendency and seasonal cycle fluctuation. To achieve this goal, first a multiplicative model is used to describe the urban domestic water demand. Then, a capital stock and a labor stock are selected as the state vector, and the investment and labor are designed as the control vector. Next, the compensator subsystem is devised in light of the internal model principle. Finally, by using the state feedback control strategy and pole allocation method, the multivariable robust economic control decision method is implemented. The implementation with this model can accomplish the urban domestic water supply control goal, with the robustness for the variation of parameters. The methodology presented in this study may be applied to the water management system in other parts of the world, provided all data used in this study are available. The robust control decision method in this paper is also applicable to deal with tracking control problems as well as stabilization control problems of other general dynamic uncertain systems. PMID:29614749

Going, going, gone: predicting the fate of genomic insertions in plant RNA viruses.

PubMed

Willemsen, Anouk; Carrasco, José L; Elena, Santiago F; Zwart, Mark P

2018-05-10

Horizontal gene transfer is common among viruses, while they also have highly compact genomes and tend to lose artificial genomic insertions rapidly. Understanding the stability of genomic insertions in viral genomes is therefore relevant for explaining and predicting their evolutionary patterns. Here, we revisit a large body of experimental research on a plant RNA virus, tobacco etch potyvirus (TEV), to identify the patterns underlying the stability of a range of homologous and heterologous insertions in the viral genome. We obtained a wide range of estimates for the recombination rate-the rate at which deletions removing the insertion occur-and these appeared to be independent of the type of insertion and its location. Of the factors we considered, recombination rate was the best predictor of insertion stability, although we could not identify the specific sequence characteristics that would help predict insertion instability. We also considered experimentally the possibility that functional insertions lead to higher mutational robustness through increased redundancy. However, our observations suggest that both functional and non-functional increases in genome size decreased the mutational robustness. Our results therefore demonstrate the importance of recombination rates for predicting the long-term stability and evolution of viral RNA genomes and suggest that there are unexpected drawbacks to increases in genome size for mutational robustness.

microRNA as a Potential Vector for the Propagation of Robustness in Protein Expression and Oscillatory Dynamics within a ceRNA Network

PubMed Central

Gérard, Claude; Novák, Béla

2013-01-01

microRNAs (miRNAs) are small noncoding RNAs that are important post-transcriptional regulators of gene expression. miRNAs can induce thresholds in protein synthesis. Such thresholds in protein output can be also achieved by oligomerization of transcription factors (TF) for the control of gene expression. First, we propose a minimal model for protein expression regulated by miRNA and by oligomerization of TF. We show that miRNA and oligomerization of TF generate a buffer, which increases the robustness of protein output towards molecular noise as well as towards random variation of kinetics parameters. Next, we extend the model by considering that the same miRNA can bind to multiple messenger RNAs, which accounts for the dynamics of a minimal competing endogenous RNAs (ceRNAs) network. The model shows that, through common miRNA regulation, TF can control the expression of all proteins formed by the ceRNA network, even if it drives the expression of only one gene in the network. The model further suggests that the threshold in protein synthesis mediated by the oligomerization of TF can be propagated to the other genes, which can increase the robustness of the expression of all genes in such ceRNA network. Furthermore, we show that a miRNA could increase the time delay of a “Goodwin-like” oscillator model, which may favor the occurrence of oscillations of large amplitude. This result predicts important roles of miRNAs in the control of the molecular mechanisms leading to the emergence of biological rhythms. Moreover, a model for the latter oscillator embedded in a ceRNA network indicates that the oscillatory behavior can be propagated, via the shared miRNA, to all proteins formed by such ceRNA network. Thus, by means of computational models, we show that miRNAs could act as vectors allowing the propagation of robustness in protein synthesis as well as oscillatory behaviors within ceRNA networks. PMID:24376695

Robust fuzzy control subject to state variance and passivity constraints for perturbed nonlinear systems with multiplicative noises.

PubMed

Chang, Wen-Jer; Huang, Bo-Jyun

2014-11-01

The multi-constrained robust fuzzy control problem is investigated in this paper for perturbed continuous-time nonlinear stochastic systems. The nonlinear system considered in this paper is represented by a Takagi-Sugeno fuzzy model with perturbations and state multiplicative noises. The multiple performance constraints considered in this paper include stability, passivity and individual state variance constraints. The Lyapunov stability theory is employed to derive sufficient conditions to achieve the above performance constraints. By solving these sufficient conditions, the contribution of this paper is to develop a parallel distributed compensation based robust fuzzy control approach to satisfy multiple performance constraints for perturbed nonlinear systems with multiplicative noises. At last, a numerical example for the control of perturbed inverted pendulum system is provided to illustrate the applicability and effectiveness of the proposed multi-constrained robust fuzzy control method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Large-scale linear programs in planning and prediction.

DOT National Transportation Integrated Search

2017-06-01

Large-scale linear programs are at the core of many traffic-related optimization problems in both planning and prediction. Moreover, many of these involve significant uncertainty, and hence are modeled using either chance constraints, or robust optim...

Efficient Robust Optimization of Metal Forming Processes using a Sequential Metamodel Based Strategy

NASA Astrophysics Data System (ADS)

Wiebenga, J. H.; Klaseboer, G.; van den Boogaard, A. H.

2011-08-01

The coupling of Finite Element (FE) simulations to mathematical optimization techniques has contributed significantly to product improvements and cost reductions in the metal forming industries. The next challenge is to bridge the gap between deterministic optimization techniques and the industrial need for robustness. This paper introduces a new and generally applicable structured methodology for modeling and solving robust optimization problems. Stochastic design variables or noise variables are taken into account explicitly in the optimization procedure. The metamodel-based strategy is combined with a sequential improvement algorithm to efficiently increase the accuracy of the objective function prediction. This is only done at regions of interest containing the optimal robust design. Application of the methodology to an industrial V-bending process resulted in valuable process insights and an improved robust process design. Moreover, a significant improvement of the robustness (>2σ) was obtained by minimizing the deteriorating effects of several noise variables. The robust optimization results demonstrate the general applicability of the robust optimization strategy and underline the importance of including uncertainty and robustness explicitly in the numerical optimization procedure.

Robust control of seismically excited cable stayed bridges with MR dampers

NASA Astrophysics Data System (ADS)

YeganehFallah, Arash; Khajeh Ahamd Attari, Nader

2017-03-01

In recent decades active and semi-active structural control are becoming attractive alternatives for enhancing performance of civil infrastructures subjected to seismic and winds loads. However, in order to have reliable active and semi-active control, there is a need to include information of uncertainties in design of the controller. In real world for civil structures, parameters such as loading places, stiffness, mass and damping are time variant and uncertain. These uncertainties in many cases model as parametric uncertainties. The motivation of this research is to design a robust controller for attenuating the vibrational responses of civil infrastructures, regarding their dynamical uncertainties. Uncertainties in structural dynamic’s parameters are modeled as affine uncertainties in state space modeling. These uncertainties are decoupled from the system through Linear Fractional Transformation (LFT) and are assumed to be unknown input to the system but norm bounded. The robust H ∞ controller is designed for the decoupled system to regulate the evaluation outputs and it is robust to effects of uncertainties, disturbance and sensors noise. The cable stayed bridge benchmark which is equipped with MR damper is considered for the numerical simulation. The simulated results show that the proposed robust controller can effectively mitigate undesired uncertainties effects on systems’ responds under seismic loading.

A Low-Power Wireless Image Sensor Node with Noise-Robust Moving Object Detection and a Region-of-Interest Based Rate Controller

DTIC Science & Technology

2017-03-01

A Low- Power Wireless Image Sensor Node with Noise-Robust Moving Object Detection and a Region-of-Interest Based Rate Controller Jong Hwan Ko...Atlanta, GA 30332 USA Contact Author Email: jonghwan.ko@gatech.edu Abstract: This paper presents a low- power wireless image sensor node for...present a low- power wireless image sensor node with a noise-robust moving object detection and region-of-interest based rate controller [Fig. 1]. The

Robust H ∞ Control for Spacecraft Rendezvous with a Noncooperative Target

PubMed Central

Wu, Shu-Nan; Zhou, Wen-Ya; Tan, Shu-Jun; Wu, Guo-Qiang

2013-01-01

The robust H ∞ control for spacecraft rendezvous with a noncooperative target is addressed in this paper. The relative motion of chaser and noncooperative target is firstly modeled as the uncertain system, which contains uncertain orbit parameter and mass. Then the H ∞ performance and finite time performance are proposed, and a robust H ∞ controller is developed to drive the chaser to rendezvous with the non-cooperative target in the presence of control input saturation, measurement error, and thrust error. The linear matrix inequality technology is used to derive the sufficient condition of the proposed controller. An illustrative example is finally provided to demonstrate the effectiveness of the controller. PMID:24027446

Gust prediction via artificial hair sensor array and neural network

NASA Astrophysics Data System (ADS)

Pankonien, Alexander M.; Thapa Magar, Kaman S.; Beblo, Richard V.; Reich, Gregory W.

2017-04-01

Gust Load Alleviation (GLA) is an important aspect of flight dynamics and control that reduces structural loadings and enhances ride quality. In conventional GLA systems, the structural response to aerodynamic excitation informs the control scheme. A phase lag, imposed by inertia, between the excitation and the measurement inherently limits the effectiveness of these systems. Hence, direct measurement of the aerodynamic loading can eliminate this lag, providing valuable information for effective GLA system design. Distributed arrays of Artificial Hair Sensors (AHS) are ideal for surface flow measurements that can be used to predict other necessary parameters such as aerodynamic forces, moments, and turbulence. In previous work, the spatially distributed surface flow velocities obtained from an array of artificial hair sensors using a Single-State (or feedforward) Neural Network were found to be effective in estimating the steady aerodynamic parameters such as air speed, angle of attack, lift and moment coefficient. This paper extends the investigation of the same configuration to unsteady force and moment estimation, which is important for active GLA control design. Implementing a Recurrent Neural Network that includes previous-timestep sensor information, the hair sensor array is shown to be capable of capturing gust disturbances with a wide range of periods, reducing predictive error in lift and moment by 68% and 52% respectively. The L2 norms of the first layer of the weight matrices were compared showing a 23% emphasis on prior versus current information. The Recurrent architecture also improves robustness, exhibiting only a 30% increase in predictive error when undertrained as compared to a 170% increase by the Single-State NN. This diverse, localized information can thus be directly implemented into a control scheme that alleviates the gusts without waiting for a structural response or requiring user-intensive sensor calibration.

Replicability and Robustness of GWAS for Behavioral Traits

PubMed Central

Rietveld, Cornelius A.; Conley, Dalton; Eriksson, Nicholas; Esko, Tõnu; Medland, Sarah E.; Vinkhuyzen, Anna A.E.; Yang, Jian; Boardman, Jason D.; Chabris, Christopher F.; Dawes, Christopher T.; Domingue, Benjamin W.; Hinds, David A.; Johannesson, Magnus; Kiefer, Amy K.; Laibson, David; Magnusson, Patrik K. E.; Mountain, Joanna L.; Oskarsson, Sven; Rostapshova, Olga; Teumer, Alexander; Tung, Joyce Y.; Visscher, Peter M.; Benjamin, Daniel J.; Cesarini, David; Koellinger, Philipp D.

2015-01-01

A recent genome-wide association study (GWAS) of educational attainment identified three single-nucleotide polymorphisms (SNPs) that, despite their small effect sizes (each R2 ≈ 0.02%), reached genome-wide significance (p < 5×10−8) in a large discovery sample and replicated in an independent sample (p < 0.05). The study also reported associations between educational attainment and indices of SNPs called “polygenic scores.” We evaluate the robustness of these findings. Study 1 finds that all three SNPs replicate in another large (N = 34,428) independent sample. We also find that the scores remain predictive (R2 ≈ 2%) with stringent controls for stratification (Study 2) and in new within-family analyses (Study 3). Our results show that large and therefore well-powered GWASs can identify replicable genetic associations with behavioral traits. The small effect sizes of individual SNPs are likely to be a major contributing explanation for the striking contrast between our results and the disappointing replication record of most candidate gene studies. PMID:25287667

ELUCIDATING BRAIN CONNECTIVITY NETWORKS IN MAJOR DEPRESSIVE DISORDER USING CLASSIFICATION-BASED SCORING.

PubMed

Sacchet, Matthew D; Prasad, Gautam; Foland-Ross, Lara C; Thompson, Paul M; Gotlib, Ian H

2014-04-01

Graph theory is increasingly used in the field of neuroscience to understand the large-scale network structure of the human brain. There is also considerable interest in applying machine learning techniques in clinical settings, for example, to make diagnoses or predict treatment outcomes. Here we used support-vector machines (SVMs), in conjunction with whole-brain tractography, to identify graph metrics that best differentiate individuals with Major Depressive Disorder (MDD) from nondepressed controls. To do this, we applied a novel feature-scoring procedure that incorporates iterative classifier performance to assess feature robustness. We found that small-worldness , a measure of the balance between global integration and local specialization, most reliably differentiated MDD from nondepressed individuals. Post-hoc regional analyses suggested that heightened connectivity of the subcallosal cingulate gyrus (SCG) in MDDs contributes to these differences. The current study provides a novel way to assess the robustness of classification features and reveals anomalies in large-scale neural networks in MDD.

Childhood emotional maltreatment as a robust predictor of suicidal ideation: A multi-wave, prospective investigation

PubMed Central

Miller, Adam Bryant; Jenness, Jessica L.; Oppenheimer, Caroline W.; Barrocas Gottleib, Andrea L.; Young, Jami F.; Hankin, Benjamin L.

2016-01-01

Despite literature suggesting a relationship between child maltreatment and suicidal ideation, few studies have examined the prospective course of this relationship. The current study examined this relationship in a sample of 682 community youth who were followed over the course of 3 years. Repeated measures of suicidal ideation, emotional maltreatment, and depressive symptom severity were examined in multi-wave path analysis models. Overall, results suggest that emotional maltreatment over time contributes uniquely to the prospective prediction of suicidal ideation, even when controlling for age, previous suicidal ideation, biological sex, and depression symptom severity. Unlike previous studies that have only measured emotional maltreatment at one-time point, the current study demonstrates that emotional maltreatment contributes unique risk to suicidal ideation prospectively among youth. Results speak to the importance of examining emotional maltreatment and suicidal ideation within prospective models of risk and suggest that emotional maltreatment is a robust predictor of suicidal ideation, over and above history of suicidal ideation and depression. PMID:27032784

T2N as a new tool for robust electrophysiological modeling demonstrated for mature and adult-born dentate granule cells

PubMed Central

Mongiat, Lucas Alberto; Schwarzacher, Stephan Wolfgang

2017-01-01

Compartmental models are the theoretical tool of choice for understanding single neuron computations. However, many models are incomplete, built ad hoc and require tuning for each novel condition rendering them of limited usability. Here, we present T2N, a powerful interface to control NEURON with Matlab and TREES toolbox, which supports generating models stable over a broad range of reconstructed and synthetic morphologies. We illustrate this for a novel, highly detailed active model of dentate granule cells (GCs) replicating a wide palette of experiments from various labs. By implementing known differences in ion channel composition and morphology, our model reproduces data from mouse or rat, mature or adult-born GCs as well as pharmacological interventions and epileptic conditions. This work sets a new benchmark for detailed compartmental modeling. T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions. We discuss possible T2N application in degeneracy studies. PMID:29165247

Silicon quantum processor with robust long-distance qubit couplings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tosi, Guilherme; Mohiyaddin, Fahd A.; Schmitt, Vivien

Practical quantum computers require a large network of highly coherent qubits, interconnected in a design robust against errors. Donor spins in silicon provide state-of-the-art coherence and quantum gate fidelities, in a platform adapted from industrial semiconductor processing. Here we present a scalable design for a silicon quantum processor that does not require precise donor placement and leaves ample space for the routing of interconnects and readout devices. We introduce the flip-flop qubit, a combination of the electron-nuclear spin states of a phosphorus donor that can be controlled by microwave electric fields. Two-qubit gates exploit a second-order electric dipole-dipole interaction, allowingmore » selective coupling beyond the nearest-neighbor, at separations of hundreds of nanometers, while microwave resonators can extend the entanglement to macroscopic distances. We predict gate fidelities within fault-tolerance thresholds using realistic noise models. This design provides a realizable blueprint for scalable spin-based quantum computers in silicon.« less

Robust quantum logic in neutral atoms via adiabatic Rydberg dressing

DOE PAGES

Keating, Tyler; Cook, Robert L.; Hankin, Aaron M.; ...

2015-01-28

We study a scheme for implementing a controlled-Z (CZ) gate between two neutral-atom qubits based on the Rydberg blockade mechanism in a manner that is robust to errors caused by atomic motion. By employing adiabatic dressing of the ground electronic state, we can protect the gate from decoherence due to random phase errors that typically arise because of atomic thermal motion. In addition, the adiabatic protocol allows for a Doppler-free configuration that involves counterpropagating lasers in a σ +/σ - orthogonal polarization geometry that further reduces motional errors due to Doppler shifts. The residual motional error is dominated by dipole-dipolemore » forces acting on doubly-excited Rydberg atoms when the blockade is imperfect. As a result, for reasonable parameters, with qubits encoded into the clock states of 133Cs, we predict that our protocol could produce a CZ gate in < 10 μs with error probability on the order of 10 -3.« less

Quantitative impurity analysis of monoclonal antibody size heterogeneity by CE-LIF: example of development and validation through a quality-by-design framework.

PubMed

Michels, David A; Parker, Monica; Salas-Solano, Oscar

2012-03-01

This paper describes the framework of quality by design applied to the development, optimization and validation of a sensitive capillary electrophoresis-sodium dodecyl sulfate (CE-SDS) assay for monitoring impurities that potentially impact drug efficacy or patient safety produced in the manufacture of therapeutic MAb products. Drug substance or drug product samples are derivatized with fluorogenic 3-(2-furoyl)quinoline-2-carboxaldehyde and nucleophilic cyanide before separation by CE-SDS coupled to LIF detection. Three design-of-experiments enabled critical labeling parameters to meet method requirements for detecting minor impurities while building precision and robustness into the assay during development. The screening design predicted optimal conditions to control labeling artifacts while two full factorial designs demonstrated method robustness through control of temperature and cyanide parameters within the normal operating range. Subsequent validation according to the guidelines of the International Committee of Harmonization showed the CE-SDS/LIF assay was specific, accurate, and precise (RSD ≤ 0.8%) for relative peak distribution and linear (R > 0.997) between the range of 0.5-1.5 mg/mL with LOD and LOQ of 10 ng/mL and 35 ng/mL, respectively. Validation confirmed the system suitability criteria used as a level of control to ensure reliable method performance. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation

PubMed Central

Carlson, Jean M.; Alderson, David L.; Stromberg, Sean P.; Bassett, Danielle S.; Craparo, Emily M.; Guiterrez-Villarreal, Francisco; Otani, Thomas

2014-01-01

Identifying and quantifying factors influencing human decision making remains an outstanding challenge, impacting the performance and predictability of social and technological systems. In many cases, system failures are traced to human factors including congestion, overload, miscommunication, and delays. Here we report results of a behavioral network science experiment, targeting decision making in a natural disaster. In a controlled laboratory setting, our results quantify several key factors influencing individual evacuation decision making in a controlled laboratory setting. The experiment includes tensions between broadcast and peer-to-peer information, and contrasts the effects of temporal urgency associated with the imminence of the disaster and the effects of limited shelter capacity for evacuees. Based on empirical measurements of the cumulative rate of evacuations as a function of the instantaneous disaster likelihood, we develop a quantitative model for decision making that captures remarkably well the main features of observed collective behavior across many different scenarios. Moreover, this model captures the sensitivity of individual- and population-level decision behaviors to external pressures, and systematic deviations from the model provide meaningful estimates of variability in the collective response. Identification of robust methods for quantifying human decisions in the face of risk has implications for policy in disasters and other threat scenarios, specifically the development and testing of robust strategies for training and control of evacuations that account for human behavior and network topologies. PMID:24520331

Accurate and sensitive quantification of protein-DNA binding affinity.

PubMed

Rastogi, Chaitanya; Rube, H Tomas; Kribelbauer, Judith F; Crocker, Justin; Loker, Ryan E; Martini, Gabriella D; Laptenko, Oleg; Freed-Pastor, William A; Prives, Carol; Stern, David L; Mann, Richard S; Bussemaker, Harmen J

2018-04-17

Transcription factors (TFs) control gene expression by binding to genomic DNA in a sequence-specific manner. Mutations in TF binding sites are increasingly found to be associated with human disease, yet we currently lack robust methods to predict these sites. Here, we developed a versatile maximum likelihood framework named No Read Left Behind (NRLB) that infers a biophysical model of protein-DNA recognition across the full affinity range from a library of in vitro selected DNA binding sites. NRLB predicts human Max homodimer binding in near-perfect agreement with existing low-throughput measurements. It can capture the specificity of the p53 tetramer and distinguish multiple binding modes within a single sample. Additionally, we confirm that newly identified low-affinity enhancer binding sites are functional in vivo, and that their contribution to gene expression matches their predicted affinity. Our results establish a powerful paradigm for identifying protein binding sites and interpreting gene regulatory sequences in eukaryotic genomes. Copyright © 2018 the Author(s). Published by PNAS.

Accurate and sensitive quantification of protein-DNA binding affinity

PubMed Central

Rastogi, Chaitanya; Rube, H. Tomas; Kribelbauer, Judith F.; Crocker, Justin; Loker, Ryan E.; Martini, Gabriella D.; Laptenko, Oleg; Freed-Pastor, William A.; Prives, Carol; Stern, David L.; Mann, Richard S.; Bussemaker, Harmen J.

2018-01-01

Transcription factors (TFs) control gene expression by binding to genomic DNA in a sequence-specific manner. Mutations in TF binding sites are increasingly found to be associated with human disease, yet we currently lack robust methods to predict these sites. Here, we developed a versatile maximum likelihood framework named No Read Left Behind (NRLB) that infers a biophysical model of protein-DNA recognition across the full affinity range from a library of in vitro selected DNA binding sites. NRLB predicts human Max homodimer binding in near-perfect agreement with existing low-throughput measurements. It can capture the specificity of the p53 tetramer and distinguish multiple binding modes within a single sample. Additionally, we confirm that newly identified low-affinity enhancer binding sites are functional in vivo, and that their contribution to gene expression matches their predicted affinity. Our results establish a powerful paradigm for identifying protein binding sites and interpreting gene regulatory sequences in eukaryotic genomes. PMID:29610332

Expression of anger and ill health in two cultures: an examination of inflammation and cardiovascular risk.

PubMed

Kitayama, Shinobu; Park, Jiyoung; Boylan, Jennifer Morozink; Miyamoto, Yuri; Levine, Cynthia S; Markus, Hazel Rose; Karasawa, Mayumi; Coe, Christopher L; Kawakami, Norito; Love, Gayle D; Ryff, Carol D

2015-02-01

Expression of anger is associated with biological health risk (BHR) in Western cultures. However, recent evidence documenting culturally divergent functions of the expression of anger suggests that its link with BHR may be moderated by culture. To test this prediction, we examined large probability samples of both Japanese and Americans using multiple measures of BHR, including pro-inflammatory markers (interleukin-6 and C-reactive protein) and indices of cardiovascular malfunction (systolic blood pressure and ratio of total to HDL cholesterol). We found that the link between greater expression of anger and increased BHR was robust for Americans. As predicted, however, this association was diametrically reversed for Japanese, among whom greater expression of anger predicted reduced BHR. These patterns were unique to the expressive facet of anger and remained after we controlled for age, gender, health status, health behaviors, social status, and reported experience of negative emotions. Implications for sociocultural modulation of bio-physiological responses are discussed. © The Author(s) 2015.

Anger Expression and Ill-Health in Two Cultures: An Examination of Inflammation and Cardiovascular Risk

PubMed Central

Kitayama, Shinobu; Park, Jiyoung; Boylan, Jennifer Morozink; Miyamoto, Yuri; Levine, Cynthia S.; Markus, Hazel Rose; Karasawa, Mayumi; Coe, Christopher L.; Kawakami, Norito; Love, Gayle D.; Ryff, Carol D.

2014-01-01

Expression of anger is associated with biological health risk (BHR) in Western cultures. However, recent evidence documenting culturally divergent functions of anger expression suggests that the link between anger expression and BHR may be moderated by culture. To test this prediction, we examined large probability samples of both Japanese and Americans with multiple measures of BHR including pro-inflammatory markers (Interleukin-6 and C-reactive protein) and indices of cardiovascular malfunction (systolic blood pressure and Total/HDL cholesterol ratio). We found that the positive link between anger expression and increased BHR was robust for Americans. As predicted, however, this association was diametrically reversed for Japanese, with anger expression predicting reduced BHR. The pattern was unique to the expressive facet of anger and remained after controlling for age, gender, health status, health behaviors, social status, and reported experience of negative emotions. Implications for socio-cultural modulation of bio-physiological responses are discussed. PMID:25564521

Self-Concept Predicts Academic Achievement Across Levels of the Achievement Distribution: Domain Specificity for Math and Reading.

PubMed

Susperreguy, Maria Ines; Davis-Kean, Pamela E; Duckworth, Kathryn; Chen, Meichu

2017-09-18

This study examines whether self-concept of ability in math and reading predicts later math and reading attainment across different levels of achievement. Data from three large-scale longitudinal data sets, the Avon Longitudinal Study of Parents and Children, National Institute of Child Health and Human Development-Study of Early Child Care and Youth Development, and Panel Study of Income Dynamics-Child Development Supplement, were used to answer this question by employing quantile regression analyses. After controlling for demographic variables, child characteristics, and early ability, the findings indicate that self-concept of ability in math and reading predicts later achievement in each respective domain across all quantile levels of achievement. These results were replicated across the three data sets representing different populations and provide robust evidence for the role of self-concept of ability in understanding achievement from early childhood to adolescence across the spectrum of performance (low to high). © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.

The Role of microRNA Markers in the Diagnosis, Treatment, and Outcome Prediction of Spinal Cord Injury.

PubMed

Martirosyan, Nikolay L; Carotenuto, Alessandro; Patel, Arpan A; Kalani, M Yashar S; Yagmurlu, Kaan; Lemole, G Michael; Preul, Mark C; Theodore, Nicholas

2016-01-01

Spinal cord injury (SCI) is a devastating condition that affects many people worldwide. Treatment focuses on controlling secondary injury cascade and improving regeneration. It has recently been suggested that both the secondary injury cascade and the regenerative process are heavily regulated by microRNAs (miRNAs). The measurement of specific biomarkers could improve our understanding of the disease processes, and thereby provide clinicians with the opportunity to guide treatment and predict clinical outcomes after SCI. A variety of miRNAs exhibit important roles in processes of inflammation, cell death, and regeneration. These miRNAs can be used as diagnostic tools for predicting outcome after SCI. In addition, miRNAs can be used in the treatment of SCI and its symptoms. Significant laboratory and clinical evidence exist to show that miRNAs could be used as robust diagnostic and therapeutic tools for the treatment of patients with SCI. Further clinical studies are warranted to clarify the importance of each subtype of miRNA in SCI management.

Predictive Scheduling for Electric Vehicles Considering Uncertainty of Load and User Behaviors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Bin; Huang, Rui; Wang, Yubo

2016-05-02

Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimizationmore » module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.« less

Evaluation of procedures for prediction of unconventional gas in the presence of geologic trends

USGS Publications Warehouse

Attanasi, E.D.; Coburn, T.C.

2009-01-01

This study extends the application of local spatial nonparametric prediction models to the estimation of recoverable gas volumes in continuous-type gas plays to regimes where there is a single geologic trend. A transformation is presented, originally proposed by Tomczak, that offsets the distortions caused by the trend. This article reports on numerical experiments that compare predictive and classification performance of the local nonparametric prediction models based on the transformation with models based on Euclidean distance. The transformation offers improvement in average root mean square error when the trend is not severely misspecified. Because of the local nature of the models, even those based on Euclidean distance in the presence of trends are reasonably robust. The tests based on other model performance metrics such as prediction error associated with the high-grade tracts and the ability of the models to identify sites with the largest gas volumes also demonstrate the robustness of both local modeling approaches. ?? International Association for Mathematical Geology 2009.

Control and prediction for blackouts caused by frequency collapse in smart grids.

PubMed

Wang, Chengwei; Grebogi, Celso; Baptista, Murilo S

2016-09-01

The electric power system is one of the cornerstones of modern society. One of its most serious malfunctions is the blackout, a catastrophic event that may disrupt a substantial portion of the system, playing havoc to human life and causing great economic losses. Thus, understanding the mechanisms leading to blackouts and creating a reliable and resilient power grid has been a major issue, attracting the attention of scientists, engineers, and stakeholders. In this paper, we study the blackout problem in power grids by considering a practical phase-oscillator model. This model allows one to simultaneously consider different types of power sources (e.g., traditional AC power plants and renewable power sources connected by DC/AC inverters) and different types of loads (e.g., consumers connected to distribution networks and consumers directly connected to power plants). We propose two new control strategies based on our model, one for traditional power grids and another one for smart grids. The control strategies show the efficient function of the fast-response energy storage systems in preventing and predicting blackouts in smart grids. This work provides innovative ideas which help us to build up a robuster and more economic smart power system.

The Effect of Paternal Age on Offspring Intelligence and Personality when Controlling for Parental Trait Levels

PubMed Central

Arslan, Ruben C.; Penke, Lars; Johnson, Wendy; Iacono, William G.; McGue, Matt

2014-01-01

Paternal age at conception has been found to predict the number of new genetic mutations. We examined the effect of father’s age at birth on offspring intelligence, head circumference and personality traits. Using the Minnesota Twin Family Study sample we tested paternal age effects while controlling for parents’ trait levels measured with the same precision as offspring’s. From evolutionary genetic considerations we predicted a negative effect of paternal age on offspring intelligence, but not on other traits. Controlling for parental intelligence (IQ) had the effect of turning an initially positive association non-significantly negative. We found paternal age effects on offspring IQ and Multidimensional Personality Questionnaire Absorption, but they were not robustly significant, nor replicable with additional covariates. No other noteworthy effects were found. Parents’ intelligence and personality correlated with their ages at twin birth, which may have obscured a small negative effect of advanced paternal age (<1% of variance explained) on intelligence. We discuss future avenues for studies of paternal age effects and suggest that stronger research designs are needed to rule out confounding factors involving birth order and the Flynn effect. PMID:24587224

Control and prediction for blackouts caused by frequency collapse in smart grids

NASA Astrophysics Data System (ADS)

Wang, Chengwei; Grebogi, Celso; Baptista, Murilo S.

2016-09-01

The electric power system is one of the cornerstones of modern society. One of its most serious malfunctions is the blackout, a catastrophic event that may disrupt a substantial portion of the system, playing havoc to human life and causing great economic losses. Thus, understanding the mechanisms leading to blackouts and creating a reliable and resilient power grid has been a major issue, attracting the attention of scientists, engineers, and stakeholders. In this paper, we study the blackout problem in power grids by considering a practical phase-oscillator model. This model allows one to simultaneously consider different types of power sources (e.g., traditional AC power plants and renewable power sources connected by DC/AC inverters) and different types of loads (e.g., consumers connected to distribution networks and consumers directly connected to power plants). We propose two new control strategies based on our model, one for traditional power grids and another one for smart grids. The control strategies show the efficient function of the fast-response energy storage systems in preventing and predicting blackouts in smart grids. This work provides innovative ideas which help us to build up a robuster and more economic smart power system.

Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks.

PubMed

Flassig, R J; Sundmacher, K

2012-12-01

Biochemical reaction networks in the form of coupled ordinary differential equations (ODEs) provide a powerful modeling tool for understanding the dynamics of biochemical processes. During the early phase of modeling, scientists have to deal with a large pool of competing nonlinear models. At this point, discrimination experiments can be designed and conducted to obtain optimal data for selecting the most plausible model. Since biological ODE models have widely distributed parameters due to, e.g. biologic variability or experimental variations, model responses become distributed. Therefore, a robust optimal experimental design (OED) for model discrimination can be used to discriminate models based on their response probability distribution functions (PDFs). In this work, we present an optimal control-based methodology for designing optimal stimulus experiments aimed at robust model discrimination. For estimating the time-varying model response PDF, which results from the nonlinear propagation of the parameter PDF under the ODE dynamics, we suggest using the sigma-point approach. Using the model overlap (expected likelihood) as a robust discrimination criterion to measure dissimilarities between expected model response PDFs, we benchmark the proposed nonlinear design approach against linearization with respect to prediction accuracy and design quality for two nonlinear biological reaction networks. As shown, the sigma-point outperforms the linearization approach in the case of widely distributed parameter sets and/or existing multiple steady states. Since the sigma-point approach scales linearly with the number of model parameter, it can be applied to large systems for robust experimental planning. An implementation of the method in MATLAB/AMPL is available at http://www.uni-magdeburg.de/ivt/svt/person/rf/roed.html. flassig@mpi-magdeburg.mpg.de Supplementary data are are available at Bioinformatics online.

Increased robustness of single-molecule counting with microfluidics, digital isothermal amplification, and a mobile phone versus real-time kinetic measurements.

PubMed

Selck, David A; Karymov, Mikhail A; Sun, Bing; Ismagilov, Rustem F

2013-11-19

Quantitative bioanalytical measurements are commonly performed in a kinetic format and are known to not be robust to perturbation that affects the kinetics itself or the measurement of kinetics. We hypothesized that the same measurements performed in a "digital" (single-molecule) format would show increased robustness to such perturbations. Here, we investigated the robustness of an amplification reaction (reverse-transcription loop-mediated amplification, RT-LAMP) in the context of fluctuations in temperature and time when this reaction is used for quantitative measurements of HIV-1 RNA molecules under limited-resource settings (LRS). The digital format that counts molecules using dRT-LAMP chemistry detected a 2-fold change in concentration of HIV-1 RNA despite a 6 °C temperature variation (p-value = 6.7 × 10(-7)), whereas the traditional kinetic (real-time) format did not (p-value = 0.25). Digital analysis was also robust to a 20 min change in reaction time, to poor imaging conditions obtained with a consumer cell-phone camera, and to automated cloud-based processing of these images (R(2) = 0.9997 vs true counts over a 100-fold dynamic range). Fluorescent output of multiplexed PCR amplification could also be imaged with the cell phone camera using flash as the excitation source. Many nonlinear amplification schemes based on organic, inorganic, and biochemical reactions have been developed, but their robustness is not well understood. This work implies that these chemistries may be significantly more robust in the digital, rather than kinetic, format. It also calls for theoretical studies to predict robustness of these chemistries and, more generally, to design robust reaction architectures. The SlipChip that we used here and other digital microfluidic technologies already exist to enable testing of these predictions. Such work may lead to identification or creation of robust amplification chemistries that enable rapid and precise quantitative molecular measurements under LRS. Furthermore, it may provide more general principles describing robustness of chemical and biological networks in digital formats.

Robust geostatistical analysis of spatial data

NASA Astrophysics Data System (ADS)

Papritz, Andreas; Künsch, Hans Rudolf; Schwierz, Cornelia; Stahel, Werner A.

2013-04-01

Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outliers affect the modelling of the large-scale spatial trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation (Welsh and Richardson, 1997). Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and non-sampled locations and kriging variances. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis a data set on heavy metal contamination of the soil in the vicinity of a metal smelter. Marchant, B.P. and Lark, R.M. 2007. Robust estimation of the variogram by residual maximum likelihood. Geoderma 140: 62-72. Richardson, A.M. and Welsh, A.H. 1995. Robust restricted maximum likelihood in mixed linear models. Biometrics 51: 1429-1439. Welsh, A.H. and Richardson, A.M. 1997. Approaches to the robust estimation of mixed models. In: Handbook of Statistics Vol. 15, Elsevier, pp. 343-384.

Robust Control of Uncertain Systems via Dissipative LQG-Type Controllers

NASA Technical Reports Server (NTRS)

Joshi, Suresh M.

2000-01-01

Optimal controller design is addressed for a class of linear, time-invariant systems which are dissipative with respect to a quadratic power function. The system matrices are assumed to be affine functions of uncertain parameters confined to a convex polytopic region in the parameter space. For such systems, a method is developed for designing a controller which is dissipative with respect to a given power function, and is simultaneously optimal in the linear-quadratic-Gaussian (LQG) sense. The resulting controller provides robust stability as well as optimal performance. Three important special cases, namely, passive, norm-bounded, and sector-bounded controllers, which are also LQG-optimal, are presented. The results give new methods for robust controller design in the presence of parametric uncertainties.

Investigation of progressive failure robustness and alternate load paths for damage tolerant structures

NASA Astrophysics Data System (ADS)

Marhadi, Kun Saptohartyadi

Structural optimization for damage tolerance under various unforeseen damage scenarios is computationally challenging. It couples non-linear progressive failure analysis with sampling-based stochastic analysis of random damage. The goal of this research was to understand the relationship between alternate load paths available in a structure and its damage tolerance, and to use this information to develop computationally efficient methods for designing damage tolerant structures. Progressive failure of a redundant truss structure subjected to small random variability was investigated to identify features that correlate with robustness and predictability of the structure's progressive failure. The identified features were used to develop numerical surrogate measures that permit computationally efficient deterministic optimization to achieve robustness and predictability of progressive failure. Analysis of damage tolerance on designs with robust progressive failure indicated that robustness and predictability of progressive failure do not guarantee damage tolerance. Damage tolerance requires a structure to redistribute its load to alternate load paths. In order to investigate the load distribution characteristics that lead to damage tolerance in structures, designs with varying degrees of damage tolerance were generated using brute force stochastic optimization. A method based on principal component analysis was used to describe load distributions (alternate load paths) in the structures. Results indicate that a structure that can develop alternate paths is not necessarily damage tolerant. The alternate load paths must have a required minimum load capability. Robustness analysis of damage tolerant optimum designs indicates that designs are tailored to specified damage. A design Optimized under one damage specification can be sensitive to other damages not considered. Effectiveness of existing load path definitions and characterizations were investigated for continuum structures. A load path definition using a relative compliance change measure (U* field) was demonstrated to be the most useful measure of load path. This measure provides quantitative information on load path trajectories and qualitative information on the effectiveness of the load path. The use of the U* description of load paths in optimizing structures for effective load paths was investigated.

Prediction of functional profiles of gut microbiota from 16S rRNA metagenomic data provides a more robust evaluation of gut dysbiosis occurring in Japanese type 2 diabetic patients.

PubMed

Inoue, Ryo; Ohue-Kitano, Ryuji; Tsukahara, Takamitsu; Tanaka, Masashi; Masuda, Shinya; Inoue, Takayuki; Yamakage, Hajime; Kusakabe, Toru; Hasegawa, Koji; Shimatsu, Akira; Satoh-Asahara, Noriko

2017-11-01

We assessed whether gut microbial functional profiles predicted from 16S rRNA metagenomics differed in Japanese type 2 diabetic patients. A total of 22 Japanese subjects were recruited from our outpatient clinic in an observational study. Fecal samples were obtained from 12 control and 10 type 2 diabetic subjects. 16S rRNA metagenomic data were generated and functional profiles predicted using "Phylogenetic Investigation of Communities by Reconstruction of Unobserved States" software. We measured the parameters of glucose metabolism, gut bacterial taxonomy and functional profile, and examined the associations in a cross-sectional manner. Eleven of 288 "Kyoto Encyclopedia of Genes and Genomes" pathways were significantly enriched in diabetic patients compared with control subjects ( p <0.05, q<0.1). The relative abundance of almost all pathways, including the Insulin signaling pathway and Glycolysis/Gluconeogenesis , showed strong, positive correlations with hemoglobin A 1c (HbA 1c ) and fasting plasma glucose (FPG) levels. Bacterial taxonomic analysis showed that genus Blautia significantly differed between groups and had negative correlations with HbA 1c and FPG levels. Our findings suggest a novel pathophysiological relationship between gut microbial communities and diabetes, further highlighting the significance and utility of combining prediction of functional profiles with ordinal bacterial taxonomic analysis (UMIN Clinical Trails Registry number: UMIN000026592).

Advanced Control Synthesis for Reverse Osmosis Water Desalination Processes.

PubMed

Phuc, Bui Duc Hong; You, Sam-Sang; Choi, Hyeung-Six; Jeong, Seok-Kwon

2017-11-01

  In this study, robust control synthesis has been applied to a reverse osmosis desalination plant whose product water flow and salinity are chosen as two controlled variables. The reverse osmosis process has been selected to study since it typically uses less energy than thermal distillation. The aim of the robust design is to overcome the limitation of classical controllers in dealing with large parametric uncertainties, external disturbances, sensor noises, and unmodeled process dynamics. The analyzed desalination process is modeled as a multi-input multi-output (MIMO) system with varying parameters. The control system is decoupled using a feed forward decoupling method to reduce the interactions between control channels. Both nominal and perturbed reverse osmosis systems have been analyzed using structured singular values for their stabilities and performances. Simulation results show that the system responses meet all the control requirements against various uncertainties. Finally the reduced order controller provides excellent robust performance, with achieving decoupling, disturbance attenuation, and noise rejection. It can help to reduce the membrane cleanings, increase the robustness against uncertainties, and lower the energy consumption for process monitoring.

Robust iterative learning control for multi-phase batch processes: an average dwell-time method with 2D convergence indexes

NASA Astrophysics Data System (ADS)

Wang, Limin; Shen, Yiteng; Yu, Jingxian; Li, Ping; Zhang, Ridong; Gao, Furong

2018-01-01

In order to cope with system disturbances in multi-phase batch processes with different dimensions, a hybrid robust control scheme of iterative learning control combined with feedback control is proposed in this paper. First, with a hybrid iterative learning control law designed by introducing the state error, the tracking error and the extended information, the multi-phase batch process is converted into a two-dimensional Fornasini-Marchesini (2D-FM) switched system with different dimensions. Second, a switching signal is designed using the average dwell-time method integrated with the related switching conditions to give sufficient conditions ensuring stable running for the system. Finally, the minimum running time of the subsystems and the control law gains are calculated by solving the linear matrix inequalities. Meanwhile, a compound 2D controller with robust performance is obtained, which includes a robust extended feedback control for ensuring the steady-state tracking error to converge rapidly. The application on an injection molding process displays the effectiveness and superiority of the proposed strategy.

Some types of parent number talk count more than others: relations between parents' input and children's cardinal-number knowledge.

PubMed

Gunderson, Elizabeth A; Levine, Susan C

2011-09-01

Before they enter preschool, children vary greatly in their numerical and mathematical knowledge, and this knowledge predicts their achievement throughout elementary school (e.g. Duncan et al., 2007; Ginsburg & Russell, 1981). Therefore, it is critical that we look to the home environment for parental inputs that may lead to these early variations. Recent work has shown that the amount of number talk that parents engage in with their children is robustly related to a critical aspect of mathematical development - cardinal-number knowledge (e.g. knowing that the word 'three' refers to sets of three entities; Levine, Suriyakham, Rowe, Huttenlocher & Gunderson, 2010). The present study characterizes the different types of number talk that parents produce and investigates which types are most predictive of children's later cardinal-number knowledge. We find that parents' number talk involving counting or labeling sets of present, visible objects is related to children's later cardinal-number knowledge, whereas other types of parent number talk are not. In addition, number talk that refers to large sets of present objects (i.e. sets of size 4 to 10 that fall outside children's ability to track individual objects) is more robustly predictive of children's later cardinal-number knowledge than talk about smaller sets. The relation between parents' number talk about large sets of present objects and children's cardinal-number knowledge remains significant even when controlling for factors such as parents' socioeconomic status and other measures of parents' number and non-number talk. © 2011 Blackwell Publishing Ltd.

Robust control of a parallel hybrid drivetrain with a CVT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayer, T.; Schroeder, D.

1996-09-01

In this paper the design of a robust control system for a parallel hybrid drivetrain is presented. The drivetrain is based on a continuously variable transmission (CVT) and is therefore a highly nonlinear multiple-input-multiple-output system (MIMO-System). Input-Output-Linearization offers the possibility of linearizing and of decoupling the system. Since for example the vehicle mass varies with the load and the efficiency of the gearbox depends strongly on the actual working point, an exact linearization of the plant will mostly fail. Therefore a robust control algorithm based on sliding mode is used to control the drivetrain.

Development of the Surface Management System Integrated with CTAS Arrival Tools

NASA Technical Reports Server (NTRS)

Jung, Yoon C.; Jara, Dave

2005-01-01

The Surface Management System (SMS) developed by NASA Ames Research Center in coordination with the Federal Aviation Administration (FAA) is a decision support tool to help tower traffic coordinators and Ground/Local controllers in managing and controlling airport surface traffic in order to increase capacity, efficiency, and flexibility. SMS provides common situation awareness to personnel at various air traffic control facilities such as airport traffic control towers (ATCT s), airline ramp towers, Terminal Radar Approach Control (TRACON), and Air Route Traffic Control Center (ARTCC). SMS also provides a traffic management tool to assist ATCT traffic management coordinators (TMCs) in making decisions such as airport configuration and runway load balancing. The Build 1 of the SMS tool was installed and successfully tested at Memphis International Airport (MEM) and received high acceptance scores from ATCT controllers and coordinators, as well as airline ramp controllers. NASA Ames Research Center continues to develop SMS under NASA s Strategic Airspace Usage (SAU) project in order to improve its prediction accuracy and robustness under various modeling uncertainties. This paper reports the recent development effort performed by the NASA Ames Research Center: 1) integration of Center TRACON Automation System (CTAS) capability with SMS and 2) an alternative approach to obtain airline gate information through a publicly available website. The preliminary analysis results performed on the air/surface traffic data at the DFW airport have shown significant improvement in predicting airport arrival demand and IN time at the gate. This paper concludes with recommendations for future research and development.

Isolating the Role of Psychological Dysfunction in Smoking Cessation Failure: Relations of Personality and Psychopathology to Attaining Smoking Cessation Milestones

PubMed Central

Leventhal, Adam M.; Japuntich, Sandra J.; Piper, Megan E.; Jorenby, Douglas E.; Schlam, Tanya R.; Baker, Timothy B.

2012-01-01

Research exploring psychological dysfunction as a predictor of smoking cessation success may be limited by nonoptimal predictor variables (i.e., categorical psychodiagnostic measures vs. continuous personality-based manifestations of dysfunction) and imprecise outcomes (i.e., summative point prevalence abstinence vs. constituent cessation milestone measures). Accordingly, this study evaluated the unique and overlapping relations of broad-spectrum personality traits (positive emotionality, negative emotionality, and constraint) and past-year psychopathology (anxiety, mood, and substance use disorder) to point prevalence abstinence and three smoking cessation milestones: (1) initiating abstinence; (2) first lapse; and (3) transition from lapse to relapse. Participants were daily smokers (N=1365) enrolled in a smoking cessation treatment study. In single predictor regression models, each manifestation of internalizing dysfunction (lower positive emotionality, higher negative emotionality, and anxiety and mood disorder) predicted failure at one or more cessation milestone. In simultaneous predictor models, lower positive and higher negative emotionality significantly predicted failure to achieve milestones after controlling for psychopathology. Psychopathology did not predict any outcome when controlling for personality. Negative emotionality showed the most robust and consistent effects, significantly predicting failure to initiate abstinence, earlier lapse, and lower point prevalence abstinence rates. Substance use disorder and constraint did not predict cessation outcomes, and no single variable predicted lapse-to-relapse transition. These findings suggest that personality-related manifestations of internalizing dysfunction are more accurate markers of affective sources of relapse risk than mood and anxiety disorders. Further, individuals with high trait negative emotionality may require intensive intervention to promote the initiation and early maintenance of abstinence. PMID:22642858

Robust consensus algorithm for multi-agent systems with exogenous disturbances under convergence conditions

NASA Astrophysics Data System (ADS)

Jiang, Yulian; Liu, Jianchang; Tan, Shubin; Ming, Pingsong

2014-09-01

In this paper, a robust consensus algorithm is developed and sufficient conditions for convergence to consensus are proposed for a multi-agent system (MAS) with exogenous disturbances subject to partial information. By utilizing H∞ robust control, differential game theory and a design-based approach, the consensus problem of the MAS with exogenous bounded interference is resolved and the disturbances are restrained, simultaneously. Attention is focused on designing an H∞ robust controller (the robust consensus algorithm) based on minimisation of our proposed rational and individual cost functions according to goals of the MAS. Furthermore, sufficient conditions for convergence of the robust consensus algorithm are given. An example is employed to demonstrate that our results are effective and more capable to restrain exogenous disturbances than the existing literature.

Adaptive integral robust control and application to electromechanical servo systems.

PubMed

Deng, Wenxiang; Yao, Jianyong

2017-03-01

This paper proposes a continuous adaptive integral robust control with robust integral of the sign of the error (RISE) feedback for a class of uncertain nonlinear systems, in which the RISE feedback gain is adapted online to ensure the robustness against disturbances without the prior bound knowledge of the additive disturbances. In addition, an adaptive compensation integrated with the proposed adaptive RISE feedback term is also constructed to further reduce design conservatism when the system also exists parametric uncertainties. Lyapunov analysis reveals the proposed controllers could guarantee the tracking errors are asymptotically converging to zero with continuous control efforts. To illustrate the high performance nature of the developed controllers, numerical simulations are provided. At the end, an application case of an actual electromechanical servo system driven by motor is also studied, with some specific design consideration, and comparative experimental results are obtained to verify the effectiveness of the proposed controllers. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Stochastic Control Synthesis of Systems with Structured Uncertainty

NASA Technical Reports Server (NTRS)

Padula, Sharon L. (Technical Monitor); Crespo, Luis G.

2003-01-01

This paper presents a study on the design of robust controllers by using random variables to model structured uncertainty for both SISO and MIMO feedback systems. Once the parameter uncertainty is prescribed with probability density functions, its effects are propagated through the analysis leading to stochastic metrics for the system's output. Control designs that aim for satisfactory performances while guaranteeing robust closed loop stability are attained by solving constrained non-linear optimization problems in the frequency domain. This approach permits not only to quantify the probability of having unstable and unfavorable responses for a particular control design but also to search for controls while favoring the values of the parameters with higher chance of occurrence. In this manner, robust optimality is achieved while the characteristic conservatism of conventional robust control methods is eliminated. Examples that admit closed form expressions for the probabilistic metrics of the output are used to elucidate the nature of the problem at hand and validate the proposed formulations.

Enhanced Attitude Control Experiment for SSTI Lewis Spacecraft

NASA Technical Reports Server (NTRS)

Maghami, Peoman G.

1997-01-01

The enhanced attitude control system experiment is a technology demonstration experiment on the NASA's small spacecraft technology initiative program's Lewis spacecraft to evaluate advanced attitude control strategies. The purpose of the enhanced attitude control system experiment is to evaluate the feasibility of designing and implementing robust multi-input/multi-output attitude control strategies for enhanced pointing performance of spacecraft to improve the quality of the measurements of the science instruments. Different control design strategies based on modern and robust control theories are being considered for the enhanced attitude control system experiment. This paper describes the experiment as well as the design and synthesis of a mixed H(sub 2)/H(sub infinity) controller for attitude control. The control synthesis uses a nonlinear programming technique to tune the controller parameters and impose robustness and performance constraints. Simulations are carried out to demonstrate the feasibility of the proposed attitude control design strategy. Introduction

Robust integrated flight/propulsion control design for a STOVL aircraft using H-infinity control design techniques

NASA Technical Reports Server (NTRS)

Garg, Sanjay

1993-01-01

Results are presented from an application of H-infinity control design methodology to a centralized integrated flight/propulsion control (IFPC) system design for a supersonic STOVL fighter aircraft in transition flight. The emphasis is on formulating the H-infinity optimal control synthesis problem such that the critical requirements for the flight and propulsion systems are adequately reflected within the linear, centralized control problem formulation and the resulting controller provides robustness to modeling uncertainties and model parameter variations with flight condition. Detailed evaluation results are presented for a reduced order controller obtained from the improved H-infinity control design showing that the control design meets the specified nominal performance objective as well as provides stability robustness for variations in plant system dynamics with changes in aircraft trim speed within the transition flight envelope.

Robust time and frequency domain estimation methods in adaptive control

NASA Technical Reports Server (NTRS)

Lamaire, Richard Orville

1987-01-01

A robust identification method was developed for use in an adaptive control system. The type of estimator is called the robust estimator, since it is robust to the effects of both unmodeled dynamics and an unmeasurable disturbance. The development of the robust estimator was motivated by a need to provide guarantees in the identification part of an adaptive controller. To enable the design of a robust control system, a nominal model as well as a frequency-domain bounding function on the modeling uncertainty associated with this nominal model must be provided. Two estimation methods are presented for finding parameter estimates, and, hence, a nominal model. One of these methods is based on the well developed field of time-domain parameter estimation. In a second method of finding parameter estimates, a type of weighted least-squares fitting to a frequency-domain estimated model is used. The frequency-domain estimator is shown to perform better, in general, than the time-domain parameter estimator. In addition, a methodology for finding a frequency-domain bounding function on the disturbance is used to compute a frequency-domain bounding function on the additive modeling error due to the effects of the disturbance and the use of finite-length data. The performance of the robust estimator in both open-loop and closed-loop situations is examined through the use of simulations.

Robustness analysis of non-ordinary Petri nets for flexible assembly systems

NASA Astrophysics Data System (ADS)

Hsieh, Fu-Shiung

2010-05-01

Non-ordinary controlled Petri nets (NCPNs) have the advantages to model flexible assembly systems in which multiple identical resources may be required to perform an operation. However, existing studies on NCPNs are still limited. For example, the robustness properties of NCPNs have not been studied. This motivates us to develop an analysis method for NCPNs. Robustness analysis concerns the ability for a system to maintain operation in the presence of uncertainties. It provides an alternative way to analyse a perturbed system without reanalysis. In our previous research, we have analysed the robustness properties of several subclasses of ordinary controlled Petri nets. To study the robustness properties of NCPNs, we augment NCPNs with an uncertainty model, which specifies an upper bound on the uncertainties for each reachable marking. The resulting PN models are called non-ordinary controlled Petri nets with uncertainties (NCPNU). Based on NCPNU, the problem is to characterise the maximal tolerable uncertainties for each reachable marking. The computational complexities to characterise maximal tolerable uncertainties for each reachable marking grow exponentially with the size of the nets. Instead of considering general NCPNU, we limit our scope to a subclass of PN models called non-ordinary controlled flexible assembly Petri net with uncertainties (NCFAPNU) for assembly systems and study its robustness. We will extend the robustness analysis to NCFAPNU. We identify two types of uncertainties under which the liveness of NCFAPNU can be maintained.

Robust control of the DC-DC boost converter based on the uncertainty and disturbance estimator

NASA Astrophysics Data System (ADS)

Oucheriah, Said

2017-11-01

In this paper, a robust non-linear controller based on the uncertainty and disturbance estimator (UDE) scheme is successfully developed and implemented for the output voltage regulation of the DC-DC boost converter. System uncertainties, external disturbances and unknown non-linear dynamics are lumped as a signal that is accurately estimated using a low-pass filter and their effects are cancelled by the controller. This methodology forms the basis of the UDE-based controller. A simple procedure is also developed that systematically determines the parameters of the controller to meet certain specifications. Using simulation, the effectiveness of the proposed controller is compared against the sliding-mode control (SMC). Experimental tests also show that the proposed controller is robust to system uncertainties, large input and load perturbations.

Impaired cellulose synthase guidance leads to stem torsion and twists phyllotactic patterns in Arabidopsis.

PubMed

Landrein, Benoît; Lathe, Rahul; Bringmann, Martin; Vouillot, Cyril; Ivakov, Alexander; Boudaoud, Arezki; Persson, Staffan; Hamant, Olivier

2013-05-20

The parallel alignment of stiff cellulose microfibrils in plant-cell walls mediates anisotropic growth. This is largely controlled by cortical microtubules, which drive the insertion and trajectory of the cellulose synthase (CESA) complex at the plasma membrane. The CESA interactive protein 1 (CSI1) acts as a physical linker between CESA and cortical microtubules. Here we show that the inflorescence stems of csi1 mutants exhibit subtle right-handed torsion. Because cellulose deposition is largely uncoupled from cortical microtubules in csi1, we hypothesize that strictly transverse deposition of microfibrils in the wild-type is replaced by a helical orientation of uniform handedness in the mutant and that the helical microfibril alignment generates torsion. Interestingly, both elastic and viscous models for an expanding cell predict that a net helical orientation of microfibrils gives rise to a torque. We indeed observed tilted microfibrils in csi1 cells, and the torsion was almost absent in a csi1 prc1 background with impaired cellulose synthesis. In addition, the stem torsion led to a novel bimodal and robust phyllotactic pattern in the csi1 mutant, illustrating how growth perturbations can replace one robust mathematical pattern with a different, equally robust pattern. Copyright © 2013 Elsevier Ltd. All rights reserved.

Guaranteeing robustness of structural condition monitoring to environmental variability

NASA Astrophysics Data System (ADS)

Van Buren, Kendra; Reilly, Jack; Neal, Kyle; Edwards, Harry; Hemez, François

2017-01-01

Advances in sensor deployment and computational modeling have allowed significant strides to be recently made in the field of Structural Health Monitoring (SHM). One widely used SHM strategy is to perform a vibration analysis where a model of the structure's pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability, unknown model functional forms, and unknown values of model parameters. Not accounting for these sources of uncertainty can lead to false-positives or false-negatives in the structural condition assessment. To manage the uncertainty, we propose a robust SHM methodology that combines three technologies. A time series algorithm is trained using "baseline" data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a user-defined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate "size" of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes throughout time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM. (Publication approved for unlimited, public release on October-28-2015, LA-UR-15-28442, unclassified.)

Visualization of the Invisible, Explanation of the Unknown, Ruggedization of the Unstable: Sensitivity Analysis, Virtual Tryout and Robust Design through Systematic Stochastic Simulation

NASA Astrophysics Data System (ADS)

Zwickl, Titus; Carleer, Bart; Kubli, Waldemar

2005-08-01

In the past decade, sheet metal forming simulation became a well established tool to predict the formability of parts. In the automotive industry, this has enabled significant reduction in the cost and time for vehicle design and development, and has helped to improve the quality and performance of vehicle parts. However, production stoppages for troubleshooting and unplanned die maintenance, as well as production quality fluctuations continue to plague manufacturing cost and time. The focus therefore has shifted in recent times beyond mere feasibility to robustness of the product and process being engineered. Ensuring robustness is the next big challenge for the virtual tryout / simulation technology. We introduce new methods, based on systematic stochastic simulations, to visualize the behavior of the part during the whole forming process — in simulation as well as in production. Sensitivity analysis explains the response of the part to changes in influencing parameters. Virtual tryout allows quick exploration of changed designs and conditions. Robust design and manufacturing guarantees quality and process capability for the production process. While conventional simulations helped to reduce development time and cost by ensuring feasible processes, robustness engineering tools have the potential for far greater cost and time savings. Through examples we illustrate how expected and unexpected behavior of deep drawing parts may be tracked down, identified and assigned to the influential parameters. With this knowledge, defects can be eliminated or springback can be compensated e.g.; the response of the part to uncontrollable noise can be predicted and minimized. The newly introduced methods enable more reliable and predictable stamping processes in general.

Robust Optimal Adaptive Control Method with Large Adaptive Gain

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2009-01-01

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.

The Applications of Model-Based Geostatistics in Helminth Epidemiology and Control

PubMed Central

Magalhães, Ricardo J. Soares; Clements, Archie C.A.; Patil, Anand P.; Gething, Peter W.; Brooker, Simon

2011-01-01

Funding agencies are dedicating substantial resources to tackle helminth infections. Reliable maps of the distribution of helminth infection can assist these efforts by targeting control resources to areas of greatest need. The ability to define the distribution of infection at regional, national and subnational levels has been enhanced greatly by the increased availability of good quality survey data and the use of model-based geostatistics (MBG), enabling spatial prediction in unsampled locations. A major advantage of MBG risk mapping approaches is that they provide a flexible statistical platform for handling and representing different sources of uncertainty, providing plausible and robust information on the spatial distribution of infections to inform the design and implementation of control programmes. Focussing on schistosomiasis and soil-transmitted helminthiasis, with additional examples for lymphatic filariasis and onchocerciasis, we review the progress made to date with the application of MBG tools in large-scale, real-world control programmes and propose a general framework for their application to inform integrative spatial planning of helminth disease control programmes. PMID:21295680

Closed Loop System Identification with Genetic Algorithms

NASA Technical Reports Server (NTRS)

Whorton, Mark S.

2004-01-01

High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.

Electron beam energy and bunch length feed forward control studies using an artificial neural network at the Linac coherent light source

NASA Astrophysics Data System (ADS)

Meier, E.; Biedron, S. G.; LeBlanc, G.; Morgan, M. J.; Wu, J.

2009-11-01

This paper describes the results of an advanced control algorithm for the stabilization of electron beam energy in a Linac. The approach combines a conventional Proportional-Integral (PI) controller with a neural network (NNET) feed forward algorithm; it utilizes the robustness of PI control and the ability of a feed forward system in order to exert control over a wider range of frequencies. The NNET is trained to recognize jitter occurring in the phase and voltage of one of the klystrons, based on a record of these parameters, and predicts future energy deviations. A systematic approach is developed to determine the optimal NNET parameters that are then applied to the Australian Synchrotron Linac. The system's capability to fully cancel multi-frequency jitter is demonstrated. The NNET system is then augmented with the PI algorithm, and further jitter attenuation is achieved when the NNET is not operating optimally.

The scent fingerprint of hepatocarcinoma: in-vitro metastasis prediction with volatile organic compounds (VOCs)

PubMed Central

Amal, Haitham; Ding, Lu; Liu, Bin-bin; Tisch, Ulrike; Xu, Zhen-qin; Shi, Da-you; Zhao, Yan; Chen, Jie; Sun, Rui-xia; Liu, Hu; Ye, Sheng-Long; Tang, Zhao-you; Haick, Hossam

2012-01-01

Background: Hepatocellular carcinoma (HCC) is a common and aggressive form of cancer. Due to a high rate of postoperative recurrence, the prognosis for HCC is poor. Subclinical metastasis is the major cause of tumor recurrence and patient mortality. Currently, there is no reliable prognostic method of invasion. Aim: To investigate the feasibility of fingerprints of volatile organic compounds (VOCs) for the in-vitro prediction of metastasis. Methods: Headspace gases were collected from 36 cell cultures (HCC with high and low metastatic potential and normal cells) and analyzed using nanomaterial-based sensors. Predictive models were built by employing discriminant factor analysis pattern recognition, and the classification success was determined using leave-one-out cross-validation. The chemical composition of each headspace sample was studied using gas chromatography coupled with mass spectrometry (GC-MS). Results: Excellent discrimination was achieved using the nanomaterial-based sensors between (i) all HCC and normal controls; (ii) low metastatic HCC and normal controls; (iii) high metastatic HCC and normal controls; and (iv) high and low HCC. Several HCC-related VOCs that could be associated with biochemical cellular processes were identified through GC-MS analysis. Conclusion: The presented results constitute a proof-of-concept for the in-vitro prediction of the metastatic potential of HCC from VOC fingerprints using nanotechnology. Further studies on a larger number of more diverse cell cultures are needed to evaluate the robustness of the VOC patterns. These findings could benefit the development of a fast and potentially inexpensive laboratory test for subclinical HCC metastasis. PMID:22888249

Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities

PubMed Central

Fang, Xin; Sastry, Anand; Mih, Nathan; Kim, Donghyuk; Tan, Justin; Lloyd, Colton J.; Gao, Ye; Yang, Laurence; Palsson, Bernhard O.

2017-01-01

Transcriptional regulatory networks (TRNs) have been studied intensely for >25 y. Yet, even for the Escherichia coli TRN—probably the best characterized TRN—several questions remain. Here, we address three questions: (i) How complete is our knowledge of the E. coli TRN; (ii) how well can we predict gene expression using this TRN; and (iii) how robust is our understanding of the TRN? First, we reconstructed a high-confidence TRN (hiTRN) consisting of 147 transcription factors (TFs) regulating 1,538 transcription units (TUs) encoding 1,764 genes. The 3,797 high-confidence regulatory interactions were collected from published, validated chromatin immunoprecipitation (ChIP) data and RegulonDB. For 21 different TF knockouts, up to 63% of the differentially expressed genes in the hiTRN were traced to the knocked-out TF through regulatory cascades. Second, we trained supervised machine learning algorithms to predict the expression of 1,364 TUs given TF activities using 441 samples. The algorithms accurately predicted condition-specific expression for 86% (1,174 of 1,364) of the TUs, while 193 TUs (14%) were predicted better than random TRNs. Third, we identified 10 regulatory modules whose definitions were robust against changes to the TRN or expression compendium. Using surrogate variable analysis, we also identified three unmodeled factors that systematically influenced gene expression. Our computational workflow comprehensively characterizes the predictive capabilities and systems-level functions of an organism’s TRN from disparate data types. PMID:28874552

Towards robust quantification and reduction of uncertainty in hydrologic predictions: Integration of particle Markov chain Monte Carlo and factorial polynomial chaos expansion

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Baetz, B. W.; Ancell, B. C.

2017-05-01

The particle filtering techniques have been receiving increasing attention from the hydrologic community due to its ability to properly estimate model parameters and states of nonlinear and non-Gaussian systems. To facilitate a robust quantification of uncertainty in hydrologic predictions, it is necessary to explicitly examine the forward propagation and evolution of parameter uncertainties and their interactions that affect the predictive performance. This paper presents a unified probabilistic framework that merges the strengths of particle Markov chain Monte Carlo (PMCMC) and factorial polynomial chaos expansion (FPCE) algorithms to robustly quantify and reduce uncertainties in hydrologic predictions. A Gaussian anamorphosis technique is used to establish a seamless bridge between the data assimilation using the PMCMC and the uncertainty propagation using the FPCE through a straightforward transformation of posterior distributions of model parameters. The unified probabilistic framework is applied to the Xiangxi River watershed of the Three Gorges Reservoir (TGR) region in China to demonstrate its validity and applicability. Results reveal that the degree of spatial variability of soil moisture capacity is the most identifiable model parameter with the fastest convergence through the streamflow assimilation process. The potential interaction between the spatial variability in soil moisture conditions and the maximum soil moisture capacity has the most significant effect on the performance of streamflow predictions. In addition, parameter sensitivities and interactions vary in magnitude and direction over time due to temporal and spatial dynamics of hydrologic processes.

Use of the Generating Options for Active Risk Control (GO-ARC) Technique can lead to more robust risk control options.

PubMed

Card, Alan J; Simsekler, Mecit Can Emre; Clark, Michael; Ward, James R; Clarkson, P John

2014-01-01

Risk assessment is widely used to improve patient safety, but healthcare workers are not trained to design robust solutions to the risks they uncover. This leads to an overreliance on the weakest category of risk control recommendations: administrative controls. Increasing the proportion of non-administrative risk control options (NARCOs) generated would enable (though not ensure) the adoption of more robust solutions. Experimentally assess a method for generating stronger risk controls: The Generating Options for Active Risk Control (GO-ARC) Technique. Participants generated risk control options in response to two patient safety scenarios. Scenario 1 (baseline): All participants used current practice (unstructured brainstorming). Scenario 2: Control group used current practice; intervention group used the GO-ARC Technique. To control for individual differences between participants, analysis focused on the change in the proportion of NARCOs for each group. Proportion of NARCOs decreased from 0.18 at baseline to 0.12. Intervention group: Proportion increased from 0.10 at baseline to 0.29 using the GO-ARC Technique. Results were statistically significant. There was no decrease in the number of administrative controls generated by the intervention group. The Generating Options for Active Risk Control (GO-ARC) Technique appears to lead to more robust risk control options.