Constrained model predictive control, state estimation and coordination
NASA Astrophysics Data System (ADS)
Yan, Jun
In this dissertation, we study the interaction between the control performance and the quality of the state estimation in a constrained Model Predictive Control (MPC) framework for systems with stochastic disturbances. This consists of three parts: (i) the development of a constrained MPC formulation that adapts to the quality of the state estimation via constraints; (ii) the application of such a control law in a multi-vehicle formation coordinated control problem in which each vehicle operates subject to a no-collision constraint posed by others' imperfect prediction computed from finite bit-rate, communicated data; (iii) the design of the predictors and the communication resource assignment problem that satisfy the performance requirement from Part (ii). Model Predictive Control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. However, if the state constraints were handled in the same certainty-equivalence fashion, the resulting control law could drive the real state to violate the constraints frequently. Part (i) focuses on exploring the inclusion of state estimates into the constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. In Part (ii), we consider applying constrained MPC as a local control law in a coordinated control problem of a group of distributed autonomous systems. Interactions between the systems are captured via constraints. First, we inspect the application of constrained MPC to a completely deterministic case. Formation stability theorems are derived for the subsystems and conditions on the local constraint set are derived in order to
NASA Astrophysics Data System (ADS)
Liu, Kailong; Li, Kang; Zhang, Cheng
2017-04-01
Battery temperature is a primary factor affecting the battery performance, and suitable battery temperature control in particular internal temperature control can not only guarantee battery safety but also improve its efficiency. This is however challenging as current controller designs for battery charging have no mechanisms to incorporate such information. This paper proposes a novel battery charging control strategy which applies the constrained generalized predictive control (GPC) to charge a LiFePO4 battery based on a newly developed coupled thermoelectric model. The control target primarily aims to maintain the battery cell internal temperature within a desirable range while delivering fast charging. To achieve this, the coupled thermoelectric model is firstly introduced to capture the battery behaviours in particular SOC and internal temperature which are not directly measurable in practice. Then a controlled auto-regressive integrated moving average (CARIMA) model whose parameters are identified by the recursive least squares (RLS) algorithm is developed as an online self-tuning predictive model for a GPC controller. Then the constrained generalized predictive controller is developed to control the charging current. Experiment results confirm the effectiveness of the proposed control strategy. Further, the best region of heat dissipation rate and proper internal temperature set-points are also investigated and analysed.
NASA Astrophysics Data System (ADS)
Witheephanich, K.; Escaño, J. M.; Hayes, M. J.
2011-08-01
This work considers the problem of controlling transmit power within a wireless sensor network (WSN), where the practical constraints typically posed by an ambulatory healthcare setting are explicitly taken into account, as a constrained received signal strength indicator (RSSI) tracking control problem. The problem is formulated using an explicit generalised predictive control (GPC) strategy for dynamic transmission power control that ensures a balance between energy consumption and quality of service (QoS) through the creation of a stable floor on information throughput. Optimal power assignment is achieved by an explicit solution of the constrained GPC problem that is computed off-line using a multi-parametric quadratic program (mpQP). The solution is shown to be a piecewise-affine function. The new design is demonstrated to be practically feasible via a resource-constrained, fully IEEE 802.15.4 compliant, Moteiv's Tmote Sky sensor node platform. Design utility is benchmarked experimentally using a representative selection of scaled ambulatory scenarios.
On robustness of constrained non-linear H ∞ predictive controllers with disturbances
NASA Astrophysics Data System (ADS)
He, De-Feng; Ji, Hai-Bo; Zheng, Tao
2010-02-01
This article considers the robustness problem of H ∞ model predictive controllers for constrained non-linear discrete-time systems subject to disturbances, which are dependent on the system state and input. The notions of input-to-state stability and finite L 2-gain of non-linear systems are introduced and exploited to investigate the robustness properties of this predictive controller under the state and input constraints and the disturbance. Moreover, this robustness of the controller is extended to the case of suboptimality of the solution. With its feasibility at initial time, the feasibility of the online optimisation problem is guaranteed for all times in the presence of disturbances and constraints. Finally, an example is employed to illustrate the proposed results.
NASA Astrophysics Data System (ADS)
Li, Huiping; Shi, Yang
2013-04-01
This article investigates a class of constrained nonlinear networked control systems (NCSs) subject to external disturbances, input and state constraints and network-induced constraints. From a practical perspective, the network-induced constraints considered include the time delays and packet dropouts on both the sensor-to-controller (S-C) channel and the controller-to-actuator (C-A) channel simultaneously. The min-max model predictive control method is proposed to design the control packets by incorporating the external disturbances into the optimisation problem. Moreover, the input-to-state practical stability of the resulting nonlinear NCS is established by constructing a novel Lyapunov function. Finally, the simulation results and the comparison studies are presented to demonstrate the effectiveness and improvement of the proposed method.
Stock management in hospital pharmacy using chance-constrained model predictive control.
Jurado, I; Maestre, J M; Velarde, P; Ocampo-Martinez, C; Fernández, I; Tejera, B Isla; Prado, J R Del
2016-05-01
One of the most important problems in the pharmacy department of a hospital is stock management. The clinical need for drugs must be satisfied with limited work labor while minimizing the use of economic resources. The complexity of the problem resides in the random nature of the drug demand and the multiple constraints that must be taken into account in every decision. In this article, chance-constrained model predictive control is proposed to deal with this problem. The flexibility of model predictive control allows taking into account explicitly the different objectives and constraints involved in the problem while the use of chance constraints provides a trade-off between conservativeness and efficiency. The solution proposed is assessed to study its implementation in two Spanish hospitals.
NASA Astrophysics Data System (ADS)
Pin, G.; Filippo, M.; Pellegrino, F. A.; Fenu, G.; Parisini, T.
2013-05-01
The objective of this work consists in the offline approximation of possibly discontinuous model predictive control laws for nonlinear discrete-time systems, while enforcing hard constraints on state and input variables. Obtaining an offline approximation of the receding horizon control law may lead to a very significant reduction of the online computational burden with respect to algorithms based on iterated optimization, thus allowing the application to fast dynamics plants. The proposed approximation scheme allows to cope with discontinuous control laws, such as those arising from constrained nonlinear finite horizon optimal control problems. A detailed stability analysis of the closed-loop system driven by the approximated state-feedback controller shows that the devised technique guarantees the input-to-state practical stability with respect to the (non-fading) approximation-induced errors. Two examples are provided to show the effectiveness of the method when the approximator is chosen either as a discontinuous nearest point function or as a smooth neural network.
Nery, Gesner A; Martins, Márcio A F; Kalid, Ricardo
2014-03-01
This paper describes the development of a method to optimally tune constrained MPC algorithms with model uncertainty. The proposed method is formulated by using the worst-case control scenario, which is characterized by the Morari resiliency index and the condition number, and a given nonlinear multi-objective performance criterion. The resulting constrained mixed-integer nonlinear optimization problem is solved on the basis of a modified version of the particle swarm optimization technique, because of its effectiveness in dealing with this kind of problem. The performance of this PSO-based tuning method is evaluated through its application to the well-known Shell heavy oil fractionator process.
Constrained control allocation
NASA Technical Reports Server (NTRS)
Durham, Wayne C.
1992-01-01
This paper addresses the problem of the allocation of several flight controls to the generation of specified body-axis moments. The number of controls is greater than the number of moments being controlled, and the ranges of the controls are constrained to certain limits. The controls are assumed to be individually linear in their effect throughout their ranges of motion, and independent of one another in their effects. The geometries of the subset of the constrained controls and of its image in moment space are examined. A direct method of allocating these several controls is presented, that guarantees the maximum possible moment is generated within the constraints of the controls. The results are illustrated by an example problem involving three controls and two moments.
NASA Technical Reports Server (NTRS)
Cabell, Randolph H.; Gibbs, Gary P.
2000-01-01
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
Zhao, Meng; Ding, Baocang
2015-03-01
This paper considers the distributed model predictive control (MPC) of nonlinear large-scale systems with dynamically decoupled subsystems. According to the coupled state in the overall cost function of centralized MPC, the neighbors are confirmed and fixed for each subsystem, and the overall objective function is disassembled into each local optimization. In order to guarantee the closed-loop stability of distributed MPC algorithm, the overall compatibility constraint for centralized MPC algorithm is decomposed into each local controller. The communication between each subsystem and its neighbors is relatively low, only the current states before optimization and the optimized input variables after optimization are being transferred. For each local controller, the quasi-infinite horizon MPC algorithm is adopted, and the global closed-loop system is proven to be exponentially stable.
Chance-constrained model predictive control applied to inventory management in hospitalary pharmacy.
Maestre, Jose Maria; Ocampo-Martinez, Carlos
2014-01-01
This extended abstract addresses the preliminary results of applying uncertainty handling strategies and advanced control techniques to the inventary management of hospitality pharmacy. Inventory management is one of the main tasks that a pharmacy department has to carry out in a hospital. It is a complex problem because it requires to establish a tradeoff between contradictory optimization criteria. The final goal of the proposed research is to update the inventory management system of hospitals such that it is possible to reduce the average inventory while maintaining preestablished clinical guarantees.
Xavier, MA; Trimboli, MS
2015-07-01
This paper introduces a novel application of model predictive control (MPC) to cell-level charging of a lithium-ion battery utilizing an equivalent circuit model of battery dynamics. The approach employs a modified form of the MPC algorithm that caters for direct feed-though signals in order to model near-instantaneous battery ohmic resistance. The implementation utilizes a 2nd-order equivalent circuit discrete-time state-space model based on actual cell parameters; the control methodology is used to compute a fast charging profile that respects input, output, and state constraints. Results show that MPC is well-suited to the dynamics of the battery control problem and further suggest significant performance improvements might be achieved by extending the result to electrochemical models. (C) 2015 Elsevier B.V. All rights reserved.
Exploring constrained quantum control landscapes
NASA Astrophysics Data System (ADS)
Moore, Katharine W.; Rabitz, Herschel
2012-10-01
The broad success of optimally controlling quantum systems with external fields has been attributed to the favorable topology of the underlying control landscape, where the landscape is the physical observable as a function of the controls. The control landscape can be shown to contain no suboptimal trapping extrema upon satisfaction of reasonable physical assumptions, but this topological analysis does not hold when significant constraints are placed on the control resources. This work employs simulations to explore the topology and features of the control landscape for pure-state population transfer with a constrained class of control fields. The fields are parameterized in terms of a set of uniformly spaced spectral frequencies, with the associated phases acting as the controls. This restricted family of fields provides a simple illustration for assessing the impact of constraints upon seeking optimal control. Optimization results reveal that the minimum number of phase controls necessary to assure a high yield in the target state has a special dependence on the number of accessible energy levels in the quantum system, revealed from an analysis of the first- and second-order variation of the yield with respect to the controls. When an insufficient number of controls and/or a weak control fluence are employed, trapping extrema and saddle points are observed on the landscape. When the control resources are sufficiently flexible, solutions producing the globally maximal yield are found to form connected "level sets" of continuously variable control fields that preserve the yield. These optimal yield level sets are found to shrink to isolated points on the top of the landscape as the control field fluence is decreased, and further reduction of the fluence turns these points into suboptimal trapping extrema on the landscape. Although constrained control fields can come in many forms beyond the cases explored here, the behavior found in this paper is illustrative of
Testing a Constrained MPC Controller in a Process Control Laboratory
ERIC Educational Resources Information Center
Ricardez-Sandoval, Luis A.; Blankespoor, Wesley; Budman, Hector M.
2010-01-01
This paper describes an experiment performed by the fourth year chemical engineering students in the process control laboratory at the University of Waterloo. The objective of this experiment is to test the capabilities of a constrained Model Predictive Controller (MPC) to control the operation of a Double Pipe Heat Exchanger (DPHE) in real time.…
Prediction of noise constrained optimum takeoff procedures
NASA Technical Reports Server (NTRS)
Padula, S. L.
1980-01-01
An optimization method is used to predict safe, maximum-performance takeoff procedures which satisfy noise constraints at multiple observer locations. The takeoff flight is represented by two-degree-of-freedom dynamical equations with aircraft angle-of-attack and engine power setting as control functions. The engine thrust, mass flow and noise source parameters are assumed to be given functions of the engine power setting and aircraft Mach number. Effective Perceived Noise Levels at the observers are treated as functionals of the control functions. The method is demonstrated by applying it to an Advanced Supersonic Transport aircraft design. The results indicate that automated takeoff procedures (continuously varying controls) can be used to significantly reduce community and certification noise without jeopardizing safety or degrading performance.
On the Constrained Attitude Control Problem
NASA Technical Reports Server (NTRS)
Hadaegh, Fred Y.; Kim, Yoonsoo; Mesbahi, Mehran; Singh, Gurkipal
2004-01-01
In this paper, we consider various classes of constrained attitude control (CAC) problem in single and multiple spacecraft settings. After categorizing attitude constraints into four distinct types, we provide an overview of the existing approaches to this problem. We then proceed to further expand on a recent algorithmic approach to the CAC problem. The paper concludes with an example demonstrating the viability of the proposed algorithm for a multiple spacecraft constrained attitude reconfiguration scenario.
Vibration control through passive constrained layer damping and active control
NASA Astrophysics Data System (ADS)
Lam, Margaretha J.; Inman, Daniel J.; Saunders, William R.
1997-05-01
To add damping to systems, viscoelastic materials (VEM) are added to structures. In order to enhance the damping effects of the VEM, a constraining layer is attached. When this constraining layer is an active element, the treatment is called active constrained layer damping (ACLD). Recently, the investigation of ACLD treatments has shown it to be an effective method of vibration suppression. In this paper, the treatment of a beam with a separate active element and passive constrained layer (PCLD) element is investigated. A Ritz- Galerkin approach is used to obtain discretized equations of motion. The damping is modeled using the GHM method and the system is analyzed in the time domain. By optimizing on the performance and control effort for both the active and passive case, it is shown that this treatment is capable of lower control effort with more inherent damping, and is therefore a better approach to damp vibration.
CCTOP: a Consensus Constrained TOPology prediction web server.
Dobson, László; Reményi, István; Tusnády, Gábor E
2015-07-01
The Consensus Constrained TOPology prediction (CCTOP; http://cctop.enzim.ttk.mta.hu) server is a web-based application providing transmembrane topology prediction. In addition to utilizing 10 different state-of-the-art topology prediction methods, the CCTOP server incorporates topology information from existing experimental and computational sources available in the PDBTM, TOPDB and TOPDOM databases using the probabilistic framework of hidden Markov model. The server provides the option to precede the topology prediction with signal peptide prediction and transmembrane-globular protein discrimination. The initial result can be recalculated by (de)selecting any of the prediction methods or mapped experiments or by adding user specified constraints. CCTOP showed superior performance to existing approaches. The reliability of each prediction is also calculated, which correlates with the accuracy of the per protein topology prediction. The prediction results and the collected experimental information are visualized on the CCTOP home page and can be downloaded in XML format. Programmable access of the CCTOP server is also available, and an example of client-side script is provided.
Vibration control of cylindrical shells using active constrained layer damping
NASA Astrophysics Data System (ADS)
Ray, Manas C.; Chen, Tung-Huei; Baz, Amr M.
1997-05-01
The fundamentals of controlling the structural vibration of cylindrical shells treated with active constrained layer damping (ACLD) treatments are presented. The effectiveness of the ACLD treatments in enhancing the damping characteristics of thin cylindrical shells is demonstrated theoretically and experimentally. A finite element model (FEM) is developed to describe the dynamic interaction between the shells and the ACLD treatments. The FEM is used to predict the natural frequencies and the modal loss factors of shells which are partially treated with patches of the ACLD treatments. The predictions of the FEM are validated experimentally using stainless steel cylinders which are 20.32 cm in diameter, 30.4 cm in length and 0.05 cm in thickness. The cylinders are treated with ACLD patches of different configurations in order to target single or multi-modes of lobar vibrations. The ACLD patches used are made of DYAD 606 visco-elastic layer which is sandwiched between two layers of PVDF piezo-electric films. Vibration attenuations of 85% are obtained with maximum control voltage of 40 volts. Such attenuations are attributed to the effectiveness of the ACLD treatment in increasing the modal damping ratios by about a factor of four over those of conventional passive constrained layer damping (PCLD) treatments. The obtained results suggest the potential of the ACLD treatments in controlling the vibration of cylindrical shells which constitute the major building block of many critical structures such as cabins of aircrafts, hulls of submarines and bodies of rockets and missiles.
Natural enemy interactions constrain pest control in complex agricultural landscapes
Martin, Emily A.; Reineking, Björn; Seo, Bumsuk; Steffan-Dewenter, Ingolf
2013-01-01
Biological control of pests by natural enemies is a major ecosystem service delivered to agriculture worldwide. Quantifying and predicting its effectiveness at large spatial scales is critical for increased sustainability of agricultural production. Landscape complexity is known to benefit natural enemies, but its effects on interactions between natural enemies and the consequences for crop damage and yield are unclear. Here, we show that pest control at the landscape scale is driven by differences in natural enemy interactions across landscapes, rather than by the effectiveness of individual natural enemy guilds. In a field exclusion experiment, pest control by flying insect enemies increased with landscape complexity. However, so did antagonistic interactions between flying insects and birds, which were neutral in simple landscapes and increasingly negative in complex landscapes. Negative natural enemy interactions thus constrained pest control in complex landscapes. These results show that, by altering natural enemy interactions, landscape complexity can provide ecosystem services as well as disservices. Careful handling of the tradeoffs among multiple ecosystem services, biodiversity, and societal concerns is thus crucial and depends on our ability to predict the functional consequences of landscape-scale changes in trophic interactions. PMID:23513216
Natural enemy interactions constrain pest control in complex agricultural landscapes.
Martin, Emily A; Reineking, Björn; Seo, Bumsuk; Steffan-Dewenter, Ingolf
2013-04-02
Biological control of pests by natural enemies is a major ecosystem service delivered to agriculture worldwide. Quantifying and predicting its effectiveness at large spatial scales is critical for increased sustainability of agricultural production. Landscape complexity is known to benefit natural enemies, but its effects on interactions between natural enemies and the consequences for crop damage and yield are unclear. Here, we show that pest control at the landscape scale is driven by differences in natural enemy interactions across landscapes, rather than by the effectiveness of individual natural enemy guilds. In a field exclusion experiment, pest control by flying insect enemies increased with landscape complexity. However, so did antagonistic interactions between flying insects and birds, which were neutral in simple landscapes and increasingly negative in complex landscapes. Negative natural enemy interactions thus constrained pest control in complex landscapes. These results show that, by altering natural enemy interactions, landscape complexity can provide ecosystem services as well as disservices. Careful handling of the tradeoffs among multiple ecosystem services, biodiversity, and societal concerns is thus crucial and depends on our ability to predict the functional consequences of landscape-scale changes in trophic interactions.
Active/Passive Control of Sound Radiation from Panels using Constrained Layer Damping
NASA Technical Reports Server (NTRS)
Gibbs, Gary P.; Cabell, Randolph H.
2003-01-01
A hybrid passive/active noise control system utilizing constrained layer damping and model predictive feedback control is presented. This system is used to control the sound radiation of panels due to broadband disturbances. To facilitate the hybrid system design, a methodology for placement of constrained layer damping which targets selected modes based on their relative radiated sound power is developed. The placement methodology is utilized to determine two constrained layer damping configurations for experimental evaluation of a hybrid system. The first configuration targets the (4,1) panel mode which is not controllable by the piezoelectric control actuator, and the (2,3) and (5,2) panel modes. The second configuration targets the (1,1) and (3,1) modes. The experimental results demonstrate the improved reduction of radiated sound power using the hybrid passive/active control system as compared to the active control system alone.
Constrained modes in control theory - Transmission zeros of uniform beams
NASA Technical Reports Server (NTRS)
Williams, T.
1992-01-01
Mathematical arguments are presented demonstrating that the well-established control system concept of the transmission zero is very closely related to the structural concept of the constrained mode. It is shown that the transmission zeros of a flexible structure form a set of constrained natural frequencies for it, with the constraints depending explicitly on the locations and the types of sensors and actuators used for control. Based on this formulation, an algorithm is derived and used to produce dimensionless plots of the zero of a uniform beam with a compatible sensor/actuator pair.
A lexicographic approach to constrained MDP admission control
NASA Astrophysics Data System (ADS)
Panfili, Martina; Pietrabissa, Antonio; Oddi, Guido; Suraci, Vincenzo
2016-02-01
This paper proposes a reinforcement learning-based lexicographic approach to the call admission control problem in communication networks. The admission control problem is modelled as a multi-constrained Markov decision process. To overcome the problems of the standard approaches to the solution of constrained Markov decision processes, based on the linear programming formulation or on a Lagrangian approach, a multi-constraint lexicographic approach is defined, and an online implementation based on reinforcement learning techniques is proposed. Simulations validate the proposed approach.
Constrained time-optimal control of double-integrator system and its application in MPC
NASA Astrophysics Data System (ADS)
Fehér, Marek; Straka, Ondřej; Šmídl, Václav
2017-01-01
The paper deals with the design of a time-optimal controller for systems subject to both state and control constraints. The focus is laid on a double-integrator system, for which the time-to-go function is calculated. The function is then used as a part of a model predictive control criterion where it represents the long-horizon part. The designed model predictive control algorithm is then used in a constrained control problem of permanent magnet synchronous motor model, which behavior can be approximated by a double integrator model. Accomplishments of the control goals are illustrated in a numerical example.
Total energy control system autopilot design with constrained parameter optimization
NASA Technical Reports Server (NTRS)
Ly, Uy-Loi; Voth, Christopher
1990-01-01
A description is given of the application of a multivariable control design method (SANDY) based on constrained parameter optimization to the design of a multiloop aircraft flight control system. Specifically, the design method is applied to the direct synthesis of a multiloop AFCS inner-loop feedback control system based on total energy control system (TECS) principles. The design procedure offers a structured approach for the determination of a set of stabilizing controller design gains that meet design specifications in closed-loop stability, command tracking performance, disturbance rejection, and limits on control activities. The approach can be extended to a broader class of multiloop flight control systems. Direct tradeoffs between many real design goals are rendered systematic by proper formulation of the design objectives and constraints. Satisfactory designs are usually obtained in few iterations. Performance characteristics of the optimized TECS design have been improved, particularly in the areas of closed-loop damping and control activity in the presence of turbulence.
Control of the constrained planar simple inverted pendulum
NASA Technical Reports Server (NTRS)
Bavarian, B.; Wyman, B. F.; Hemami, H.
1983-01-01
Control of a constrained planar inverted pendulum by eigenstructure assignment is considered. Linear feedback is used to stabilize and decouple the system in such a way that specified subspaces of the state space are invariant for the closed-loop system. The effectiveness of the feedback law is tested by digital computer simulation. Pre-compensation by an inverse plant is used to improve performance.
Deadbeat Predictive Controllers
NASA Technical Reports Server (NTRS)
Juang, Jer-Nan; Phan, Minh
1997-01-01
Several new computational algorithms are presented to compute the deadbeat predictive control law. The first algorithm makes use of a multi-step-ahead output prediction to compute the control law without explicitly calculating the controllability matrix. The system identification must be performed first and then the predictive control law is designed. The second algorithm uses the input and output data directly to compute the feedback law. It combines the system identification and the predictive control law into one formulation. The third algorithm uses an observable-canonical form realization to design the predictive controller. The relationship between all three algorithms is established through the use of the state-space representation. All algorithms are applicable to multi-input, multi-output systems with disturbance inputs. In addition to the feedback terms, feed forward terms may also be added for disturbance inputs if they are measurable. Although the feedforward terms do not influence the stability of the closed-loop feedback law, they enhance the performance of the controlled system.
Stall Recovery Guidance Algorithms Based on Constrained Control Approaches
NASA Technical Reports Server (NTRS)
Stepanyan, Vahram; Krishnakumar, Kalmanje; Kaneshige, John; Acosta, Diana
2016-01-01
Aircraft loss-of-control, in particular approach to stall or fully developed stall, is a major factor contributing to aircraft safety risks, which emphasizes the need to develop algorithms that are capable of assisting the pilots to identify the problem and providing guidance to recover the aircraft. In this paper we present several stall recovery guidance algorithms, which are implemented in the background without interfering with flight control system and altering the pilot's actions. They are using input and state constrained control methods to generate guidance signals, which are provided to the pilot in the form of visual cues. It is the pilot's decision to follow these signals. The algorithms are validated in the pilot-in-the loop medium fidelity simulation experiment.
Digital robust control law synthesis using constrained optimization
NASA Technical Reports Server (NTRS)
Mukhopadhyay, Vivekananda
1989-01-01
Development of digital robust control laws for active control of high performance flexible aircraft and large space structures is a research area of significant practical importance. The flexible system is typically modeled by a large order state space system of equations in order to accurately represent the dynamics. The active control law must satisy multiple conflicting design requirements and maintain certain stability margins, yet should be simple enough to be implementable on an onboard digital computer. Described here is an application of a generic digital control law synthesis procedure for such a system, using optimal control theory and constrained optimization technique. A linear quadratic Gaussian type cost function is minimized by updating the free parameters of the digital control law, while trying to satisfy a set of constraints on the design loads, responses and stability margins. Analytical expressions for the gradients of the cost function and the constraints with respect to the control law design variables are used to facilitate rapid numerical convergence. These gradients can be used for sensitivity study and may be integrated into a simultaneous structure and control optimization scheme.
NASA Astrophysics Data System (ADS)
McDonough, Kevin K.
these sets for aircraft longitudinal and lateral aircraft dynamics are reported, and it is shown that these sets can be larger in size compared to the more commonly used safe sets. An approach to constrained maneuver planning based on chaining recoverable sets or integral safe sets is described and illustrated with a simulation example. To facilitate the application of this maneuver planning approach in aircraft loss of control (LOC) situations when the model is only identified at the current trim condition but when these sets need to be predicted at other flight conditions, the dependence trends of the safe and recoverable sets on aircraft flight conditions are characterized. The scaling procedure to estimate subsets of safe and recoverable sets at one trim condition based on their knowledge at another trim condition is defined. Finally, two control schemes that exploit integral safe sets are proposed. The first scheme, referred to as the controller state governor (CSG), resets the controller state (typically an integrator) to enforce the constraints and enlarge the set of plant states that can be recovered without constraint violation. The second scheme, referred to as the controller state and reference governor (CSRG), combines the controller state governor with the reference governor control architecture and provides the capability of simultaneously modifying the reference command and the controller state to enforce the constraints. Theoretical results that characterize the response properties of both schemes are presented. Examples are reported that illustrate the operation of these schemes on aircraft flight dynamics models and gas turbine engine dynamic models.
On identified predictive control
NASA Technical Reports Server (NTRS)
Bialasiewicz, Jan T.
1993-01-01
Self-tuning control algorithms are potential successors to manually tuned PID controllers traditionally used in process control applications. A very attractive design method for self-tuning controllers, which has been developed over recent years, is the long-range predictive control (LRPC). The success of LRPC is due to its effectiveness with plants of unknown order and dead-time which may be simultaneously nonminimum phase and unstable or have multiple lightly damped poles (as in the case of flexible structures or flexible robot arms). LRPC is a receding horizon strategy and can be, in general terms, summarized as follows. Using assumed long-range (or multi-step) cost function the optimal control law is found in terms of unknown parameters of the predictor model of the process, current input-output sequence, and future reference signal sequence. The common approach is to assume that the input-output process model is known or separately identified and then to find the parameters of the predictor model. Once these are known, the optimal control law determines control signal at the current time t which is applied at the process input and the whole procedure is repeated at the next time instant. Most of the recent research in this field is apparently centered around the LRPC formulation developed by Clarke et al., known as generalized predictive control (GPC). GPC uses ARIMAX/CARIMA model of the process in its input-output formulation. In this paper, the GPC formulation is used but the process predictor model is derived from the state space formulation of the ARIMAX model and is directly identified over the receding horizon, i.e., using current input-output sequence. The underlying technique in the design of identified predictive control (IPC) algorithm is the identification algorithm of observer/Kalman filter Markov parameters developed by Juang et al. at NASA Langley Research Center and successfully applied to identification of flexible structures.
Luo, Zhongkui; Wang, Enli; Sun, Osbert J
2017-01-23
Pool-based carbon (C) models are widely applied to predict soil C dynamics under global change and infer underlying mechanisms. However, it is unclear about the credibility of model-predicted C pool size, decay rate (k) and/or microbial C use efficiency (e) as only data on bulked total C is usually available for model-constraining. Using observing system simulation experiments (OSSE), we constrained a two-pool model using simulated datasets of total soil C dynamics under topical hypotheses on responses of soil C dynamics to warming and elevated CO2 (i.e., global change scenarios). The results indicated that the model predicted great uncertainties in C pool size, k and e under all global change scenarios, resulting in the difficulty to correctly infer the presupposed "real" values of those parameters that are used to generate the simulated total soil C for constraining the model. Furthermore, the model using the constrained parameters generated divergent future soil C dynamics. Compared with the predictions using the presupposed real parameters (i.e., the real future C dynamics), the percentage uncertainty in 100-year predictions using the constrained parameters was up to 45% depending on global change scenarios and data availability for model-constraining. Such great uncertainty was mainly due to the high collinearity among the model parameters. Using pool-based models, we argue that soil C pool size, k and/or e and their responses to global change have to be estimated explicitly and empirically, rather than through model-fitting, in order to accurately predict C dynamics and infer underlying mechanisms. The OSSE approach provides a powerful way to identify data requirement for the new generation of model development and test model performance. This article is protected by copyright. All rights reserved.
A dispersal-constrained habitat suitability model for predicting invasion of alpine vegetation.
Williams, Nicholas S G; Hahs, Amy K; Morgan, John W
2008-03-01
Developing tools to predict the location of new biological invasions is essential if exotic species are to be controlled before they become widespread. Currently, alpine areas in Australia are largely free of exotic plant species but face increasing pressure from invasive species due to global warming and intensified human use. To predict the potential spread of highly invasive orange hawkweed (Hieracium aurantiacum) from existing founder populations on the Bogong High Plains in southern Australia, we developed an expert-based, spatially explicit, dispersal-constrained, habitat suitability model. The model combines a habitat suitability index, developed from disturbance, site wetness, and vegetation community parameters, with a phenomenological dispersal kernel that uses wind direction and observed dispersal distances. After generating risk maps that defined the relative suitability of H. aurantiacum establishment across the study area, we intensively searched several locations to evaluate the model. The highest relative suitability for H. aurantiacum establishment was southeast from the initial infestations. Native tussock grasslands and disturbed areas had high suitability for H. aurantiacum establishment. Extensive field searches failed to detect new populations. Time-step evaluation using the location of populations known in 1998-2000, accurately assigned high relative suitability for locations where H. aurantiacum had established post-2003 (AUC [area under curve] = 0.855 +/- 0.035). This suggests our model has good predictive power and will improve the ability to detect populations and prioritize areas for ongoing monitoring.
Control of nanoparticle formation using the constrained dewetting of polymer brushes
NASA Astrophysics Data System (ADS)
Lee, Thomas; Hendy, Shaun C.; Neto, Chiara
2015-02-01
We have used coarse-grained molecular dynamics simulations to investigate the use of pinned micelles formed by the constrained dewetting of polymer brushes to act as a template for nanoparticle formation. The evaporation of a thin film containing a dissolved solute from a polymer brush was modeled to study the effect of solubility, concentration, grafting density, and evaporation rate on the nucleation and growth of nanoparticles. Control over particle nucleation could be imposed when the solution was dilute enough such that particle nucleation occurred following the onset of constrained dewetting. We predict that nanoparticles with sizes on the order of 1 nm to 10 nm could be produced from a range of organic molecules under experimentally accessible conditions. This method could allow the functionality of organic materials to potentially be imparted onto surfaces without the need for synthetic modification of the functional molecule, and with control over particle size and aggregation, for application in the preparation of surfaces with useful optical, pharmaceutical, or electronic properties.We have used coarse-grained molecular dynamics simulations to investigate the use of pinned micelles formed by the constrained dewetting of polymer brushes to act as a template for nanoparticle formation. The evaporation of a thin film containing a dissolved solute from a polymer brush was modeled to study the effect of solubility, concentration, grafting density, and evaporation rate on the nucleation and growth of nanoparticles. Control over particle nucleation could be imposed when the solution was dilute enough such that particle nucleation occurred following the onset of constrained dewetting. We predict that nanoparticles with sizes on the order of 1 nm to 10 nm could be produced from a range of organic molecules under experimentally accessible conditions. This method could allow the functionality of organic materials to potentially be imparted onto surfaces without the
Control of nanoparticle formation using the constrained dewetting of polymer brushes
NASA Astrophysics Data System (ADS)
Lee, Thomas; Hendy, Shaun C.; Neto, Chiara
2015-03-01
We have used coarse-grained molecular dynamics simulations to investigate the use of pinned micelles formed by the constrained dewetting of polymer brushes to act as a template for nanoparticle formation. The evaporation of a thin film containing a dissolved solute from a polymer brush was modeled to study the effect of solubility, concentration, grafting density, and evaporation rate on the nucleation and growth of nanoparticles. Control over particle nucleation could be imposed when the solution was dilute enough such that particle nucleation occurred following the onset of constrained dewetting. We predict that nanoparticles with sizes on the order of 1 nm to 10 nm could be produced from a range of organic molecules under experimentally accessable conditions. This method could allow the functionality of organic materials to be imparted onto surfaces without the need for synthetic modification of the functional molecule, and with control over particle size and aggregation, allowing for the preparation of surfaces with useful optical, pharmaceutical, or electronic properties. Now at Department of Civil and Environmental Engineering, Massachusettes Institute of Technology, Cambridge, MA.
Global velocity constrained cloud motion prediction for short-term solar forecasting
NASA Astrophysics Data System (ADS)
Chen, Yanjun; Li, Wei; Zhang, Chongyang; Hu, Chuanping
2016-09-01
Cloud motion is the primary reason for short-term solar power output fluctuation. In this work, a new cloud motion estimation algorithm using a global velocity constraint is proposed. Compared to the most used Particle Image Velocity (PIV) algorithm, which assumes the homogeneity of motion vectors, the proposed method can capture the accurate motion vector for each cloud block, including both the motional tendency and morphological changes. Specifically, global velocity derived from PIV is first calculated, and then fine-grained cloud motion estimation can be achieved by global velocity based cloud block researching and multi-scale cloud block matching. Experimental results show that the proposed global velocity constrained cloud motion prediction achieves comparable performance to the existing PIV and filtered PIV algorithms, especially in a short prediction horizon.
An framework for robust flight control design using constrained optimization
NASA Technical Reports Server (NTRS)
Palazoglu, A.; Yousefpor, M.; Hess, R. A.
1992-01-01
An analytical framework is described for the design of feedback control systems to meet specified performance criteria in the presence of structured and unstructured uncertainty. Attention is focused upon the linear time invariant, single-input, single-output problem for the purposes of exposition. The framework provides for control of the degree of the stabilizing compensator or controller.
State-Constrained Optimal Control Problems of Impulsive Differential Equations
Forcadel, Nicolas; Rao Zhiping Zidani, Hasnaa
2013-08-01
The present paper studies an optimal control problem governed by measure driven differential systems and in presence of state constraints. The first result shows that using the graph completion of the measure, the optimal solutions can be obtained by solving a reparametrized control problem of absolutely continuous trajectories but with time-dependent state-constraints. The second result shows that it is possible to characterize the epigraph of the reparametrized value function by a Hamilton-Jacobi equation without assuming any controllability assumption.
Improved Sensitivity Relations in State Constrained Optimal Control
Bettiol, Piernicola; Frankowska, Hélène; Vinter, Richard B.
2015-04-15
Sensitivity relations in optimal control provide an interpretation of the costate trajectory and the Hamiltonian, evaluated along an optimal trajectory, in terms of gradients of the value function. While sensitivity relations are a straightforward consequence of standard transversality conditions for state constraint free optimal control problems formulated in terms of control-dependent differential equations with smooth data, their verification for problems with either pathwise state constraints, nonsmooth data, or for problems where the dynamic constraint takes the form of a differential inclusion, requires careful analysis. In this paper we establish validity of both ‘full’ and ‘partial’ sensitivity relations for an adjoint state of the maximum principle, for optimal control problems with pathwise state constraints, where the underlying control system is described by a differential inclusion. The partial sensitivity relation interprets the costate in terms of partial Clarke subgradients of the value function with respect to the state variable, while the full sensitivity relation interprets the couple, comprising the costate and Hamiltonian, as the Clarke subgradient of the value function with respect to both time and state variables. These relations are distinct because, for nonsmooth data, the partial Clarke subdifferential does not coincide with the projection of the (full) Clarke subdifferential on the relevant coordinate space. We show for the first time (even for problems without state constraints) that a costate trajectory can be chosen to satisfy the partial and full sensitivity relations simultaneously. The partial sensitivity relation in this paper is new for state constraint problems, while the full sensitivity relation improves on earlier results in the literature (for optimal control problems formulated in terms of Lipschitz continuous multifunctions), because a less restrictive inward pointing hypothesis is invoked in the proof, and because
Multiplexed Predictive Control of a Large Commercial Turbofan Engine
NASA Technical Reports Server (NTRS)
Richter, hanz; Singaraju, Anil; Litt, Jonathan S.
2008-01-01
Model predictive control is a strategy well-suited to handle the highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. However, it has thus far been infeasible to implement model predictive control in engine control applications, because of the combination of model complexity and the time allotted for the control update calculation. In this paper, a multiplexed implementation is proposed that dramatically reduces the computational burden of the quadratic programming optimization that must be solved online as part of the model-predictive-control algorithm. Actuator updates are calculated sequentially and cyclically in a multiplexed implementation, as opposed to the simultaneous optimization taking place in conventional model predictive control. Theoretical aspects are discussed based on a nominal model, and actual computational savings are demonstrated using a realistic commercial engine model.
Measurements of human force control during a constrained arm motion using a force-actuated joystick.
McIntyre, J; Gurfinkel, E V; Lipshits, M I; Droulez, J; Gurfinkel, V S
1995-03-01
1. When interacting with the environment, human arm movements may be prevented in certain directions (i.e., when sliding the hand along a surface) resulting in what is called a "constrained motion." In the directions that the movement is restricted, the subject is instead free to control the forces against the constraint. 2. Control strategies for constrained motion may be characterized by two extreme models. Under the active compliance model, an essentially feedback-based approach, measurements of contact force may be used in real time to modify the motor command and precisely control the forces generated against the constraint. Under the passive compliance model the motion would be executed in a feedforward manner, using an internal model of the constraint geometry. The feedforward model relies on the compliant behavior of the passive mechanical system to maintain contact while avoiding excessive contact forces. 3. Subjects performed a task in which they were required to slide the hand along a rigid surface. This task was performed in a virtual force environment in which contact forces were simulated by a two-dimensional force-actuated joystick. Unknown to the subject, the orientation of the surface constraint was varied from trial to trial, and contact force changes induced by these perturbations were measured. 4. Subjects showed variations in contact force correlated with the direction of the orientation perturbation. "Upward" tilts resulted in higher contact forces, whereas "downward" tilts resulted in lower contact forces. This result is consistent with a feedforward-based control of a passively compliant system. 5. Subject responses did not, however, correspond exactly to the predictions of a static analysis of a passive, feedforward-controlled system. A dynamic analysis reveals a much closer resemblance between a passive, feedforward model and the observed data. Numerical simulations demonstrate that a passive, dynamic system model of the movement captures
De, Wang; Nie, Feiping; Huang, Heng; Yan, Jingwen; Risacher, Shannon L; Saykin, Andrew J; Shen, Li
2013-01-01
Neuroimaging markers have been widely used to predict the cognitive functions relevant to the progression of Alzheimer's disease (AD). Most previous studies identify the imaging markers without considering the brain structural correlations between neuroimaging measures. However, many neuroimaging markers interrelate and work together to reveal the cognitive functions, such that these relevant markers should be selected together as the phenotypic markers. To solve this problem, in this paper, we propose a novel network constrained feature selection (NCFS) model to identify the neuroimaging markers guided by the structural brain network, which is constructed by the sparse representation method such that the interrelations between neuroimaging features are encoded into probabilities. Our new methods are evaluated by the MRI and AV45-PET data from ADNI-GO and ADNI-2 (Alzheimer's Disease Neuroimaging Initiative). In all cognitive function prediction tasks, our new NCFS method outperforms other state-of-the-art regression approaches. Meanwhile, we show that the new method can select the correlated imaging markers, which are ignored by the competing approaches.
NASA Astrophysics Data System (ADS)
Denning, S.
2014-12-01
The carbon-climate community has an historic opportunity to make a step-function improvement in climate prediction by using regional constraints to improve mechanistic model representation of carbon cycle processes. Interactions among atmospheric CO2, global biogeochemistry, and physical climate constitute leading sources of uncertainty in future climate. First-order differences among leading models of these processes produce differences in climate as large as differences in aerosol-cloud-radiation interactions and fossil fuel combustion. Emergent constraints based on global observations of interannual variations provide powerful constraints on model parameterizations. Additional constraints can be defined at regional scales. Organized intercomparison experiments have shown that uncertainties in future carbon-climate feedback arise primarily from model representations of the dependence of photosynthesis on CO2 and drought stress and the dependence of decomposition on temperature. Just as representations of net carbon fluxes have benefited from eddy flux, ecosystem manipulations, and atmospheric CO2, component carbon fluxes (photosynthesis, respiration, decomposition, disturbance) can be constrained at regional scales using new observations. Examples include biogeochemical tracers such as isotopes and carbonyl sulfide as well as remotely-sensed parameters such as chlorophyll fluorescence and biomass. Innovative model evaluation experiments will be needed to leverage the information content of new observations to improve process representations as well as to provide accurate initial conditions for coupled climate model simulations. Successful implementation of a comprehensive benchmarking program could have a huge impact on understanding and predicting future climate change.
Understanding and constraining global controls on dust emissions from playas
NASA Astrophysics Data System (ADS)
Bryant, Robert; Eckardt, Frank; Vickery, Kate; Wiggs, Giles; Hipondoka, Martin; Murray, Jon; Baddock, Matt; Brindley, Helen; King, James; Nield, Jo; Thomas, Dave; Washington, Richard; Haustein, Karsten
2016-04-01
Playas are ephemeral, endorheic lake systems that are common in arid regions. They have been identified as both regionally and globally significant sources of mineral dust. Emissions of dust from large playas can therefore impact significantly on regional climate through a range of land/atmosphere interactions. However, not all playas have or will emit dust, and those that do emit dust rarely do so consistently. Thus, global models that target ephemeral lakes at source areas often struggle to model the emission characteristics of the locations accurately. It is clear that our understanding of controls on dust emission from these environments varies at global scales (i.e. relevant to climate models) is poorly understood. Existing research confirms that the potential for dust emission from playas within dryland regions can be extremely varied; large disparities are noted to exist from one playa to another, and significant spatial/temporal heterogeneity has been observed within those playas that do emit dust. Research also shows that dust fluxes from playa surfaces varies vary based on hydrological gradient or ephemeral inflows and may change over time in response to human or climate forcing mechanisms. Consequently, despite the presence of abundant fine sediment and suitable wind conditions, some playas will remain supply limited and will not emit dust as they are either too wet (e.g. via extensive groundwater discharge) not salty enough (e.g. salts have been removed from the surface by groundwater recharge) or there is not a sufficient supply of sand (coarse particles) on or at the upwind edge of the playa surface to cause dust emission. Other playas (e.g. Owens Lake) have emitted dust at a disproportionate (regionally/nationally) significant level seemingly without constraint (becoming effectively transport capacity limited) through optimal combinations of the same factors. Finally, we can also see situations where dust emitting playa systems flip between supply
Adaptive, Distributed Control of Constrained Multi-Agent Systems
NASA Technical Reports Server (NTRS)
Bieniawski, Stefan; Wolpert, David H.
2004-01-01
Product Distribution (PO) theory was recently developed as a broad framework for analyzing and optimizing distributed systems. Here we demonstrate its use for adaptive distributed control of Multi-Agent Systems (MASS), i.e., for distributed stochastic optimization using MAS s. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (Probability dist&&on on the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. One common way to find that equilibrium is to have each agent run a Reinforcement Learning (E) algorithm. PD theory reveals this to be a particular type of search algorithm for minimizing the Lagrangian. Typically that algorithm i s quite inefficient. A more principled alternative is to use a variant of Newton's method to minimize the Lagrangian. Here we compare this alternative to RL-based search in three sets of computer experiments. These are the N Queen s problem and bin-packing problem from the optimization literature, and the Bar problem from the distributed RL literature. Our results confirm that the PD-theory-based approach outperforms the RL-based scheme in all three domains.
Predicting and Controlling School Violence.
ERIC Educational Resources Information Center
Rich, John Martin
1992-01-01
Discusses the extent to which violence can be accurately predicted, suggesting interventions, control, and remediation. The educator's role in reducing violence includes dealing with the school, parents, media, and community. Educators need conflict resolution skills for defusing aggression and establishing better relations. (SM)
Empirically Constrained Predictions for Metal-line Emission from the Circumgalactic Medium
NASA Astrophysics Data System (ADS)
Corlies, Lauren; Schiminovich, David
2016-08-01
The circumgalactic medium (CGM) is one of the remaining least constrained components of galaxies and as such has significant potential for advancing galaxy formation theories. In this work, we vary the extragalactic ultraviolet background for a high-resolution cosmological simulation of a Milky-Way-like galaxy and examine the effect on the absorption and emission properties of metals in the CGM. We find that a reduced quasar background brings the column density predictions into better agreement with recent data. Similarly, when the observationally derived physical properties of the gas are compared to the simulation, we find that the simulation gas is always at temperatures approximately 0.5 dex higher. Thus, similar column densities can be produced from fundamentally different gas. However, emission maps can provide complementary information to the line-of-sight column densities to better derive gas properties. From the simulations, we find that the brightest emission is less sensitive to the extragalactic background and that it closely follows the fundamental filamentary structure of the halo. This becomes increasingly true as the galaxy evolves from z = 1 to z = 0 and the majority of the gas transitions to a hotter, more diffuse phase. For the brightest ions (C iii, C iv, O vi), detectable emission can extend as far as 120 kpc at z = 0. Finally, resolution is a limiting factor for the conclusions we can draw from emission observations, but with moderate resolution and reasonable detection limits, upcoming instrumentation should place constraints on the physical properties of the CGM.
Inhibitory Control Predicts Grammatical Ability
Ibbotson, Paul; Kearvell-White, Jennifer
2015-01-01
We present evidence that individual variation in grammatical ability can be predicted by individual variation in inhibitory control. We tested 81 5-year-olds using two classic tests from linguistics and psychology (Past Tense and the Stroop). Inhibitory control was a better predicator of grammatical ability than either vocabulary or age. Our explanation is that giving the correct response in both tests requires using a common cognitive capacity to inhibit unwanted competition. The implications are that understanding the developmental trajectory of language acquisition can benefit from integrating the developmental trajectory of non-linguistic faculties, such as executive control. PMID:26659926
Antonarakis, Alexander S; Saatchi, Sassan S; Chazdon, Robin L; Moorcroft, Paul R
2011-06-01
Insights into vegetation and aboveground biomass dynamics within terrestrial ecosystems have come almost exclusively from ground-based forest inventories that are limited in their spatial extent. Lidar and synthetic-aperture Radar are promising remote-sensing-based techniques for obtaining comprehensive measurements of forest structure at regional to global scales. In this study we investigate how Lidar-derived forest heights and Radar-derived aboveground biomass can be used to constrain the dynamics of the ED2 terrestrial biosphere model. Four-year simulations initialized with Lidar and Radar structure variables were compared against simulations initialized from forest-inventory data and output from a long-term potential-vegtation simulation. Both height and biomass initializations from Lidar and Radar measurements significantly improved the representation of forest structure within the model, eliminating the bias of too many large trees that arose in the potential-vegtation-initialized simulation. The Lidar and Radar initializations decreased the proportion of larger trees estimated by the potential vegetation by approximately 20-30%, matching the forest inventory. This resulted in improved predictions of ecosystem-scale carbon fluxes and structural dynamics compared to predictions from the potential-vegtation simulation. The Radar initialization produced biomass values that were 75% closer to the forest inventory, with Lidar initializations producing canopy height values closest to the forest inventory. Net primary production values for the Radar and Lidar initializations were around 6-8% closer to the forest inventory. Correcting the Lidar and Radar initializations for forest composition resulted in improved biomass and basal-area dynamics as well as leaf-area index. Correcting the Lidar and Radar initializations for forest composition and fine-scale structure by combining the remote-sensing measurements with ground-based inventory data further improved
Valencia-Palomo, G; Rossiter, J A
2011-01-01
This paper makes two key contributions. First, it tackles the issue of the availability of constrained predictive control for low-level control loops. Hence, it describes how the constrained control algorithm is embedded in an industrial programmable logic controller (PLC) using the IEC 61131-3 programming standard. Second, there is a definition and implementation of a novel auto-tuned predictive controller; the key novelty is that the modelling is based on relatively crude but pragmatic plant information. Laboratory experiment tests were carried out in two bench-scale laboratory systems to prove the effectiveness of the combined algorithm and hardware solution. For completeness, the results are compared with a commercial proportional-integral-derivative (PID) controller (also embedded in the PLC) using the most up to date auto-tuning rules.
Vogelsang, David A; Bonnici, Heidi M; Bergström, Zara M; Ranganath, Charan; Simons, Jon S
2016-08-01
To remember a previous event, it is often helpful to use goal-directed control processes to constrain what comes to mind during retrieval. Behavioral studies have demonstrated that incidental learning of new "foil" words in a recognition test is superior if the participant is trying to remember studied items that were semantically encoded compared to items that were non-semantically encoded. Here, we applied subsequent memory analysis to fMRI data to understand the neural mechanisms underlying the "foil effect". Participants encoded information during deep semantic and shallow non-semantic tasks and were tested in a subsequent blocked memory task to examine how orienting retrieval towards different types of information influences the incidental encoding of new words presented as foils during the memory test phase. To assess memory for foils, participants performed a further surprise old/new recognition test involving foil words that were encountered during the previous memory test blocks as well as completely new words. Subsequent memory effects, distinguishing successful versus unsuccessful incidental encoding of foils, were observed in regions that included the left inferior frontal gyrus and posterior parietal cortex. The left inferior frontal gyrus exhibited disproportionately larger subsequent memory effects for semantic than non-semantic foils, and significant overlap in activity during semantic, but not non-semantic, initial encoding and foil encoding. The results suggest that orienting retrieval towards different types of foils involves re-implementing the neurocognitive processes that were involved during initial encoding.
Adaptive, predictive controller for optimal process control
Brown, S.K.; Baum, C.C.; Bowling, P.S.; Buescher, K.L.; Hanagandi, V.M.; Hinde, R.F. Jr.; Jones, R.D.; Parkinson, W.J.
1995-12-01
One can derive a model for use in a Model Predictive Controller (MPC) from first principles or from experimental data. Until recently, both methods failed for all but the simplest processes. First principles are almost always incomplete and fitting to experimental data fails for dimensions greater than one as well as for non-linear cases. Several authors have suggested the use of a neural network to fit the experimental data to a multi-dimensional and/or non-linear model. Most networks, however, use simple sigmoid functions and backpropagation for fitting. Training of these networks generally requires large amounts of data and, consequently, very long training times. In 1993 we reported on the tuning and optimization of a negative ion source using a special neural network[2]. One of the properties of this network (CNLSnet), a modified radial basis function network, is that it is able to fit data with few basis functions. Another is that its training is linear resulting in guaranteed convergence and rapid training. We found the training to be rapid enough to support real-time control. This work has been extended to incorporate this network into an MPC using the model built by the network for predictive control. This controller has shown some remarkable capabilities in such non-linear applications as continuous stirred exothermic tank reactors and high-purity fractional distillation columns[3]. The controller is able not only to build an appropriate model from operating data but also to thin the network continuously so that the model adapts to changing plant conditions. The controller is discussed as well as its possible use in various of the difficult control problems that face this community.
Shear wave prediction using committee fuzzy model constrained by lithofacies, Zagros basin, SW Iran
NASA Astrophysics Data System (ADS)
Shiroodi, Sadjad Kazem; Ghafoori, Mohammad; Ansari, Hamid Reza; Lashkaripour, Golamreza; Ghanadian, Mostafa
2017-02-01
The main purpose of this study is to introduce the geological controlling factors in improving an intelligence-based model to estimate shear wave velocity from seismic attributes. The proposed method includes three main steps in the framework of geological events in a complex sedimentary succession located in the Persian Gulf. First, the best attributes were selected from extracted seismic data. Second, these attributes were transformed into shear wave velocity using fuzzy inference systems (FIS) such as Sugeno's fuzzy inference (SFIS), adaptive neuro-fuzzy inference (ANFIS) and optimized fuzzy inference (OFIS). Finally, a committee fuzzy machine (CFM) based on bat-inspired algorithm (BA) optimization was applied to combine previous predictions into an enhanced solution. In order to show the geological effect on improving the prediction, the main classes of predominate lithofacies in the reservoir of interest including shale, sand, and carbonate were selected and then the proposed algorithm was performed with and without lithofacies constraint. The results showed a good agreement between real and predicted shear wave velocity in the lithofacies-based model compared to the model without lithofacies especially in sand and carbonate.
NASA Astrophysics Data System (ADS)
Davidson, C. D.; Dietze, M.
2011-12-01
Arctic climate is warming at a rate disproportionate to the rest of the world, and recent interest has emerged in using terrestrial biosphere models to understand and predict the response of tundra ecosystems to such warming. Of particular interest are the potential feedbacks between permafrost melting, plant community dynamics, and biogeochemical cycles. Here, we report on efforts to calibrate and validate version 2 of the Ecosystem Demography model (ED2) for the Alaskan tundra and on the use of model analyses to motivate targeted field measurements. ED2 is a terrestrial biosphere model unique in its ability to scale physiological and plant community dynamics to regional levels. We began by assessing the ability of ED2's land surface model to capture permafrost thermodynamics and hydrology. Simulations at Barrow and Toolik Lake, Alaska bore several incongruities with observed data, with soil temperatures significantly higher and soil moisture lower than observed. Modifications were made to increase the soil column depth and to simulate the effect of wind compaction on snow density, and in turn, the insulation of winter soils. In addition to these changes, a new soil class was created to represent unique characteristics within the organic horizon of tundra soils. Together these changes significantly improved permafrost dynamics without substantially altering dynamics in the temperate region. To capture tundra vegetation dynamics, tundra species were classified into three plant functional types (graminoid, deciduous shrub, evergreen shrub). ED2 was then iteratively calibrated for the tundra using the Predictive Ecosystem Analyzer (PEcAn), a scientific workflow and ecoinformatics toolbox developed to aid model parameterization and analysis. Initial parameter estimates were derived from a formal Bayesian meta-analysis of compiled plant trait data. Sensitivity analyses and variance decomposition demonstrated that model uncertainties were driven by the minimum
NASA Astrophysics Data System (ADS)
Hau, L. C.; Fung, E. H. K.
2004-08-01
This work presents the use of a multi-objective genetic algorithm (MOGA) to solve an integrated optimization problem for the shape control of flexible beams with an active constrained layer damping (ACLD) treatment. The design objectives are to minimize the total weight of the system, the input voltages and the steady-state error between the achieved and desired shapes. Design variables include the thickness of the constraining and viscoelastic layers, the arrangement of the ACLD patches, as well as the control gains. In order to set up an evaluator for the MOGA, the finite element method (FEM), in conjunction with the Golla-Hughes-McTavish (GHM) method, is employed to model a clamped-free beam with ACLD patches to predict the dynamic behaviour of the system. As a result of the optimization, reasonable Pareto solutions are successfully obtained. It is shown that ACLD treatment is suitable for shape control of flexible structures and that the MOGA is applicable to the present integrated optimization problem.
NASA Astrophysics Data System (ADS)
Hadi, Fatemeh; Janbozorgi, Mohammad; Sheikhi, M. Reza H.; Metghalchi, Hameed
2016-10-01
The rate-controlled constrained-equilibrium (RCCE) method is employed to study the interactions between mixing and chemical reaction. Considering that mixing can influence the RCCE state, the key objective is to assess the accuracy and numerical performance of the method in simulations involving both reaction and mixing. The RCCE formulation includes rate equations for constraint potentials, density and temperature, which allows taking account of mixing alongside chemical reaction without splitting. The RCCE is a dimension reduction method for chemical kinetics based on thermodynamics laws. It describes the time evolution of reacting systems using a series of constrained-equilibrium states determined by RCCE constraints. The full chemical composition at each state is obtained by maximizing the entropy subject to the instantaneous values of the constraints. The RCCE is applied to a spatially homogeneous constant pressure partially stirred reactor (PaSR) involving methane combustion in oxygen. Simulations are carried out over a wide range of initial temperatures and equivalence ratios. The chemical kinetics, comprised of 29 species and 133 reaction steps, is represented by 12 RCCE constraints. The RCCE predictions are compared with those obtained by direct integration of the same kinetics, termed detailed kinetics model (DKM). The RCCE shows accurate prediction of combustion in PaSR with different mixing intensities. The method also demonstrates reduced numerical stiffness and overall computational cost compared to DKM.
NASA Astrophysics Data System (ADS)
Lee, Haksu; Seo, Dong-Jun; Noh, Seong Jin
2016-11-01
This paper presents a simple yet effective weakly-constrained (WC) data assimilation (DA) approach for hydrologic models which accounts for model structural inadequacies associated with rainfall-runoff transformation processes. Compared to the strongly-constrained (SC) DA, WC DA adjusts the control variables less while producing similarly or more accurate analysis. Hence the adjusted model states are dynamically more consistent with those of the base model. The inadequacy of a rainfall-runoff model was modeled as an additive error to runoff components prior to routing and penalized in the objective function. Two example modeling applications, distributed and lumped, were carried out to investigate the effects of the WC DA approach on DA results. For distributed modeling, the distributed Sacramento Soil Moisture Accounting (SAC-SMA) model was applied to the TIFM7 Basin in Missouri, USA. For lumped modeling, the lumped SAC-SMA model was applied to nineteen basins in Texas. In both cases, the variational DA (VAR) technique was used to assimilate discharge data at the basin outlet. For distributed SAC-SMA, spatially homogeneous error modeling yielded updated states that are spatially much more similar to the a priori states, as quantified by Earth Mover's Distance (EMD), than spatially heterogeneous error modeling by up to ∼10 times. DA experiments using both lumped and distributed SAC-SMA modeling indicated that assimilating outlet flow using the WC approach generally produce smaller mean absolute difference as well as higher correlation between the a priori and the updated states than the SC approach, while producing similar or smaller root mean square error of streamflow analysis and prediction. Large differences were found in both lumped and distributed modeling cases between the updated and the a priori lower zone tension and primary free water contents for both WC and SC approaches, indicating possible model structural deficiency in describing low flows or
NASA Astrophysics Data System (ADS)
Chen, Zhang; Liang, Bin; Zhang, Tao
2016-05-01
When teleoperations are implemented in the constrained environment, the lack of environment information would lead to contacts and undesired excessive contact forces, which are more evident with the existence of time delays. In this paper, a hybrid compliant bilateral controller is proposed to deal with this problem. The controller adopts a self-adjusting selecting scheme to divide the subspaces online. The master and slave manipulators are synchronized in the position subspace through an adaptive bilateral control scheme. At the same time, the slave manipulator is controlled by a local sliding mode impedance controller in order to achieve the desired compliant motion when contacting with the environment. Theoretical analysis proves the stability of the hybrid bilateral controller and concludes the transient performance of the teleoperators. Simulations are carried out to verify the effectiveness of the proposed approach. The results show that the control goals are all achieved.
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.
Broadband Noise Control Using Predictive Techniques
NASA Technical Reports Server (NTRS)
Eure, Kenneth W.; Juang, Jer-Nan
1997-01-01
Predictive controllers have found applications in a wide range of industrial processes. Two types of such controllers are generalized predictive control and deadbeat control. Recently, deadbeat control has been augmented to include an extended horizon. This modification, named deadbeat predictive control, retains the advantage of guaranteed stability and offers a novel way of control weighting. This paper presents an application of both predictive control techniques to vibration suppression of plate modes. Several system identification routines are presented. Both algorithms are outlined and shown to be useful in the suppression of plate vibrations. Experimental results are given and the algorithms are shown to be applicable to non- minimal phase systems.
NASA Astrophysics Data System (ADS)
Hau, L. C.; Fung, E. H. K.; Yau, D. T. W.
2006-12-01
This paper describes the use of the multi-objective genetic algorithm (MOGA) to solve an integrated optimization problem of a rotating flexible arm with active constrained layer damping (ACLD) treatment. The arm is rotating in a horizontal plane with triangular velocity profiles. The ACLD patch is placed at the clamped end of the arm. The design objectives are to minimize the total treatment weight, the control voltage and the tip displacement of the arm, as well as to maximize the passive damping characteristic of the arm. Design variables include the control gains, the maximum angular velocity, the shear modulus of the viscoelastic layer, the thickness of the piezoelectric constraining and viscoelastic layers, and the length of the ACLD patch. In order to evaluate the effect of different combinations of design variables on the system, the finite element method, in conjunction with the Golla-Hughes-McTavish (GHM) method, is employed to model the flexible arm with ACLD treatment to predict its dynamic behavior, in which the effects of centrifugal stiffening due to the rotation of flexible arm are taken into account. As a result of optimization, reasonable Pareto solutions are successfully obtained. It is shown that the MOGA is applicable to the present integrated optimization problem.
CLFs-based optimization control for a class of constrained visual servoing systems.
Song, Xiulan; Miaomiao, Fu
2017-03-01
In this paper, we use the control Lyapunov function (CLF) technique to present an optimized visual servo control method for constrained eye-in-hand robot visual servoing systems. With the knowledge of camera intrinsic parameters and depth of target changes, visual servo control laws (i.e. translation speed) with adjustable parameters are derived by image point features and some known CLF of the visual servoing system. The Fibonacci method is employed to online compute the optimal value of those adjustable parameters, which yields an optimized control law to satisfy constraints of the visual servoing system. The Lyapunov's theorem and the properties of CLF are used to establish stability of the constrained visual servoing system in the closed-loop with the optimized control law. One merit of the presented method is that there is no requirement of online calculating the pseudo-inverse of the image Jacobian's matrix and the homography matrix. Simulation and experimental results illustrated the effectiveness of the method proposed here.
Nugent, Timothy; Mole, Sara E; Jones, David T
2008-04-02
The CLN3 gene encodes an integral membrane protein of unknown function. Mutations in CLN3 can cause juvenile neuronal ceroid lipofuscinosis, or Batten disease, an inherited neurodegenerative lysosomal storage disease affecting children. Here, we report a topological study of the CLN3 protein using bioinformatic approaches constrained by experimental data. Our results suggest that CLN3 has a six transmembrane helix topology with cytoplasmic N and C-termini, three large lumenal loops, one of which may contain an amphipathic helix, and one large cytoplasmic loop. Surprisingly, varied topological predictions were made using different subsets of orthologous sequences, highlighting the challenges still remaining for bioinformatics.
NASA Astrophysics Data System (ADS)
Beri, Rajan; Chattopadhyay, Aditi; Nam, Changho
2000-06-01
A rigorous multi-objective optimization procedure, is developed to address the integrated structures/control design of composite plates with surface bonded segmented active constrained layer (ACL) damping treatment. The Kresselmeier- Steinhauser function approach is used to formulate this multidisciplinary problem. The goal is to control vibration without incorporating a weight penalty. Objective functions and constraints include damping ratios, structural weight and natural frequencies. Design variables include the ply stacking sequence, dimensions and placement of segmented ACL. The optimal designs show improved plate vibratory characteristics and reduced structural weight. The results of the multi- objective optimization problem are compared to those of a single objective optimization with vibration control as the objective. Results establish the necessity for developing the integrated structures/controls optimization procedure.
NASA Technical Reports Server (NTRS)
Parker, Kevin Kit; Brock, Amy Lepre; Brangwynne, Cliff; Mannix, Robert J.; Wang, Ning; Ostuni, Emanuele; Geisse, Nicholas A.; Adams, Josephine C.; Whitesides, George M.; Ingber, Donald E.
2002-01-01
Directed cell migration is critical for tissue morphogenesis and wound healing, but the mechanism of directional control is poorly understood. Here we show that the direction in which cells extend their leading edge can be controlled by constraining cell shape using micrometer-sized extracellular matrix (ECM) islands. When cultured on square ECM islands in the presence of motility factors, cells preferentially extended lamellipodia, filopodia, and microspikes from their corners. Square cells reoriented their stress fibers and focal adhesions so that tractional forces were concentrated in these corner regions. When cell tension was dissipated, lamellipodia extension ceased. Mechanical interactions between cells and ECM that modulate cytoskeletal tension may therefore play a key role in the control of directional cell motility.
Generalized Predictive and Neural Generalized Predictive Control of Aerospace Systems
NASA Technical Reports Server (NTRS)
Kelkar, Atul G.
2000-01-01
The research work presented in this thesis addresses the problem of robust control of uncertain linear and nonlinear systems using Neural network-based Generalized Predictive Control (NGPC) methodology. A brief overview of predictive control and its comparison with Linear Quadratic (LQ) control is given to emphasize advantages and drawbacks of predictive control methods. It is shown that the Generalized Predictive Control (GPC) methodology overcomes the drawbacks associated with traditional LQ control as well as conventional predictive control methods. It is shown that in spite of the model-based nature of GPC it has good robustness properties being special case of receding horizon control. The conditions for choosing tuning parameters for GPC to ensure closed-loop stability are derived. A neural network-based GPC architecture is proposed for the control of linear and nonlinear uncertain systems. A methodology to account for parametric uncertainty in the system is proposed using on-line training capability of multi-layer neural network. Several simulation examples and results from real-time experiments are given to demonstrate the effectiveness of the proposed methodology.
Neural adaptive chaotic control with constrained input using state and output feedback
NASA Astrophysics Data System (ADS)
Gao, Shi-Gen; Dong, Hai-Rong; Sun, Xu-Bin; Ning, Bin
2015-01-01
This paper presents neural adaptive control methods for a class of chaotic nonlinear systems in the presence of constrained input and unknown dynamics. To attenuate the influence of constrained input caused by actuator saturation, an effective auxiliary system is constructed to prevent the stability of closed loop system from being destroyed. Radial basis function neural networks (RBF-NNs) are used in the online learning of the unknown dynamics, which do not require an off-line training phase. Both state and output feedback control laws are developed. In the output feedback case, high-order sliding mode (HOSM) observer is utilized to estimate the unmeasurable system states. Simulation results are presented to verify the effectiveness of proposed schemes. Project supported by the National High Technology Research and Development Program of China (Grant No. 2012AA041701), the Fundamental Research Funds for Central Universities of China (Grant No. 2013JBZ007), the National Natural Science Foundation of China (Grant Nos. 61233001, 61322307, 61304196, and 61304157), and the Research Program of Beijing Jiaotong University, China (Grant No. RCS2012ZZ003).
Reinforcement learning solution for HJB equation arising in constrained optimal control problem.
Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong
2015-11-01
The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations.
Predicted functional RNAs within coding regions constrain evolutionary rates of yeast proteins.
Warden, Charles D; Kim, Seong-Ho; Yi, Soojin V
2008-02-13
Functional RNAs (fRNAs) are being recognized as an important regulatory component in biological processes. Interestingly, recent computational studies suggest that the number and biological significance of functional RNAs within coding regions (coding fRNAs) may have been underestimated. We hypothesized that such coding fRNAs will impose additional constraint on sequence evolution because the DNA primary sequence has to simultaneously code for functional RNA secondary structures on the messenger RNA in addition to the amino acid codons for the protein sequence. To test this prediction, we first utilized computational methods to predict conserved fRNA secondary structures within multiple species alignments of Saccharomyces sensu strico genomes. We predict that as much as 5% of the genes in the yeast genome contain at least one functional RNA secondary structure within their protein-coding region. We then analyzed the impact of coding fRNAs on the evolutionary rate of protein-coding genes because a decrease in evolutionary rate implies constraint due to biological functionality. We found that our predicted coding fRNAs have a significant influence on evolutionary rates (especially at synonymous sites), independent of other functional measures. Thus, coding fRNA may play a role on sequence evolution. Given that coding regions of humans and flies contain many more predicted coding fRNAs than yeast, the impact of coding fRNAs on sequence evolution may be substantial in genomes of higher eukaryotes.
Snow data assimilation-constrained land initialization improves seasonal temperature prediction
NASA Astrophysics Data System (ADS)
Lin, Peirong; Wei, Jiangfeng; Yang, Zong-Liang; Zhang, Yongfei; Zhang, Kai
2016-11-01
We present the first systematic study to quantify the impact of land initialization on seasonal temperature prediction in the Northern Hemisphere, emphasizing the role of land snow data assimilation (DA). Three suites of ensemble seasonal integrations are conducted for coupled land-atmosphere runs. The land component is initialized using datasets from (1) no DA, (2) assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF), and (3) assimilating both MODIS SCF and Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage. Results show that snow DA improves temperature predictions especially in the Tibetan Plateau (by 5-20%) and high latitudes. Improvements at low latitudes are seen immediately and last up to 60 days, whereas improvements at high latitudes only appear later in transitional seasons. At high latitudes, assimilating GRACE data results in marked and prolonged improvements (by 25%) due to large initial snow mass changes. This study has great implications for future land DA and seasonal climate prediction studies.
NASA Technical Reports Server (NTRS)
Douglass, Anne R.; Stolarski, Richard S.
1987-01-01
Atmospheric photochemistry models have been used to predict the sensitivity of the ozone layer to various perturbations. These same models also predict concentrations of chemical species in the present day atmosphere which can be compared to observations. Model results for both present day values and sensitivity to perturbation depend upon input data for reaction rates, photodissociation rates, and boundary conditions. A method of combining the results of a Monte Carlo uncertainty analysis with the existing set of present atmospheric species measurements is developed. The method is used to examine the range of values for the sensitivity of ozone to chlorine perturbations that is possible within the currently accepted ranges for input data. It is found that model runs which predict ozone column losses much greater than 10 percent as a result of present fluorocarbon fluxes produce concentrations and column amounts in the present atmosphere which are inconsistent with the measurements for ClO, HCl, NO, NO2, and HNO3.
PREDICTING CME EJECTA AND SHEATH FRONT ARRIVAL AT L1 WITH A DATA-CONSTRAINED PHYSICAL MODEL
Hess, Phillip; Zhang, Jie
2015-10-20
We present a method for predicting the arrival of a coronal mass ejection (CME) flux rope in situ, as well as the sheath of solar wind plasma accumulated ahead of the driver. For faster CMEs, the front of this sheath will be a shock. The method is based upon geometrical separate measurement of the CME ejecta and sheath. These measurements are used to constrain a drag-based model, improved by including both a height dependence and accurate de-projected velocities. We also constrain the geometry of the model to determine the error introduced as a function of the deviation of the CME nose from the Sun–Earth line. The CME standoff-distance in the heliosphere fit is also calculated, fit, and combined with the ejecta model to determine sheath arrival. Combining these factors allows us to create predictions for both fronts at the L1 point and compare them against observations. We demonstrate an ability to predict the sheath arrival with an average error of under 3.5 hr, with an rms error of about 1.58 hr. For the ejecta the error is less than 1.5 hr, with an rms error within 0.76 hr. We also discuss the physical implications of our model for CME expansion and density evolution. We show the power of our method with ideal data and demonstrate the practical implications of having a permanent L5 observer with space weather forecasting capabilities, while also discussing the limitations of the method that will have to be addressed in order to create a real-time forecasting tool.
Yen, Eric A.; Tsay, Aaron; Waldispuhl, Jerome; Vogel, Jackie
2014-01-01
Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and visualization of the hidden
NASA Astrophysics Data System (ADS)
Balamurugan, V.; Narayanan, S.
2003-10-01
In the present paper, the active-passive hybrid vibration control performance due to Enhanced Smart Constrained Layer Damping (ESCLD) treatment as proposed by Liao and Wang on plate like structures has been considered. This treatment consists of a viscoelastic layer constrained between a smart piezoelectric layer and the base structure being controlled. Also, the smart constraining layer is clamped to the base structure. This type of damping treatment has got both active and passive component of damping. The passive damping is through cyclic shearing of viscoelastic constrained layer which is further enhanced by activating the smart piezoelectric constraining layer and the active component of the damping is through the transfer of control moments from the piezoelectric layer to the base structure through the viscoelastic layer and also bypassed through the clamps. A plate finite element has been formulated using first order shear deformation theory, including the effect of transverse shear and rotary inertia. The effect of the viscoelastic shear layer and piezoelectric constraining layer on the mass and stiffness has been included in the model. The viscoelastic shear layer is modeled usig Golla-Hughes-McTavish (GHM) method, which is a time domain approach. The clamps (edge elements) are modeled as equivalent springs connecting the smart piezoelectric constraining layer with the structure to be controlled. LQR optimal control strategy is used to obtain optimal control gains. The effect of the viscoelastic material properties (shear modulus and loss factor) on the hybrid vibration control performance is studied for both SCLD (without edge elements) and ESCLD systems.
The Modelling and Vibration Control of Beams with Active Constrained Layer Damping
NASA Astrophysics Data System (ADS)
SHI, Y. M.; LI, Z. F.; HUA, H. X.; FU, Z. F.; LIU, T. X.
2001-08-01
The finite element method (FEM) is combined with the Golla-Hughes-McTavish (GHM) model of viscoelastic materials (VEM) to model a cantilever beam with active constrained layer damping treatments. This approach avoids time-consuming iteration in solving modal frequencies, modal damping ratios and responses. But the resultant finite element (FE) model has too many degrees of freedom (d.o.f.s) from the point of view of control, nor is it observable and controllable. A new model reduction procedure is proposed. An iterative dynamic condensation is performed in the physical space, and Guyan condensation is taken as an initial iteration approximation. A reduced order model (ROM) of suitable size emerges, but it is still not observable and controllable. Accordingly, a robust model reduction method is then employed in the state space. A numerical example proves that this procedure reduces the model and assures the stability, controllability and observability of the final reduced order model (FROM). Finally, a controller is designed by linear-quadratic Gaussian (LQG) method based on the FROM. The vibration attenuation is evident
Finite-dimensional constrained fuzzy control for a class of nonlinear distributed process systems.
Wu, Huai-Ning; Li, Han-Xiong
2007-10-01
This correspondence studies the problem of finite-dimensional constrained fuzzy control for a class of systems described by nonlinear parabolic partial differential equations (PDEs). Initially, Galerkin's method is applied to the PDE system to derive a nonlinear ordinary differential equation (ODE) system that accurately describes the dynamics of the dominant (slow) modes of the PDE system. Subsequently, a systematic modeling procedure is given to construct exactly a Takagi-Sugeno (T-S) fuzzy model for the finite-dimensional ODE system under state constraints. Then, based on the T-S fuzzy model, a sufficient condition for the existence of a stabilizing fuzzy controller is derived, which guarantees that the state constraints are satisfied and provides an upper bound on the quadratic performance function for the finite-dimensional slow system. The resulting fuzzy controllers can also guarantee the exponential stability of the closed-loop PDE system. Moreover, a local optimization algorithm based on the linear matrix inequalities is proposed to compute the feedback gain matrices of a suboptimal fuzzy controller in the sense of minimizing the quadratic performance bound. Finally, the proposed design method is applied to the control of the temperature profile of a catalytic rod.
Risk Prediction for Acute Hypotensive Patients by Using Gap Constrained Sequential Contrast Patterns
Ghosh, Shameek; Feng, Mengling; Nguyen, Hung; Li, Jinyan
2014-01-01
The development of acute hypotension in a critical care patient causes decreased tissue perfusion, which can lead to multiple organ failures. Existing systems that employ population level prognostic scores to stratify the risks of critical care patients based on hypotensive episodes are suboptimal in predicting impending critical conditions, or in directing an effective goal-oriented therapy. In this work, we propose a sequential pattern mining approach which target novel and informative sequential contrast patterns for the detection of hypotension episodes. Our results demonstrate the competitiveness of the approach, in terms of both prediction performance as well as knowledge interpretability. Hence, sequential patterns-based computational biomarkers can help comprehend unusual episodes in critical care patients ahead of time for early warning systems. Sequential patterns can thus aid in the development of a powerful critical care knowledge discovery framework for facilitating novel patient treatment plans. PMID:25954447
NASA Astrophysics Data System (ADS)
Kallel, Hichem
Three classes of postural adjustments are investigated with the view of a better understanding of the control mechanisms involved in human movement. The control mechanisms and responses of human or computer models to deliberately induced disturbances in postural adjustments are the focus of this dissertation. The classes of postural adjustments are automatic adjustments, (i.e. adjustments not involving voluntary deliberate movement), adjustments involving imposition of constraints for the purpose of maintaining support forces, and adjustments involving violation and imposition of constraints for the purpose of maintaining balance, (i.e. taking one or more steps). For each class, based on the physiological attributes of the control mechanisms in human movements, control strategies are developed to synthesize the desired postural response. The control strategies involve position and velocity feedback control, on line relegation control, and pre-stored trajectory control. Stability analysis for constrained and unconstrained maneuvers is carried out based on Lyapunov stability theorems. The analysis is based on multi-segment biped robots. Depending on the class of postural adjustments, different biped models are developed. An eight-segment three dimensional biped model is formulated for the study of automatic adjustments and adjustments for balance. For the study of adjustments for support, a four segment lateral biped model is considered. Muscle synergies in automatic adjustments are analyzed based on a three link six muscle system. The muscle synergies considered involve minimal muscle number and muscle co-activation. The role of active and passive feedback in these automatic adjustments is investigated based on the specified stiffness and damping of the segments. The effectiveness of the control strategies and the role of muscle synergies in automatic adjustments are demonstrated by a number of digital computer simulations.
H ∞ predictive control of networked control systems
NASA Astrophysics Data System (ADS)
Xia, Yuanqing; Li, Li; Liu, Guo-Ping; Shi, Peng
2011-06-01
This article is concerned with the problem of H ∞ predictive control of networked control system with random network delay. A new control scheme termed networked predictive control is proposed. This scheme mainly consists of the control prediction generator and network delay compensator. While designing the predictor, the control input to the actuator may be different due to networked induced time-delay and data dropout, and two cases are considered depending on the way that the observer obtains the plant control input u t . The necessary and sufficient conditions are given for the closed-loop networked predictive control system to be stochastically stable for different u t and random network delays in controller to actuator channel (CAC) and sensor to controller channel (SCC). A simulation study shows the effectiveness of the proposed scheme.
Constrained optimal controller for linear systems with state and control dependent disturbance
NASA Technical Reports Server (NTRS)
Basuthakur, S.
1975-01-01
The problem is posed with the additional constraints that the dynamic controller uses only noise-corrupted outputs, and that its dimension is significantly lower than that of a Kalman filter. The unknown disturbance is viewed as an adversary which tries to maximize a performance criterion: a criterion that the controller gains attempt to minimize. The optimal controller gains are determined by solving a nonlinear matrix two-point boundary value problem.
NASA Astrophysics Data System (ADS)
Krishnan, Hariharan
1993-06-01
This thesis is organized in two parts. In Part 1, control systems described by a class of nonlinear differential and algebraic equations are introduced. A procedure for local stabilization based on a local state realization is developed. An alternative approach to local stabilization is developed based on a classical linearization of the nonlinear differential-algebraic equations. A theoretical framework is established for solving a tracking problem associated with the differential-algebraic system. First, a simple procedure is developed for the design of a feedback control law which ensures, at least locally, that the tracking error in the closed loop system lies within any given bound if the reference inputs are sufficiently slowly varying. Next, by imposing additional assumptions, a procedure is developed for the design of a feedback control law which ensures that the tracking error in the closed loop system approaches zero exponentially for reference inputs which are not necessarily slowly varying. The control design methodologies are used for simultaneous force and position control in constrained robot systems. The differential-algebraic equations are shown to characterize the slow dynamics of a certain nonlinear control system in nonstandard singularly perturbed form. In Part 2, the attitude stabilization (reorientation) of a rigid spacecraft using only two control torques is considered. First, the case of momentum wheel actuators is considered. The complete spacecraft dynamics are not controllable. However, the spacecraft dynamics are small time locally controllable in a reduced sense. The reduced spacecraft dynamics cannot be asymptotically stabilized using continuous feedback, but a discontinuous feedback control strategy is constructed. Next, the case of gas jet actuators is considered. If the uncontrolled principal axis is not an axis of symmetry, the complete spacecraft dynamics are small time locally controllable. However, the spacecraft attitude
NASA Technical Reports Server (NTRS)
Krishnan, Hariharan
1993-01-01
This thesis is organized in two parts. In Part 1, control systems described by a class of nonlinear differential and algebraic equations are introduced. A procedure for local stabilization based on a local state realization is developed. An alternative approach to local stabilization is developed based on a classical linearization of the nonlinear differential-algebraic equations. A theoretical framework is established for solving a tracking problem associated with the differential-algebraic system. First, a simple procedure is developed for the design of a feedback control law which ensures, at least locally, that the tracking error in the closed loop system lies within any given bound if the reference inputs are sufficiently slowly varying. Next, by imposing additional assumptions, a procedure is developed for the design of a feedback control law which ensures that the tracking error in the closed loop system approaches zero exponentially for reference inputs which are not necessarily slowly varying. The control design methodologies are used for simultaneous force and position control in constrained robot systems. The differential-algebraic equations are shown to characterize the slow dynamics of a certain nonlinear control system in nonstandard singularly perturbed form. In Part 2, the attitude stabilization (reorientation) of a rigid spacecraft using only two control torques is considered. First, the case of momentum wheel actuators is considered. The complete spacecraft dynamics are not controllable. However, the spacecraft dynamics are small time locally controllable in a reduced sense. The reduced spacecraft dynamics cannot be asymptotically stabilized using continuous feedback, but a discontinuous feedback control strategy is constructed. Next, the case of gas jet actuators is considered. If the uncontrolled principal axis is not an axis of symmetry, the complete spacecraft dynamics are small time locally controllable. However, the spacecraft attitude
Predicting and Controlling Complex Networks
2015-06-22
networks and control . . . . . . . . . . . . . . . . . . . 7 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically...Ni, Y.-C. Lai, and C. Grebogi, “Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games,” Physical Review E 83...of Physics B 76, 179-183 (2010). 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games Biodiversity is
An efficient artificial bee colony algorithm with application to nonlinear predictive control
NASA Astrophysics Data System (ADS)
Ait Sahed, Oussama; Kara, Kamel; Benyoucef, Abousoufyane; Laid Hadjili, Mohamed
2016-05-01
In this paper a constrained nonlinear predictive control algorithm, that uses the artificial bee colony (ABC) algorithm to solve the optimization problem, is proposed. The main objective is to derive a simple and efficient control algorithm that can solve the nonlinear constrained optimization problem with minimal computational time. Indeed, a modified version, enhancing the exploring and the exploitation capabilities, of the ABC algorithm is proposed and used to design a nonlinear constrained predictive controller. This version allows addressing the premature and the slow convergence drawbacks of the standard ABC algorithm, using a modified search equation, a well-known organized distribution mechanism for the initial population and a new equation for the limit parameter. A convergence statistical analysis of the proposed algorithm, using some well-known benchmark functions is presented and compared with several other variants of the ABC algorithm. To demonstrate the efficiency of the proposed algorithm in solving engineering problems, the constrained nonlinear predictive control of the model of a Multi-Input Multi-Output industrial boiler is considered. The control performances of the proposed ABC algorithm-based controller are also compared to those obtained using some variants of the ABC algorithms.
2013-09-30
display a currently valid OMB control number. 1. REPORT DATE 30 SEP 2013 2. REPORT TYPE 3. DATES COVERED 00-00-2013 to 00-00-2013 4 . TITLE...antisymmetric component of MJO convection is in phase with the antisymmetric diurnal cycle. The former relationship breaks down during strong El Nino ...acquired through the MURI. 4 Figure 2: Multiscale spatiotemporal modes of the averaged temperature brightness field for 1992— 1993
A Robustly Stabilizing Model Predictive Control Algorithm
NASA Technical Reports Server (NTRS)
Ackmece, A. Behcet; Carson, John M., III
2007-01-01
A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.
Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes
Antolík, Ján; Hofer, Sonja B.; Bednar, James A.; Mrsic-Flogel, Thomas D.
2016-01-01
Accurate estimation of neuronal receptive fields is essential for understanding sensory processing in the early visual system. Yet a full characterization of receptive fields is still incomplete, especially with regard to natural visual stimuli and in complete populations of cortical neurons. While previous work has incorporated known structural properties of the early visual system, such as lateral connectivity, or imposing simple-cell-like receptive field structure, no study has exploited the fact that nearby V1 neurons share common feed-forward input from thalamus and other upstream cortical neurons. We introduce a new method for estimating receptive fields simultaneously for a population of V1 neurons, using a model-based analysis incorporating knowledge of the feed-forward visual hierarchy. We assume that a population of V1 neurons shares a common pool of thalamic inputs, and consists of two layers of simple and complex-like V1 neurons. When fit to recordings of a local population of mouse layer 2/3 V1 neurons, our model offers an accurate description of their response to natural images and significant improvement of prediction power over the current state-of-the-art methods. We show that the responses of a large local population of V1 neurons with locally diverse receptive fields can be described with surprisingly limited number of thalamic inputs, consistent with recent experimental findings. Our structural model not only offers an improved functional characterization of V1 neurons, but also provides a framework for studying the relationship between connectivity and function in visual cortical areas. PMID:27348548
Plasma Stabilization Based on Model Predictive Control
NASA Astrophysics Data System (ADS)
Sotnikova, Margarita
The nonlinear model predictive control algorithms for plasma current and shape stabilization are proposed. Such algorithms are quite suitable for the situations when the plant to be controlled has essentially nonlinear dynamics. Besides that, predictive model based control algorithms allow to take into account a lot of requirements and constraints involved both on the controlled and manipulated variables. The significant drawback of the algorithms is that they require a lot of time to compute control input at each sampling instant. In this paper the model predictive control algorithms are demonstrated by the example of plasma vertical stabilization for ITER-FEAT tokamak. The tuning of parameters of algorithms is performed in order to decrease computational load.
Linear predictive control with state variable constraints
NASA Astrophysics Data System (ADS)
Bdirina, K.; Djoudi, D.; Lagoun, M.
2012-11-01
While linear model predictive control is popular since the 70s of the past century, the 90s have witnessed a steadily increasing attention from control theoretists as well as control practitioners in the area of model predictive control (MPC). The practical interest is driven by the fact that today's processes need to be operated under tighter performance specifications. At the same time more and more constraints, stemming for example from environmental and safety considerations, need to besatisfied. Often these demands can only be met when process constraints are explicitly considered in the controller. Predictive control with constraints appears to be a well suited approach for this kind of problems. In this paper the basic principle of MPC with constraints is reviewed and some of the theoretical, computational, and implementation aspects of MPC are discussed. Furthermore the MPC with constraints was applied to linear example.
Experimental Investigation on Adaptive Robust Controller Designs Applied to Constrained Manipulators
Nogueira, Samuel L.; Pazelli, Tatiana F. P. A. T.; Siqueira, Adriano A. G.; Terra, Marco H.
2013-01-01
In this paper, two interlaced studies are presented. The first is directed to the design and construction of a dynamic 3D force/moment sensor. The device is applied to provide a feedback signal of forces and moments exerted by the robotic end-effector. This development has become an alternative solution to the existing multi-axis load cell based on static force and moment sensors. The second one shows an experimental investigation on the performance of four different adaptive nonlinear ℋ∞ control methods applied to a constrained manipulator subject to uncertainties in the model and external disturbances. Coordinated position and force control is evaluated. Adaptive procedures are based on neural networks and fuzzy systems applied in two different modeling strategies. The first modeling strategy requires a well-known nominal model for the robot, so that the intelligent systems are applied only to estimate the effects of uncertainties, unmodeled dynamics and external disturbances. The second strategy considers that the robot model is completely unknown and, therefore, intelligent systems are used to estimate these dynamics. A comparative study is conducted based on experimental implementations performed with an actual planar manipulator and with the dynamic force sensor developed for this purpose. PMID:23598503
Nonlinear stochastic controllers for power-flow-constrained vibratory energy harvesters
NASA Astrophysics Data System (ADS)
Cassidy, Ian L.; Scruggs, Jeffrey T.
2013-06-01
This study addresses the formulation of nonlinear feedback controllers for stochastically excited vibratory energy harvesters. Maximizing the average power generated from such systems requires the transducer current to be regulated using a bi-directional power electronic converter. There are many applications where the implementation of these types of converters is infeasible, due to the higher parasitic losses they must sustain. If instead the transducer current is regulated using a converter capable of single-directional power-flow, then these parasitic losses can be reduced significantly. However, the constraint on the power-flow directionality restricts the domain of feasible feedback laws. The only feasible linear feedback law imposes a static relationship between current and voltage, i.e., a static admittance. In stochastic response, the power generation performance can be enhanced significantly beyond that of the optimal static admittance, using nonlinear feedback. In this paper, a general approach to nonlinear control synthesis for power-flow-constrained energy harvesters is presented, which is analytically guaranteed to outperform the optimal static admittance in stationary stochastic response. Simulation results are presented for a single-degree-of-freedom resonant oscillator with an electromagnetic transducer, as well as for a piezoelectric bimorph cantilever beam.
Nogueira, Samuel L; Pazelli, Tatiana F P A T; Siqueira, Adriano A G; Terra, Marco H
2013-04-18
In this paper, two interlaced studies are presented. The first is directed to the design and construction of a dynamic 3D force/moment sensor. The device is applied to provide a feedback signal of forces and moments exerted by the robotic end-effector. This development has become an alternative solution to the existing multi-axis load cell based on static force and moment sensors. The second one shows an experimental investigation on the performance of four different adaptive nonlinear H∞ control methods applied to a constrained manipulator subject to uncertainties in the model and external disturbances. Coordinated position and force control is evaluated. Adaptive procedures are based on neural networks and fuzzy systems applied in two different modeling strategies. The first modeling strategy requires a well-known nominal model for the robot, so that the intelligent systems are applied only to estimate the effects of uncertainties, unmodeled dynamics and external disturbances. The second strategy considers that the robot model is completely unknown and, therefore, intelligent systems are used to estimate these dynamics. A comparative study is conducted based on experimental implementations performed with an actual planar manipulator and with the dynamic force sensor developed for this purpose.
Multi-Constrained Optimal Control of 3D Robotic Arm Manipulators
NASA Astrophysics Data System (ADS)
Trivailo, Pavel M.; Fujii, Hironori A.; Kojima, Hirohisa; Watanabe, Takeo
This paper presents a generic method for optimal motion planning for three-dimensional 3-DOF multi-link robotic manipulators. We consider the operation of the manipulator systems, which involve constrained payload transportation/ capture/release, which is a subject to the minimization of the user-defined objective function, enabling for example minimization of the time of the transfer and/or actuation efforts. It should be stressed out that the task is solved in the presence of arbitrary multiple additional constraints. The solutions of the associated nonlinear differential equations of motion are obtained numerically using the direct transcription method. The direct method seeks to transform the continuous optimal control problem into a discrete mathematical programming problem, which in turn is solved using a non-linear programming algorithm. By discretizing the state and control variables at a series of nodes, the integration of the dynamical equations of motion is not required. The Chebyshev pseudospectral method, due to its high accuracy and fast computation times, was chosen as the direct optimization method to be employed to solve the problem. To illustrate the capabilities of the methodology, maneuvering of RRR 3D robot manipulators were considered in detail. Their optimal operations were simulated for the manipulators, binded to move their effectors along the specified 2D plane and 3D spherical and cylindrical surfaces (imitating for example, welding, tooling or scanning robots).
Demand management in healthcare IT. Controlling IT demand to meet constrained IT resource supply.
Mohrmann, Gregg; Schlusberg, Craig; Kropf, Roger
2007-01-01
Healthcare is behind other industries in the ability to manage and control increasing demand for IT services, and to ensure that IT staff are available when and where needed. From everyday support requests to large capital projects, the IT department's ability to meet demand is limited. Organizational and IT leaders need to proactively address this issue and do a better job of predicting when services will be needed and whether appropriate resources will be available. This article describes the common issues that healthcare IT departments face in the efficient delivery of services as a result of factors such as budget constraints, skill sets and project dependencies. Best practices for controlling demand are discussed, including resource allocation, governance processes and a graphical analysis of forecasted vs. actual thresholds. Using specific healthcare provider examples, the article intends to provide IT management with an approach to predicting and controlling resource demand.
Model predictive control: A new approach
NASA Astrophysics Data System (ADS)
Nagy, Endre
2017-01-01
New methods are proposed in this paper for solution of the model predictive control problem. Nonlinear state space design techniques are also treated. For nonlinear state prediction (state evolution computation) a new predictor given with an operator is introduced and tested. Settling the model predictive control problem may be obtained through application of the principle "direct stochastic optimum tracking" with a simple algorithm, which can be derived from a previously developed optimization procedure. The final result is obtained through iterations. Two examples show the applicability and advantages of the method.
NASA Astrophysics Data System (ADS)
Blacksberg, Jordana; Eiler, John; Brown, Mike; Ehlmann, Bethany; Hand, Kevin; Hodyss, Robert; Mahjoub, Ahmed; Poston, Michael; Liu, Yang; Choukroun, Mathieu; Carey, Elizabeth; Wong, Ian
2014-11-01
Hypotheses based on recent dynamical models (e.g. the Nice Model) shape our current understanding of solar system evolution, suggesting radical rearrangement in the first hundreds of millions of years of its history, changing the orbital distances of Jupiter, Saturn, and a large number of small bodies. The goal of this work is to build a methodology to concretely tie individual solar system bodies to dynamical models using observables, providing evidence for their origins and evolutionary pathways. Ultimately, one could imagine identifying a set of chemical or mineralogical signatures that could quantitatively and predictably measure the radial distance at which icy and rocky bodies first accreted. The target of the work presented here is the Jupiter Trojan asteroids, predicted by the Nice Model to have initially formed in the Kuiper belt and later been scattered inward to co-orbit with Jupiter. Here we present our strategy which is fourfold: (1) Generate predictions about the mineralogical, chemical, and isotopic compositions of materials accreted in the early solar system as a function of distance from the Sun. (2) Use temperature and irradiation to simulate evolutionary processing of ices and silicates, and measure the alteration in spectral properties from the UV to mid-IR. (3) Characterize simulants to search for potential fingerprints of origin and processing pathways, and (4) Use telescopic observations to increase our knowledge of the Trojan asteroids, collecting data on populations and using spectroscopy to constrain their compositions. In addition to the overall strategy, we will present preliminary results on compositional modeling, observations, and the synthesis, processing, and characterization of laboratory simulants including ices and silicates. This work has been supported by the Keck Institute for Space Studies (KISS). The research described here was carried out at the Jet Propulsion Laboratory, Caltech, under a contract with the National
Stefanucci, Azzurra; Pinnen, Francesco; Feliciani, Federica; Cacciatore, Ivana; Lucente, Gino; Mollica, Adriano
2011-01-01
A successful design of peptidomimetics must come to terms with χ-space control. The incorporation of χ-space constrained amino acids into bioactive peptides renders the χ1 and χ2 torsional angles of pharmacophore amino acids critical for activity and selectivity as with other relevant structural features of the template. This review describes histidine analogues characterized by replacement of native α and/or β-hydrogen atoms with alkyl substituents as well as analogues with α, β-didehydro unsaturation or Cα-Cβ cyclopropane insertion (ACC derivatives). Attention is also dedicated to the relevant field of β-aminoacid chemistry by describing the synthesis of β2- and β3-models (β-hHis). Structural modifications leading to cyclic imino derivatives such as spinacine, aza-histidine and analogues with shortening or elongation of the native side chain (nor-histidine and homo-histidine, respectively) are also described. Examples of the use of the described analogues to replace native histidine in bioactive peptides are also given. PMID:21686155
NASA Astrophysics Data System (ADS)
Liu, Suihan; Burgueño, Rigoberto
2016-12-01
Axially compressed bilaterally constrained columns, which can attain multiple snap-through buckling events in their elastic postbuckling response, can be used as energy concentrators and mechanical triggers to transform external quasi-static displacement input to local high-rate motions and excite vibration-based piezoelectric transducers for energy harvesting devices. However, the buckling location with highest kinetic energy release along the element, and where piezoelectric oscillators should be optimally placed, cannot be controlled or isolated due to the changing buckling configurations. This paper proposes the concept of stiffness variations along the column to gain control of the buckling location for optimal placement of piezoelectric transducers. Prototyped non-prismatic columns with piece-wise varying thickness were fabricated through 3D printing for experimental characterization and numerical simulations were conducted using the finite element method. A simple theoretical model was also developed based on the stationary potential energy principle for predicting the critical line contact segment that triggers snap-through events and the buckling morphologies as compression proceeds. Results confirm that non-prismatic column designs allow control of the buckling location in the elastic postbuckling regime. Compared to prismatic columns, non-prismatic designs can attain a concentrated kinetic energy release spot and a higher number of snap-buckling mode transitions under the same global strain. The direct relation between the column’s dynamic response and the output voltage from piezoelectric oscillator transducers allows the tailorable postbuckling response of non-prismatic columns to be used as multi-stable energy concentrators with enhanced performance in micro-energy harvesters.
Fast predictive control of networked energy systems
NASA Astrophysics Data System (ADS)
Chuang, Frank Fu-Han
In this thesis we study the optimal control of networked energy systems. Networked energy systems consist of a collection of energy storage nodes and a network of links and inputs which allow energy to be exchanged, injected, or removed from the nodes. The nodes may exchange energy between each other autonomously or via controlled flows between the nodes. Examples of networked systems include building heating, ventilation, and air conditioning (HVAC) systems and networked battery systems. In the building system example, the nodes of the system are rooms which store thermal energy in the air and other elements which have thermal capacity. The rooms transfer energy autonomously through thermal conduction, convection, and radiation. Thermal energy can be injected into or removed from the rooms via conditioned air or slabs. In the case of a networked battery system, the batteries store electrical energy in their chemical cells. The batteries may be electrically linked so that a controller can move electrical charge from one battery to another. Networked energy systems are typically large-scale (contain many states and inputs), affected by uncertain forecasts and disturbances, and require fast computation on cheap embedded platforms. In this thesis, the optimal control technique we study is model predictive control for networked energy systems. Model predictive or receding horizon control is a time-domain optimization-based control technique which uses predictive models of a system to forecast its behavior and minimize a performance cost subject to system constraints. In this thesis we address two primary issues concerning model predictive control for networked energy systems: robustness to uncertainty in forecasts and reducing the complexity of the large-scale optimization problem for use in embedded platforms. The first half of the thesis deals primarily with the efficient computation of robust controllers for dealing with random and adversarial uncertainties in the
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.
Model predictive formation control of helicopter systems
NASA Astrophysics Data System (ADS)
Saffarian, Mehdi
In this thesis, a robust formation control framework for formation control of a group of helicopters is proposed and designed. The dynamic model of the helicopter has been developed and verified through simulations. The control framework is constructed using two main control schemes for navigation of a helicopter group in three-dimensional (3D) environments. Two schemes are designed to maintain the position of one helicopter with respect to one or two other neighboring members, respectively. The developed parameters can uniquely define the position of the helicopters with respect to each other and can be used for any other aerial and under water vehicles such as airplanes, spacecrafts and submarines. Also, since this approach is modular, it is possible to use it for desired number and form of the group helicopters. Using the defined control parameters, two decentralized controllers are designed based on Nonlinear Model Predictive Control (NMPC) algorithm technique. The framework performance has been tested through simulation of different formation scenarios.
Model predictive control for cooperative control of space robots
NASA Astrophysics Data System (ADS)
Kannan, Somasundar; Alamdari, Seyed Amin Sajadi; Dentler, Jan; Olivares-Mendez, Miguel A.; Voos, Holger
2017-01-01
The problem of Orbital Manipulation of Passive body is discussed here. Two scenarios including passive object rigidly attached to robotic servicers and passive body attached to servicers through manipulators are discussed. The Model Predictive Control (MPC) technique is briefly presented and successfully tested through simulations on two cases of position control of passive body in the orbit.
Model Predictive Control of Sewer Networks
NASA Astrophysics Data System (ADS)
Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.
2017-01-01
The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.
NASA Astrophysics Data System (ADS)
Jaouadi, A.; Barrez, E.; Justum, Y.; Desouter-Lecomte, M.
2013-07-01
We simulate the implementation of a 3-qubit quantum Fourier transform gate in the hyperfine levels of ultracold polar alkali dimers in their first two lowest rotational levels. The chosen dimer is 41K87Rb supposed to be trapped in an optical lattice. The hyperfine levels are split by a static magnetic field. The pulses operating in the microwave domain are obtained by optimal control theory. We revisit the problem of phase control in information processing. We compare the efficiency of two optimal fields. The first one is obtained from a functional based on the average of the transition probabilities for each computational basis state but constrained by a supplementary transformation to enforce phase alignment. The second is obtained from a functional constructed on the phase sensitive fidelity involving the sum of the transition amplitudes without any supplementary constrain.
Predictive Control of Large Complex Networks
NASA Astrophysics Data System (ADS)
Haber, Aleksandar; Motter, Adilson E.
Networks of coupled dynamical subsystems are increasingly used to represent complex natural and engineered systems. While recent technological developments give us improved means to actively control the dynamics of individual subsystems in various domains, network control remains a challenging problem due to difficulties imposed by intrinsic nonlinearities, control constraints, and the large-scale nature of the systems. In this talk, we will present a model predictive control approach that is effective while accounting for these realistic properties of complex networks. Our method can systematically identify control interventions that steer the trajectory to a desired state, even in the presence of strong nonlinearities and constraints. Numerical tests show that the method is applicable to a variety of networks, ranging from power grids to chemical reaction systems.
NASA Astrophysics Data System (ADS)
Velarde, P.; Valverde, L.; Maestre, J. M.; Ocampo-Martinez, C.; Bordons, C.
2017-03-01
In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.
Model predictive control of MSMPR crystallizers
NASA Astrophysics Data System (ADS)
Moldoványi, Nóra; Lakatos, Béla G.; Szeifert, Ferenc
2005-02-01
A multi-input-multi-output (MIMO) control problem of isothermal continuous crystallizers is addressed in order to create an adequate model-based control system. The moment equation model of mixed suspension, mixed product removal (MSMPR) crystallizers that forms a dynamical system is used, the state of which is represented by the vector of six variables: the first four leading moments of the crystal size, solute concentration and solvent concentration. Hence, the time evolution of the system occurs in a bounded region of the six-dimensional phase space. The controlled variables are the mean size of the grain; the crystal size-distribution and the manipulated variables are the input concentration of the solute and the flow rate. The controllability and observability as well as the coupling between the inputs and the outputs was analyzed by simulation using the linearized model. It is shown that the crystallizer is a nonlinear MIMO system with strong coupling between the state variables. Considering the possibilities of the model reduction, a third-order model was found quite adequate for the model estimation in model predictive control (MPC). The mean crystal size and the variance of the size distribution can be nearly separately controlled by the residence time and the inlet solute concentration, respectively. By seeding, the controllability of the crystallizer increases significantly, and the overshoots and the oscillations become smaller. The results of the controlling study have shown that the linear MPC is an adaptable and feasible controller of continuous crystallizers.
Moissenet, Florent; Chèze, Laurence; Dumas, Raphaël
2012-06-01
Inverse dynamics combined with a constrained static optimization analysis has often been proposed to solve the muscular redundancy problem. Typically, the optimization problem consists in a cost function to be minimized and some equality and inequality constraints to be fulfilled. Penalty-based and Lagrange multipliers methods are common optimization methods for the equality constraints management. More recently, the pseudo-inverse method has been introduced in the field of biomechanics. The purpose of this paper is to evaluate the ability and the efficiency of this new method to solve the muscular redundancy problem, by comparing respectively the musculo-tendon forces prediction and its cost-effectiveness against common optimization methods. Since algorithm efficiency and equality constraints fulfillment highly belong to the optimization method, a two-phase procedure is proposed in order to identify and compare the complexity of the cost function, the number of iterations needed to find a solution and the computational time of the penalty-based method, the Lagrange multipliers method and pseudo-inverse method. Using a 2D knee musculo-skeletal model in an isometric context, the study of the cost functions isovalue curves shows that the solution space is 2D with the penalty-based method, 3D with the Lagrange multipliers method and 1D with the pseudo-inverse method. The minimal cost function area (defined as the area corresponding to 5% over the minimal cost) obtained for the pseudo-inverse method is very limited and along the solution space line, whereas the minimal cost function area obtained for other methods are larger or more complex. Moreover, when using a 3D lower limb musculo-skeletal model during a gait cycle simulation, the pseudo-inverse method provides the lowest number of iterations while Lagrange multipliers and pseudo-inverse method have almost the same computational time. The pseudo-inverse method, by providing a better suited cost function and an
Robust model predictive control of Wiener systems
NASA Astrophysics Data System (ADS)
Biagiola, S. I.; Figueroa, J. L.
2011-03-01
Block-oriented models (BOMs) have shown to be appealing and efficient as nonlinear representations for many applications. They are at the same time valid and simple models in a more extensive region than time-invariant linear models. In this work, Wiener models are considered. They are one of the most diffused BOMs, and their structure consists in a linear dynamics in cascade with a nonlinear static block. Particularly, the problem of control of these systems in the presence of uncertainty is treated. The proposed methodology makes use of a robust identification procedure in order to obtain a robust model to represent the uncertain system. This model is then employed to design a model predictive controller. The mathematical problem involved in the controller design is formulated in the context of the existing linear matrix inequalities (LMI) theory. The main feature of this approach is that it takes advantage of the static nature of the nonlinearity, which allows to solve the control problem by focusing only in the linear dynamics. This formulation results in a simplified design procedure, because the original nonlinear model predictive control (MPC) problem turns into a linear one.
Kuramatsu, Yuko; Muraki, Takayuki; Oouchida, Yutaka; Sekiguchi, Yusuke; Izumi, Shin-Ichi
2012-05-01
This study aimed to investigate the manner in which healthy individuals execute robust whole body movements despite unstable body structure from the perspective of perception-action coupling. Twelve healthy adults performed sit-to-stand (STS) movements under conditions of constrained visual and somatic senses. During this movement, centre of mass (COM) of the body in the anterior-posterior, upward-downward and right-left directions was computed. The conditions of perceptual constraint were set as vision-restricted, somatosensory-restricted, vision- and somatosensory-restricted, and normal conditions. To evaluate COM control under these perceptual constraints, the variability in position and velocity of COM were assessed. The variabilities in COM velocity in the anterior-posterior and upward-downward directions decreased around the lift-off period only when both vision and somatic senses were constrained, whereas the variability of the COM position in the right-left direction increased under the somatosensory-restricted condition. Our findings suggested that control of COM velocity was enhanced in the major moving directions (anterior and upward directions) around the lift-off period during STS when both modalities of perception with regard to postural orientation were constrained. These motor regulations with perceptual constraints facilitate better adaptation to changes in body and environmental situations in daily life.
NASA Astrophysics Data System (ADS)
Travis, K.; Jacob, D. J.; Fisher, J. A.; Marais, E. A.; Kim, S.; Zhu, L.; Yu, K.; Yantosca, R.; Payer Sulprizio, M.; Paulot, F.; Mao, J.; Wennberg, P. O.; Crounse, J.; Ryerson, T. B.; Wisthaler, A.; Huey, L. G.; Thompson, A. M.
2014-12-01
The Southeast United States (SEUS) is unique in its atmospheric chemistry and the difficulty of models in reproducing observed ozone (O3) (Fiore et al, 2009). Unlike the Western U.S., O3 variability is more heavily influenced by anthropogenic impacts than background sources such as wildfires, foreign transport, and stratospheric intrusions (Zhang et al, 2011). In addition, the SEUS has biogenic VOC emissions, important O3 precursors, which are among the highest in the world. We use observations from the SEAC4RS campaign over the SEUS in summer 2013, interpreted with the global chemical transport model GEOS-Chem, to evaluate the factors controlling O3 in this region. We use the GEOS-Chem model version v9-02 with significant updates, including improved treatment of isoprene nitrates (Lee et al, 2014), revised yields of MVK and MACR (Liu et al, 2013), improved treatment of isoprene epoxides (Bates et al, 2014), and faster deposition of isoprene oxidation products. The model significantly over predicts the observed O3, particularly in isoprene-rich, low-NOx regions. Properly capturing the fate of the isoprene peroxy radical (RO2) is essential to modeling O3 during the campaign. The amount of NOx in the SEUS is mainly driven by anthropogenic emissions with a smaller contribution from lightning and soil NOx, in addition to the amount of NOx recycled by isoprene nitrates. The variability in the amount of HOx available in the model can be influenced by the recycling of OH assumed in the GEOS-Chem chemical mechanism. We use the ratio of measured isoprene hydroxyperoxide (ISOPOOH) to isoprene nitrates (ISOPN) to constrain the modeled branching between the RO2 + HO2 and RO2 + NO2 pathways. Based on this ratio, we find that the RO2 + HO2 pathway is underestimated in our current chemical mechanism. Moreover, our NOx emissions may be overestimated by comparison with satellite tropospheric NO2 columns. We increase the importance of the RO2 + HO2 pathway with the inclusion of HONO
Wind farms production: Control and prediction
NASA Astrophysics Data System (ADS)
El-Fouly, Tarek Hussein Mostafa
Wind energy resources, unlike dispatchable central station generation, produce power dependable on external irregular source and that is the incident wind speed which does not always blow when electricity is needed. This results in the variability, unpredictability, and uncertainty of wind resources. Therefore, the integration of wind facilities to utility electrical grid presents a major challenge to power system operator. Such integration has significant impact on the optimum power flow, transmission congestion, power quality issues, system stability, load dispatch, and economic analysis. Due to the irregular nature of wind power production, accurate prediction represents the major challenge to power system operators. Therefore, in this thesis two novel models are proposed for wind speed and wind power prediction. One proposed model is dedicated to short-term prediction (one-hour ahead) and the other involves medium term prediction (one-day ahead). The accuracy of the proposed models is revealed by comparing their results with the corresponding values of a reference prediction model referred to as the persistent model. Utility grid operation is not only impacted by the uncertainty of the future production of wind farms, but also by the variability of their current production and how the active and reactive power exchange with the grid is controlled. To address this particular task, a control technique for wind turbines, driven by doubly-fed induction generators (DFIGs), is developed to regulate the terminal voltage by equally sharing the generated/absorbed reactive power between the rotor-side and the gridside converters. To highlight the impact of the new developed technique in reducing the power loss in the generator set, an economic analysis is carried out. Moreover, a new aggregated model for wind farms is proposed that accounts for the irregularity of the incident wind distribution throughout the farm layout. Specifically, this model includes the wake effect
Real-time Adaptive Control Using Neural Generalized Predictive Control
NASA Technical Reports Server (NTRS)
Haley, Pam; Soloway, Don; Gold, Brian
1999-01-01
The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.
Vinding, Mads S.; Guérin, Bastien; Vosegaard, Thomas; Nielsen, Niels Chr.
2016-01-01
Purpose To present a constrained optimal-control (OC) framework for designing large-flip-angle parallel-transmit (pTx) pulses satisfying hardware peak-power as well as regulatory local and global specific-absorption-rate (SAR) limits. The application is 2D and 3D spatial-selective 90° and 180° pulses. Theory and Methods The OC gradient-ascent-pulse-engineering method with exact gradients and the limited-memory Broyden-Fletcher-Goldfarb-Shanno method is proposed. Local SAR is constrained by the virtual-observation-points method. Two numerical models facilitated the optimizations, a torso at 3 T and a head at 7 T, both in eight-channel pTx coils and acceleration-factors up to 4. Results The proposed approach yielded excellent flip-angle distributions. Enforcing the local-SAR constraint, as opposed to peak power alone, reduced the local SAR 7 and 5-fold with the 2D torso excitation and inversion pulse, respectively. The root-mean-square errors of the magnetization profiles increased less than 5% with the acceleration factor of 4. Conclusion A local and global SAR, and peak-power constrained OC large-flip-angle pTx pulse design was presented, and numerically validated for 2D and 3D spatial-selective 90° and 180° pulses at 3 T and 7 T. PMID:26715084
NASA Astrophysics Data System (ADS)
Mirzaei, Mahmood; Tibaldi, Carlo; Hansen, Morten H.
2016-09-01
PI/PID controllers are the most common wind turbine controllers. Normally a first tuning is obtained using methods such as pole-placement or Ziegler-Nichols and then extensive aeroelastic simulations are used to obtain the best tuning in terms of regulation of the outputs and reduction of the loads. In the traditional tuning approaches, the properties of different open loop and closed loop transfer functions of the system are not normally considered. In this paper, an assessment of the pole-placement tuning method is presented based on robustness measures. Then a constrained optimization setup is suggested to automatically tune the wind turbine controller subject to robustness constraints. The properties of the system such as the maximum sensitivity and complementary sensitivity functions (Ms and Mt ), along with some of the responses of the system, are used to investigate the controller performance and formulate the optimization problem. The cost function is the integral absolute error (IAE) of the rotational speed from a disturbance modeled as a step in wind speed. Linearized model of the DTU 10-MW reference wind turbine is obtained using HAWCStab2. Thereafter, the model is reduced with model order reduction. The trade-off curves are given to assess the tunings of the poles- placement method and a constrained optimization problem is solved to find the best tuning.
A constrained-gradient method to control divergence errors in numerical MHD
NASA Astrophysics Data System (ADS)
Hopkins, Philip F.
2016-10-01
In numerical magnetohydrodynamics (MHD), a major challenge is maintaining nabla \\cdot {B}=0. Constrained transport (CT) schemes achieve this but have been restricted to specific methods. For more general (meshless, moving-mesh, ALE) methods, `divergence-cleaning' schemes reduce the nabla \\cdot {B} errors; however they can still be significant and can lead to systematic errors which converge away slowly. We propose a new constrained gradient (CG) scheme which augments these with a projection step, and can be applied to any numerical scheme with a reconstruction. This iteratively approximates the least-squares minimizing, globally divergence-free reconstruction of the fluid. Unlike `locally divergence free' methods, this actually minimizes the numerically unstable nabla \\cdot {B} terms, without affecting the convergence order of the method. We implement this in the mesh-free code GIZMO and compare various test problems. Compared to cleaning schemes, our CG method reduces the maximum nabla \\cdot {B} errors by ˜1-3 orders of magnitude (˜2-5 dex below typical errors if no nabla \\cdot {B} cleaning is used). By preventing large nabla \\cdot {B} at discontinuities, this eliminates systematic errors at jumps. Our CG results are comparable to CT methods; for practical purposes, the nabla \\cdot {B} errors are eliminated. The cost is modest, ˜30 per cent of the hydro algorithm, and the CG correction can be implemented in a range of numerical MHD methods. While for many problems, we find Dedner-type cleaning schemes are sufficient for good results, we identify a range of problems where using only Powell or `8-wave' cleaning can produce order-of-magnitude errors.
Optimal Control of Distributed Energy Resources using Model Predictive Control
Mayhorn, Ebony T.; Kalsi, Karanjit; Elizondo, Marcelo A.; Zhang, Wei; Lu, Shuai; Samaan, Nader A.; Butler-Purry, Karen
2012-07-22
In an isolated power system (rural microgrid), Distributed Energy Resources (DERs) such as renewable energy resources (wind, solar), energy storage and demand response can be used to complement fossil fueled generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation. The problem is formulated as a multi-objective optimization problem with the goals of minimizing fuel costs and changes in power output of diesel generators, minimizing costs associated with low battery life of energy storage and maintaining system frequency at the nominal operating value. Two control modes are considered for controlling the energy storage to compensate either net load variability or wind variability. Model predictive control (MPC) is used to solve the aforementioned problem and the performance is compared to an open-loop look-ahead dispatch problem. Simulation studies using high and low wind profiles, as well as, different MPC prediction horizons demonstrate the efficacy of the closed-loop MPC in compensating for uncertainties in wind and demand.
Mazinan, A H
2016-03-01
The research addresses a Lyapunov-based constrained control strategy to deal with the autonomous space system in the presence of large disturbances. The aforementioned autonomous space system under control is first represented through a dynamics model and subsequently the proposed control strategy is fully investigated with a focus on the three-axis detumbling and the corresponding pointing mode control approaches. The three-axis detumbling mode control approach is designed to deal with the unwanted angular rates of the system to be zero, while the saturations of the actuators are taken into consideration. Moreover, the three-axis pointing mode control approach is designed in the similar state to deal with the rotational angles of the system to be desirable. The contribution of the research is mathematically made to propose a control law in connection with a new candidate of Lyapunov function to deal with the rotational angles and the related angular rates of the present autonomous space system with respect to state-of-the-art. A series of experiments are carried out to consider the efficiency of the proposed control strategy, as long as a number of benchmarks are realized in the same condition to verify and guarantee the strategy performance in both modes of control approaches.
NASA Astrophysics Data System (ADS)
BALAMURUGAN, V.; NARAYANAN, S.
2002-01-01
This work deals with the active vibration control of beams with smart constrained layer damping (SCLD) treatment. SCLD design consists of viscoelastic shear layer sandwiched between two layers of piezoelectric sensors and actuator. This composite SCLD when bonded to a vibrating structure acts as a smart treatment. The sensor piezoelectric layer measures the vibration response of the structure and a feedback controller is provided which regulates the axial deformation of the piezoelectric actuator (constraining layer), thereby providing adjustable and significant damping in the structure. The damping offered by SCLD treatment has two components, active action and passive action. The active action is transmitted from the piezoelectric actuator to the host structure through the viscoelastic layer. The passive action is through the shear deformation in the viscoelastic layer. The active action apart from providing direct active control also adjusts the passive action by regulating the shear deformation in the structure. The passive damping component of this design eliminates spillover, reduces power consumption, improves robustness and reliability of the system, and reduces vibration response at high-frequency ranges where active damping is difficult to implement. A beam finite element model has been developed based on Timoshenko's beam theory with partially covered SCLD. The Golla-Hughes-McTavish (GHM) method has been used to model the viscoelastic layer. The dissipation co-ordinates, defined using GHM approach, describe the frequency-dependent viscoelastic material properties. Models of PCLD and purely active systems could be obtained as a special case of SCLD. Using linear quadratic regulator (LQR) optimal control, the effects of the SCLD on vibration suppression performance and control effort requirements are investigated. The effects of the viscoelastic layer thickness and material properties on the vibration control performance are investigated.
NASA Astrophysics Data System (ADS)
Swaidan, Waleeda; Hussin, Amran
2015-10-01
Most direct methods solve finite time horizon optimal control problems with nonlinear programming solver. In this paper, we propose a numerical method for solving nonlinear optimal control problem with state and control inequality constraints. This method used quasilinearization technique and Haar wavelet operational matrix to convert the nonlinear optimal control problem into a quadratic programming problem. The linear inequality constraints for trajectories variables are converted to quadratic programming constraint by using Haar wavelet collocation method. The proposed method has been applied to solve Optimal Control of Multi-Item Inventory Model. The accuracy of the states, controls and cost can be improved by increasing the Haar wavelet resolution.
NASA Astrophysics Data System (ADS)
Bonne, F.; Alamir, M.; Bonnay, P.
2017-02-01
This paper deals with multivariable constrained model predictive control for Warm Compression Stations (WCS). WCSs are subject to numerous constraints (limits on pressures, actuators) that need to be satisfied using appropriate algorithms. The strategy is to replace all the PID loops controlling the WCS with an optimally designed model-based multivariable loop. This new strategy leads to high stability and fast disturbance rejection such as those induced by a turbine or a compressor stop, a key-aspect in the case of large scale cryogenic refrigeration. The proposed control scheme can be used to achieve precise control of pressures in normal operation or to avoid reaching stopping criteria (such as excessive pressures) under high disturbances (such as a pulsed heat load expected to take place in future fusion reactors, expected in the cryogenic cooling systems of the International Thermonuclear Experimental Reactor ITER or the Japan Torus-60 Super Advanced fusion experiment JT-60SA). The paper details the simulator used to validate this new control scheme and the associated simulation results on the SBTs WCS. This work is partially supported through the French National Research Agency (ANR), task agreement ANR-13-SEED-0005.
Cascade generalized predictive control strategy for boiler drum level.
Xu, Min; Li, Shaoyuan; Cai, Wenjian
2005-07-01
This paper proposes a cascade model predictive control scheme for boiler drum level control. By employing generalized predictive control structures for both inner and outer loops, measured and unmeasured disturbances can be effectively rejected, and drum level at constant load is maintained. In addition, nonminimum phase characteristic and system constraints in both loops can be handled effectively by generalized predictive control algorithms. Simulation results are provided to show that cascade generalized predictive control results in better performance than that of well tuned cascade proportional integral differential controllers. The algorithm has also been implemented to control a 75-MW boiler plant, and the results show an improvement over conventional control schemes.
Nonconvex model predictive control for commercial refrigeration
NASA Astrophysics Data System (ADS)
Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John
2013-08-01
We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.
Chen, Qihong; Long, Rong; Quan, Shuhai; Zhang, Liyan
2014-01-01
This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell.
Chen, Qihong; Long, Rong; Quan, Shuhai
2014-01-01
This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell. PMID:24707206
Source-Constrained Recall: Front-End and Back-End Control of Retrieval Quality
ERIC Educational Resources Information Center
Halamish, Vered; Goldsmith, Morris; Jacoby, Larry L.
2012-01-01
Research on the strategic regulation of memory accuracy has focused primarily on monitoring and control processes used to edit out incorrect information after it is retrieved (back-end control). Recent studies, however, suggest that rememberers also enhance accuracy by preventing the retrieval of incorrect information in the first place (front-end…
NASA Astrophysics Data System (ADS)
Jaffery, Mujtaba H.; Shead, Leo; Forshaw, Jason L.; Lappas, Vaios J.
2013-12-01
A new linear model predictive control (MPC) algorithm in a state-space framework is presented based on the fusion of two past MPC control laws: steady-state optimal MPC (SSOMPC) and Laguerre optimal MPC (LOMPC). The new controller, SSLOMPC, is demonstrated to have improved feasibility, tracking performance and computation time than its predecessors. This is verified in both simulation and practical experimentation on a quadrotor unmanned air vehicle in an indoor motion-capture testbed. The performance of the control law is experimentally compared with proportional-integral-derivative (PID) and linear quadratic regulator (LQR) controllers in an unconstrained square manoeuvre. The use of soft control output and hard control input constraints is also examined in single and dual constrained manoeuvres.
Predictive Control of Networked Multiagent Systems via Cloud Computing.
Liu, Guo-Ping
2017-01-18
This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.
NASA Astrophysics Data System (ADS)
Moorcroft, P. R.; Zhang, K.; Ali, A. A.; Scott, D.
2015-12-01
In both natural and managed ecosystems the fluxes of carbon into and out of the ecosystem are strongly connected to the dynamics of soil moisture. In this study, we examine how remote-sensing derived estimates of root zone soil moisture (RZSM) available from the AirMOSS P-band radar remote sensing instrument can be used to constrain terrestrial biosphere model predictions of carbon, water and energy fluxes on timescales ranging from hours to decades. Results from ecosystems in the continental US, including an eastern temperate forest, a mid-western grassland, a Californian oak-savannah, and a western conifer forest, indicate that RZSM measurements can provide an important data-constraint on terrestrial biosphere model predictions of how plant photosynthesis and ecosystem respiration respond to changes in soil moisture availability. In doing so, they pave the way for improved estimates of key model parameters and for reducing uncertainty in regional and continental carbon budgets.
A method to predict circulation control noise
NASA Astrophysics Data System (ADS)
Reger, Robert W.
Underwater vehicles suffer from reduced maneuverability with conventional lifting append-\\ ages due to the low velocity of operation. Circulation control offers a method to increase maneuverability independent of vehicle speed. However, with circulation control comes additional noise sources, which are not well understood. To better understand these noise sources, a modal-based prediction method is developed, potentially offering a quantitative connection between flow structures and far-field noise. This method involves estimation of the velocity field, surface pressure field, and far-field noise, using only non-time-resolved velocity fields and time-resolved probe measurements. Proper orthogonal decomposition, linear stochastic estimation and Kalman smoothing are employed to estimate time-resolved velocity fields. Poisson's equation is used to calculate time-resolved pressure fields from velocity. Curle's analogy is then used to propagate the surface pressure forces to the far field. This method is developed on a direct numerical simulation of a two-dimensional cylinder at a low Reynolds number (150). Since each of the fields to be estimated are also known from the simulation, a means of obtaining the error from using the methodology is provided. The velocity estimation and the simulated velocity match well when the simulated additive measurement noise is low. The pressure field suffers due to a small domain size; however, the surface pressures estimates fare much better. The far-field estimation contains similar frequency content with reduced magnitudes, attributed to the exclusion of the viscous forces in Curle's analogy. In the absence of added noise, the estimation procedure performs quite nicely for this model problem. The method is tested experimentally on a 650,000 chord-Reynolds-number flow over a 2-D, 20% thick, elliptic circulation control airfoil. Slot jet momentum coefficients of 0 and 0.10 are investigated. Particle image velocimetry, unsteady
Predicting Loss-of-Control Boundaries Toward a Piloting Aid
NASA Technical Reports Server (NTRS)
Barlow, Jonathan; Stepanyan, Vahram; Krishnakumar, Kalmanje
2012-01-01
This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.
Blashfield, Roger K; Fuller, A Kenneth
2016-06-01
Twenty years ago, slightly after the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition was published, we predicted the characteristics of the future Diagnostic and Statistical Manual of Mental Disorders (fifth edition) (). Included in our predictions were how many diagnoses it would contain, the physical size of the Diagnostic and Statistical Manual of Mental Disorders (fifth edition), who its leader would be, how many professionals would be involved in creating it, the revenue generated, and the color of its cover. This article reports on the accuracy of our predictions. Our largest prediction error concerned financial revenue. The earnings growth of the DSM's has been remarkable. Drug company investments, insurance benefits, the financial need of the American Psychiatric Association, and the research grant process are factors that have stimulated the growth of the DSM's. Restoring order and simplicity to the classification of mental disorders will not be a trivial task.
Experimental results of a predictive neural network HVAC controller
Jeannette, E.; Assawamartbunlue, K.; Kreider, J.F.; Curtiss, P.S.
1998-12-31
Proportional, integral, and derivative (PID) control is widely used in many HVAC control processes and requires constant attention for optimal control. Artificial neural networks offer the potential for improved control of processes through predictive techniques. This paper introduces and shows experimental results of a predictive neural network (PNN) controller applied to an unstable hot water system in an air-handling unit. Actual laboratory testing of the PNN and PID controllers show favorable results for the PNN controller.
NASA Astrophysics Data System (ADS)
Hormuth, David A., II; Weis, Jared A.; Barnes, Stephanie L.; Miga, Michael I.; Rericha, Erin C.; Quaranta, Vito; Yankeelov, Thomas E.
2015-07-01
Reaction-diffusion models have been widely used to model glioma growth. However, it has not been shown how accurately this model can predict future tumor status using model parameters (i.e., tumor cell diffusion and proliferation) estimated from quantitative in vivo imaging data. To this end, we used in silico studies to develop the methods needed to accurately estimate tumor specific reaction-diffusion model parameters, and then tested the accuracy with which these parameters can predict future growth. The analogous study was then performed in a murine model of glioma growth. The parameter estimation approach was tested using an in silico tumor ‘grown’ for ten days as dictated by the reaction-diffusion equation. Parameters were estimated from early time points and used to predict subsequent growth. Prediction accuracy was assessed at global (total volume and Dice value) and local (concordance correlation coefficient, CCC) levels. Guided by the in silico study, rats (n = 9) with C6 gliomas, imaged with diffusion weighted magnetic resonance imaging, were used to evaluate the model’s accuracy for predicting in vivo tumor growth. The in silico study resulted in low global (tumor volume error <8.8%, Dice >0.92) and local (CCC values >0.80) level errors for predictions up to six days into the future. The in vivo study showed higher global (tumor volume error >11.7%, Dice <0.81) and higher local (CCC <0.33) level errors over the same time period. The in silico study shows that model parameters can be accurately estimated and used to accurately predict future tumor growth at both the global and local scale. However, the poor predictive accuracy in the experimental study suggests the reaction-diffusion equation is an incomplete description of in vivo C6 glioma biology and may require further modeling of intra-tumor interactions including segmentation of (for example) proliferative and necrotic regions.
Niu, Ben; Liu, Yanjun; Zong, Guangdeng; Han, Zhaoyu; Fu, Jun
2017-01-16
In this paper, a new adaptive approximation-based tracking controller design approach is developed for a class of uncertain nonlinear switched lower-triangular systems with an output constraint using neural networks (NNs). By introducing a novel barrier Lyapunov function (BLF), the constrained switched system is first transformed into a new system without any constraint, which means the control objectives of the both systems are equivalent. Then command filter technique is applied to solve the so-called "explosion of complexity" problem in traditional backstepping procedure, and radial basis function NNs are directly employed to model the unknown nonlinear functions. The designed controller ensures that all the closed-loop variables are ultimately boundedness, while the output limit is not transgressed and the output tracking error can be reduced arbitrarily small. Furthermore, the use of an asymmetric BLF is also explored to handle the case of asymmetric output constraint as a generalization result. Finally, the control performance of the presented control schemes is illustrated via two examples.
Adaptive robust motion/force control of holonomic-constrained nonholonomic mobile manipulators.
Li, Zhijun; Ge, Shuzhi Sam; Ming, Aiguo
2007-06-01
In this paper, adaptive robust force/motion control strategies are presented for mobile manipulators under both holonomic and nonholonomic constraints in the presence of uncertainties and disturbances. The proposed control is robust not only to parameter uncertainties such as mass variations but also to external ones such as disturbances. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The proposed control strategies guarantee that the system motion converges to the desired manifold with prescribed performance and the bounded constraint force. Simulation results validate that the motion of the system converges to the desired trajectory, and the constraint force converges to the desired force.
Constrained model based control for minimum-time start of hydraulic turbines
NASA Astrophysics Data System (ADS)
Mesnage, Hugo; Alamir, Mazen; Perrissin-Fabert, Nicolas; Alloin, Quentin
2016-11-01
This paper introduces a simplified model of hydraulic turbines including the hydraulic nonlinear hill-chart and a first order model of the penstock. Based on the resulting reduced model, a graphical representation of the vector fields of the resulting controlled system is obtained under band unlimited actuator. This ideal 2D-graphical representation enables an exact evaluation of the lower bound on the minimum achievable start-time as well as the time structure of the control profile. The consequences of the use of a band-limited actuator is also analyzed enabling a close estimation of the lower bound on the start time in practical situations.
A non-linear model predictive controller with obstacle avoidance for a space robot
NASA Astrophysics Data System (ADS)
Wang, Mingming; Luo, Jianjun; Walter, Ulrich
2016-04-01
This study investigates the use of the non-linear model predictive control (NMPC) strategy for a kinematically redundant space robot to approach an un-cooperative target in complex space environment. Collision avoidance, traditionally treated as a high level planning problem, can be effectively translated into control constraints as part of the NMPC. The objective of this paper is to evaluate the performance of the predictive controller in a constrained workspace and to investigate the feasibility of imposing additional constraints into the NMPC. In this paper, we reformulated the issue of the space robot motion control by using NMPC with predefined objectives under input, output and obstacle constraints over a receding horizon. An on-line quadratic programming (QP) procedure is employed to obtain the constrained optimal control decisions in real-time. This study has been implemented for a 7 degree-of-freedom (DOF) kinematically redundant manipulator mounted on a 6 DOF free-floating spacecraft via simulation studies. Real-time trajectory tracking and collision avoidance particularly demonstrate the effectiveness and potential of the proposed NMPC strategy for the space robot.
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.
Wu, Jian; Singla, Mithun; Olmi, Claudio; Shieh, Leang S; Song, Gangbing
2010-07-01
In this paper, a scalar sign function-based digital design methodology is developed for modeling and control of a class of analog nonlinear systems that are restricted by the absolute value function constraints. As is found to be not uncommon, many real systems are subject to the constraints which are described by the non-smooth functions such as absolute value function. The non-smooth and nonlinear nature poses significant challenges to the modeling and control work. To overcome these difficulties, a novel idea proposed in this work is to use a scalar sign function approach to effectively transform the original nonlinear and non-smooth model into a smooth nonlinear rational function model. Upon the resulting smooth model, a systematic digital controller design procedure is established, in which an optimal linearization method, LQR design and digital implementation through an advanced digital redesign technique are sequentially applied. The example of tracking control of a piezoelectric actuator system is utilized throughout the paper for illustrating the proposed methodology.
A policy iteration approach to online optimal control of continuous-time constrained-input systems.
Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L
2013-09-01
This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach.
An Anatomically Constrained, Stochastic Model of Eye Movement Control in Reading
ERIC Educational Resources Information Center
McDonald, Scott A.; Carpenter, R. H. S.; Shillcock, Richard C.
2005-01-01
This article presents SERIF, a new model of eye movement control in reading that integrates an established stochastic model of saccade latencies (LATER; R. H. S. Carpenter, 1981) with a fundamental anatomical constraint on reading: the vertically split fovea and the initial projection of information in either visual field to the contralateral…
Analytical optimal controls for the state constrained addition and removal of cryoprotective agents
Chicone, Carmen C.; Critser, John K.
2014-01-01
Cryobiology is a field with enormous scientific, financial and even cultural impact. Successful cryopreservation of cells and tissues depends on the equilibration of these materials with high concentrations of permeating chemicals (CPAs) such as glycerol or 1,2 propylene glycol. Because cells and tissues are exposed to highly anisosmotic conditions, the resulting gradients cause large volume fluctuations that have been shown to damage cells and tissues. On the other hand, there is evidence that toxicity to these high levels of chemicals is time dependent, and therefore it is ideal to minimize exposure time as well. Because solute and solvent flux is governed by a system of ordinary differential equations, CPA addition and removal from cells is an ideal context for the application of optimal control theory. Recently, we presented a mathematical synthesis of the optimal controls for the ODE system commonly used in cryobiology in the absence of state constraints and showed that controls defined by this synthesis were optimal. Here we define the appropriate model, analytically extend the previous theory to one encompassing state constraints, and as an example apply this to the critical and clinically important cell type of human oocytes, where current methodologies are either difficult to implement or have very limited success rates. We show that an enormous increase in equilibration efficiency can be achieved under the new protocols when compared to classic protocols, potentially allowing a greatly increased survival rate for human oocytes, and pointing to a direction for the cryopreservation of many other cell types. PMID:22527943
Finite dimensional approximation of a class of constrained nonlinear optimal control problems
NASA Technical Reports Server (NTRS)
Gunzburger, Max D.; Hou, L. S.
1994-01-01
An abstract framework for the analysis and approximation of a class of nonlinear optimal control and optimization problems is constructed. Nonlinearities occur in both the objective functional and in the constraints. The framework includes an abstract nonlinear optimization problem posed on infinite dimensional spaces, and approximate problem posed on finite dimensional spaces, together with a number of hypotheses concerning the two problems. The framework is used to show that optimal solutions exist, to show that Lagrange multipliers may be used to enforce the constraints, to derive an optimality system from which optimal states and controls may be deduced, and to derive existence results and error estimates for solutions of the approximate problem. The abstract framework and the results derived from that framework are then applied to three concrete control or optimization problems and their approximation by finite element methods. The first involves the von Karman plate equations of nonlinear elasticity, the second, the Ginzburg-Landau equations of superconductivity, and the third, the Navier-Stokes equations for incompressible, viscous flows.
Sequential Prediction for Information Fusion and Control
2013-10-14
paradigms , and external feedback mechanisms. Online prediction and targeted collection of information is an emerging paradigm at the intersection of...unknown, environmental dynamics, potentially stemming from an adversary who reacts to sensing actions, active sensing paradigms , and external feedback mech...anisms. Online prediction and targeted collection of information is an emerging paradigm at the inter- section of optimization, machine learning and
NASA Astrophysics Data System (ADS)
Glasgow, M. E.; Schmandt, B.; Gaeuman, D.
2015-12-01
To evaluate the utility of riverside seismic monitoring for constraining temporal and spatial variations in coarse bedload transport in gravel-bed rivers we collected seismic data during a dam-controlled flood of the Trinity River in northern California in May 2015. This field area was chosen because the Trinity River Restoration Project conducts extensive monitoring of water and sediment transport, and riverbed morphology to guide management of the river with the goal of improving salmon habitat. Four three component broadband seismometers were collocated with water discharge and bedload physical sampling sites along a ~30 km reach of the Trinity River downstream of the Lewiston Dam. Arrays with 10-80 cable-free vertical component geophones were also deployed at each of the four sites in order to constrain spatial variability and amplitude decay of seismic signals emanating from the river. Nominal inter-station spacing within the geophone arrays was ~30 m. The largest geophone array consisted of 83 nodes along a 700 m reach of the Trinity River with a gravel augmentation site at its upstream end. Initial analyses of the seismic data show that ground velocity power from averaged from ~7 - 90 Hz is correlated with discharge at all sites. The array at the gravel injection site shows greater high frequency (>30 Hz) power at the upstream end where gravel was injected during the release compared to ~300 m downstream, consistent with bedload transport providing a significant source of seismic energy in addition to water discharge. Declining seismic power during a ~3 day plateau at peak discharge when physical sampler data shows decreasing bedload flux provides a further indication that the seismic data are sensitive to bedload transport. We will use the array data to back-project the seismic signals in multiple frequency bands into the channel to create maps of the time-varying spatial intensity of seismic energy production. We hypothesize that the greatest seismic
Multiple model predictive control for a hybrid proton exchange membrane fuel cell system
NASA Astrophysics Data System (ADS)
Chen, Qihong; Gao, Lijun; Dougal, Roger A.; Quan, Shuhai
This paper presents a hierarchical predictive control strategy to optimize both power utilization and oxygen control simultaneously for a hybrid proton exchange membrane fuel cell/ultracapacitor system. The control employs fuzzy clustering-based modeling, constrained model predictive control, and adaptive switching among multiple models. The strategy has three major advantages. First, by employing multiple piecewise linear models of the nonlinear system, we are able to use linear models in the model predictive control, which significantly simplifies implementation and can handle multiple constraints. Second, the control algorithm is able to perform global optimization for both the power allocation and oxygen control. As a result, we can achieve the optimization from the entire system viewpoint, and a good tradeoff between transient performance of the fuel cell and the ultracapacitor can be obtained. Third, models of the hybrid system are identified using real-world data from the hybrid fuel cell system, and models are updated online. Therefore, the modeling mismatch is minimized and high control accuracy is achieved. Study results demonstrate that the control strategy is able to appropriately split power between fuel cell and ultracapacitor, avoid oxygen starvation, and so enhance the transient performance and extend the operating life of the hybrid system.
Receding horizon output feedback control for constrained uncertain systems using periodic invariance
NASA Astrophysics Data System (ADS)
Lim, Jae Sik; Son, Sung Yong; Lee, Young Il
2010-06-01
In this article, we consider a receding horizon output feedback control (RHOC) method for linear discrete-time systems with polytopic model uncertainties and input constraints. First, we derive a set of estimator gains and then we obtain, on the basis of the periodic invariance, a series of state feedback gains stabilising the augmented output feedback system with these estimator gains. These procedures are formulated as linear matrix inequalities. An RHOC strategy is proposed based on these state feedback and state estimator gains in conjunction with their corresponding periodically invariant sets. The proposed RHOC strategy enhances the performance in comparison with the case in which static periodic gains are used, and increases the size of the stabilisable region by introducing a degree of freedom to steer the augmented state into periodically invariant sets.
Zhang, Y M; Huang, G; Lu, H W; He, Li
2015-08-15
A key issue facing integrated water resources management and water pollution control is to address the vague parametric information. A full credibility-based chance-constrained programming (FCCP) method is thus developed by introducing the new concept of credibility into the modeling framework. FCCP can deal with fuzzy parameters appearing concurrently in the objective and both sides of the constraints of the model, but also provide a credibility level indicating how much confidence one can believe the optimal modeling solutions. The method is applied to Heshui River watershed in the south-central China for demonstration. Results from the case study showed that groundwater would make up for the water shortage in terms of the shrinking surface water and rising water demand, and the optimized total pumpage of groundwater from both alluvial and karst aquifers would exceed 90% of its maximum allowable levels when credibility level is higher than or equal to 0.9. It is also indicated that an increase in credibility level would induce a reduction in cost for surface water acquisition, a rise in cost from groundwater withdrawal, and negligible variation in cost for water pollution control.
Valtonen, Anu; Ayres, Matthew P; Roininen, Heikki; Pöyry, Juha; Leinonen, Reima
2011-01-01
Ecological systems have naturally high interannual variance in phenology. Component species have presumably evolved to maintain appropriate phenologies under historical climates, but cases of inappropriate phenology can be expected with climate change. Understanding controls on phenology permits predictions of ecological responses to climate change. We studied phenological control systems in Lepidoptera by analyzing flight times recorded at a network of sites in Finland. We evaluated the strength and form of controls from temperature and photoperiod, and tested for geographic variation within species. Temperature controls on phenology were evident in 51% of 112 study species and for a third of those thermal controls appear to be modified by photoperiodic cues. For 24% of the total, photoperiod by itself emerged as the most likely control system. Species with thermal control alone should be most immediately responsive in phenology to climate warming, but variably so depending upon the minimum temperature at which appreciable development occurs and the thermal responsiveness of development rate. Photoperiodic modification of thermal controls constrains phenotypic responses in phenologies to climate change, but can evolve to permit local adaptation. Our results suggest that climate change will alter the phenological structure of the Finnish Lepidoptera community in ways that are predictable with knowledge of the proximate physiological controls. Understanding how phenological controls in Lepidoptera compare to that of their host plants and enemies could permit general inferences regarding climatic effects on mid- to high-latitude ecosystems.
Voltage control in pulsed system by predict-ahead control
Payne, A.N.; Watson, J.A.; Sampayan, S.E.
1994-09-13
A method and apparatus for predict-ahead pulse-to-pulse voltage control in a pulsed power supply system is disclosed. A DC power supply network is coupled to a resonant charging network via a first switch. The resonant charging network is coupled at a node to a storage capacitor. An output load is coupled to the storage capacitor via a second switch. A de-Q-ing network is coupled to the resonant charging network via a third switch. The trigger for the third switch is a derived function of the initial voltage of the power supply network, the initial voltage of the storage capacitor, and the present voltage of the storage capacitor. A first trigger closes the first switch and charges the capacitor. The third trigger is asserted according to the derived function to close the third switch. When the third switch is closed, the first switch opens and voltage on the node is regulated. The second trigger may be thereafter asserted to discharge the capacitor into the output load. 4 figs.
Voltage control in pulsed system by predict-ahead control
Payne, Anthony N.; Watson, James A.; Sampayan, Stephen E.
1994-01-01
A method and apparatus for predict-ahead pulse-to-pulse voltage control in a pulsed power supply system is disclosed. A DC power supply network is coupled to a resonant charging network via a first switch. The resonant charging network is coupled at a node to a storage capacitor. An output load is coupled to the storage capacitor via a second switch. A de-Q-ing network is coupled to the resonant charging network via a third switch. The trigger for the third switch is a derived function of the initial voltage of the power supply network, the initial voltage of the storage capacitor, and the present voltage of the storage capacitor. A first trigger closes the first switch and charges the capacitor. The third trigger is asserted according to the derived function to close the third switch. When the third switch is closed, the first switch opens and voltage on the node is regulated. The second trigger may be thereafter asserted to discharge the capacitor into the output load.
NASA Astrophysics Data System (ADS)
Chang, Wen-Jer; Meng, Yu-Teh; Tsai, Kuo-Hui
2012-12-01
In this article, Takagi-Sugeno (T-S) fuzzy control theory is proposed as a key tool to design an effective active queue management (AQM) router for the transmission control protocol (TCP) networks. The probability control of packet marking in the TCP networks is characterised by an input constrained control problem in this article. By modelling the TCP network into a time-delay affine T-S fuzzy model, an input constrained fuzzy control methodology is developed in this article to serve the AQM router design. The proposed fuzzy control approach, which is developed based on the parallel distributed compensation technique, can provide smaller probability of dropping packets than previous AQM design schemes. Lastly, a numerical simulation is provided to illustrate the usefulness and effectiveness of the proposed design approach.
Networked Robust Predictive Control Systems Design with Packet Loss
NASA Astrophysics Data System (ADS)
Nguyen, Quang T.; Veselý, Vojtech; Kozáková, Alena; Pakshin, Pavel
2014-01-01
The paper addresses problem of designing a robust output feedback model predictive control for uncertain linear systems over networks with packet-loss. The packet-loss process is arbitrary and bounded by the control horizon of model predictive control. Networked predictive control systems with packet loss are modeled as switched linear systems. This enables us to apply the theory of switched systems to establish the stability condition. The stabilizing controller design is based on sufficient robust stability conditions formulated as a solution of bilinear matrix inequality. Finally, a benchmark numerical example-double integrator is given to illustrate the effectiveness of the proposed method.
Consensus and Stability Analysis of Networked Multiagent Predictive Control Systems.
Liu, Guo-Ping
2016-03-17
This paper is concerned with the consensus and stability problem of multiagent control systems via networks with communication delays and data loss. A networked multiagent predictive control scheme is proposed to achieve output consensus and also compensate for the communication delays and data loss actively. The necessary and sufficient conditions of achieving both consensus and stability of the closed-loop networked multiagent control systems are derived. An important result that is obtained is that the consensus and stability of closed-loop networked multiagent predictive control systems are not related to the communication delays and data loss. An example illustrates the performance of the networked multiagent predictive control scheme.
Comparison of predictive control methods for high consumption industrial furnace.
Stojanovski, Goran; Stankovski, Mile
2013-01-01
We describe several predictive control approaches for high consumption industrial furnace control. These furnaces are major consumers in production industries, and reducing their fuel consumption and optimizing the quality of the products is one of the most important engineer tasks. In order to demonstrate the benefits from implementation of the advanced predictive control algorithms, we have compared several major criteria for furnace control. On the basis of the analysis, some important conclusions have been drawn.
NASA Technical Reports Server (NTRS)
Probst, D.; Jensen, L.
1991-01-01
Delay-insensitive VLSI systems have a certain appeal on the ground due to difficulties with clocks; they are even more attractive in space. We answer the question, is it possible to control state explosion arising from various sources during automatic verification (model checking) of delay-insensitive systems? State explosion due to concurrency is handled by introducing a partial-order representation for systems, and defining system correctness as a simple relation between two partial orders on the same set of system events (a graph problem). State explosion due to nondeterminism (chiefly arbitration) is handled when the system to be verified has a clean, finite recurrence structure. Backwards branching is a further optimization. The heart of this approach is the ability, during model checking, to discover a compact finite presentation of the verified system without prior composition of system components. The fully-implemented POM verification system has polynomial space and time performance on traditional asynchronous-circuit benchmarks that are exponential in space and time for other verification systems. We also sketch the generalization of this approach to handle delay-constrained VLSI systems.
Model Predictive Control for Nonlinear Parabolic Partial Differential Equations
NASA Astrophysics Data System (ADS)
Hashimoto, Tomoaki; Yoshioka, Yusuke; Ohtsuka, Toshiyuki
In this study, the optimal control problem of nonlinear parabolic partial differential equations (PDEs) is investigated. Optimal control of nonlinear PDEs is an open problem with applications that include fluid, thermal, biological, and chemically-reacting systems. Model predictive control with a fast numerical solution method has been well established to solve the optimal control problem of nonlinear systems described by ordinary differential equations. In this study, we develop a design method of the model predictive control for nonlinear systems described by parabolic PDEs. Our approach is a direct infinite dimensional extension of the model predictive control method for finite-dimensional systems. The objective of this paper is to develop an efficient algorithm for numerically solving the model predictive control problem of nonlinear parabolic PDEs. The effectiveness of the proposed method is verified by numerical simulations.
Model-Based Predictive Control of Turbulent Channel Flow
NASA Astrophysics Data System (ADS)
Kellogg, Steven M.; Collis, S. Scott
1999-11-01
In recent simulations of optimal turbulence control, the time horizon over which the control is determined matches the time horizon over which the flow is advanced. A popular workhorse of the controls community, Model-Based Predictive Control (MBPC), suggests using longer predictive horizons than advancement windows. Including additional time information in the optimization may generate improved controls. When the advancement horizon is smaller than the predictive horizon, part of the optimization and resulting control are discarded. Although this inherent inefficiency may be justified by improved control predictions, it has hampered prior investigations of MBPC for turbulent flow due to the expense associated with optimal control based on Direct Numerical Simulation. The current approach overcomes this by using our optimal control formulation based on Large Eddy Simulation. This presentation summarizes the results of optimal control simulations for turbulent channel flow using various ratios of advancement and predictive horizons. These results provide clues as to the roles of foresight, control history, cost functional, and turbulence structures for optimal control of wall-bounded turbulence.
Prospects for earthquake prediction and control
Healy, J.H.; Lee, W.H.K.; Pakiser, L.C.; Raleigh, C.B.; Wood, M.D.
1972-01-01
The San Andreas fault is viewed, according to the concepts of seafloor spreading and plate tectonics, as a transform fault that separates the Pacific and North American plates and along which relative movements of 2 to 6 cm/year have been taking place. The resulting strain can be released by creep, by earthquakes of moderate size, or (as near San Francisco and Los Angeles) by great earthquakes. Microearthquakes, as mapped by a dense seismograph network in central California, generally coincide with zones of the San Andreas fault system that are creeping. Microearthquakes are few and scattered in zones where elastic energy is being stored. Changes in the rate of strain, as recorded by tiltmeter arrays, have been observed before several earthquakes of about magnitude 4. Changes in fluid pressure may control timing of seismic activity and make it possible to control natural earthquakes by controlling variations in fluid pressure in fault zones. An experiment in earthquake control is underway at the Rangely oil field in Colorado, where the rates of fluid injection and withdrawal in experimental wells are being controlled. ?? 1972.
Luo, Shaohua; Hou, Zhiwei; Chen, Zhong
2015-12-15
In this paper, chaos control is proposed for the output- constrained system with uncertain control gain and time delay and is applied to the brushless DC motor. Using the dynamic surface technology, the controller overcomes the repetitive differentiation of backstepping and boundedness hypothesis of pre-determined control gain by incorporating radial basis function neural network and adaptive technology. The tangent barrier Lyapunov function is employed for time-delay chaotic system to prevent constraint violation. It is proved that the proposed control approach can guarantee asymptotically stable in the sense of uniformly ultimate boundedness without constraint violation. Finally, the effectiveness of the proposed approach is demonstrated on the brushless DC motor example.
NASA Astrophysics Data System (ADS)
Luo, Shaohua; Hou, Zhiwei; Chen, Zhong
2015-12-01
In this paper, chaos control is proposed for the output- constrained system with uncertain control gain and time delay and is applied to the brushless DC motor. Using the dynamic surface technology, the controller overcomes the repetitive differentiation of backstepping and boundedness hypothesis of pre-determined control gain by incorporating radial basis function neural network and adaptive technology. The tangent barrier Lyapunov function is employed for time-delay chaotic system to prevent constraint violation. It is proved that the proposed control approach can guarantee asymptotically stable in the sense of uniformly ultimate boundedness without constraint violation. Finally, the effectiveness of the proposed approach is demonstrated on the brushless DC motor example.
Launch ascent guidance by discrete multi-model predictive control
NASA Astrophysics Data System (ADS)
Vachon, Alexandre; Desbiens, André; Gagnon, Eric; Bérard, Caroline
2014-02-01
This paper studies the application of discrete multi-model predictive control as a trajectory tracking guidance law for a space launcher. Two different algorithms are developed, each one based on a different representation of launcher translation dynamics. These representations are based on an interpolation of the linear approximation of nonlinear pseudo-five degrees of freedom equations of translation around an elliptical Earth. The interpolation gives a linear-time-varying representation and a linear-fractional representation. They are used as the predictive model of multi-model predictive controllers. The controlled variables are the orbital parameters, and constraints on a terminal region for the minimal accepted precision are also included. Use of orbital parameters as the controlled variables allows for a partial definition of the trajectory. Constraints can also be included in multi-model predictive control to reduce the number of unknowns of the problem by defining input shaping constraints. The guidance algorithms are tested in nominal conditions and off-nominal conditions with uncertainties on the thrust. The results are compared to those of a similar formulation with a nonlinear model predictive controller and to a guidance method based on the resolution of a simplified version of the two-point boundary value problem. In nominal conditions, the model predictive controllers are more precise and produce a more optimal trajectory but are longer to compute than the two-point boundary solution. Moreover, in presence of uncertainties, developed algorithms exhibit poor robustness properties. The multi-model predictive control algorithms do not reach the desired orbit while the nonlinear model predictive control algorithm still converges but produces larger maneuvers than the other method.
Model predictive control of P-time event graphs
NASA Astrophysics Data System (ADS)
Hamri, H.; Kara, R.; Amari, S.
2016-12-01
This paper deals with model predictive control of discrete event systems modelled by P-time event graphs. First, the model is obtained by using the dater evolution model written in the standard algebra. Then, for the control law, we used the finite-horizon model predictive control. For the closed-loop control, we used the infinite-horizon model predictive control (IH-MPC). The latter is an approach that calculates static feedback gains which allows the stability of the closed-loop system while respecting the constraints on the control vector. The problem of IH-MPC is formulated as a linear convex programming subject to a linear matrix inequality problem. Finally, the proposed methodology is applied to a transportation system.
Rate-Based Model Predictive Control of Turbofan Engine Clearance
NASA Technical Reports Server (NTRS)
DeCastro, Jonathan A.
2006-01-01
An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.
Pilots Rate Augmented Generalized Predictive Control for Reconfiguration
NASA Technical Reports Server (NTRS)
Soloway, Don; Haley, Pam
2004-01-01
The objective of this paper is to report the results from the research being conducted in reconfigurable fight controls at NASA Ames. A study was conducted with three NASA Dryden test pilots to evaluate two approaches of reconfiguring an aircraft's control system when failures occur in the control surfaces and engine. NASA Ames is investigating both a Neural Generalized Predictive Control scheme and a Neural Network based Dynamic Inverse controller. This paper highlights the Predictive Control scheme where a simple augmentation to reduce zero steady-state error led to the neural network predictor model becoming redundant for the task. Instead of using a neural network predictor model, a nominal single point linear model was used and then augmented with an error corrector. This paper shows that the Generalized Predictive Controller and the Dynamic Inverse Neural Network controller perform equally well at reconfiguration, but with less rate requirements from the actuators. Also presented are the pilot ratings for each controller for various failure scenarios and two samples of the required control actuation during reconfiguration. Finally, the paper concludes by stepping through the Generalized Predictive Control's reconfiguration process for an elevator failure.
Iterated non-linear model predictive control based on tubes and contractive constraints.
Murillo, M; Sánchez, G; Giovanini, L
2016-05-01
This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle.
Power-constrained supercomputing
NASA Astrophysics Data System (ADS)
Bailey, Peter E.
. Adaptive power balancing efficiently predicts where critical paths are likely to occur and distributes power to those paths. Greater power, in turn, allows increased thread concurrency levels, CPU frequency/voltage, or both. We describe these techniques in detail and show that, compared to the state-of-the-art technique of using statically predetermined, per-node power caps, Conductor leads to a best-case performance improvement of up to 30%, and an average improvement of 19.1%. At the node level, an accurate power/performance model will aid in selecting the right configuration from a large set of available configurations. We present a novel approach to generate such a model offline using kernel clustering and multivariate linear regression. Our model requires only two iterations to select a configuration, which provides a significant advantage over exhaustive search-based strategies. We apply our model to predict power and performance for different applications using arbitrary configurations, and show that our model, when used with hardware frequency-limiting in a runtime system, selects configurations with significantly higher performance at a given power limit than those chosen by frequency-limiting alone. When applied to a set of 36 computational kernels from a range of applications, our model accurately predicts power and performance; our runtime system based on the model maintains 91% of optimal performance while meeting power constraints 88% of the time. When the runtime system violates a power constraint, it exceeds the constraint by only 6% in the average case, while simultaneously achieving 54% more performance than an oracle. Through the combination of the above contributions, we hope to provide guidance and inspiration to research practitioners working on runtime systems for power-constrained environments. We also hope this dissertation will draw attention to the need for software and runtime-controlled power management under power constraints at various levels
Switched linear model predictive controllers for periodic exogenous signals
NASA Astrophysics Data System (ADS)
Wang, Liuping; Gawthrop, Peter; Owens, David. H.; Rogers, Eric
2010-04-01
This article develops switched linear controllers for periodic exogenous signals using the framework of a continuous-time model predictive control. In this framework, the control signal is generated by an algorithm that uses receding horizon control principle with an on-line optimisation scheme that permits inclusion of operational constraints. Unlike traditional repetitive controllers, applying this method in the form of switched linear controllers ensures bumpless transfer from one controller to another. Simulation studies are included to demonstrate the efficacy of the design with or without hard constraints.
Model and Predictive Control for a Wind Turbine
NASA Astrophysics Data System (ADS)
Gilev, B.; Slavchev, J.; Penev, D.; Yonchev, A.
2011-12-01
A mathematical model of the system consisting of wind turbine, gear box and asynchronous generator is presented in this work. The model is linearized. Then a controller, which provides a desire mode of frequency stabilization, is developed using the predictive control theory.
Towards feasible and effective predictive wavefront control for adaptive optics
Poyneer, L A; Veran, J
2008-06-04
We have recently proposed Predictive Fourier Control, a computationally efficient and adaptive algorithm for predictive wavefront control that assumes frozen flow turbulence. We summarize refinements to the state-space model that allow operation with arbitrary computational delays and reduce the computational cost of solving for new control. We present initial atmospheric characterization using observations with Gemini North's Altair AO system. These observations, taken over 1 year, indicate that frozen flow is exists, contains substantial power, and is strongly detected 94% of the time.
Optimal Tuning for Disturbance Suppression Mechanism for Model Predictive Control
NASA Astrophysics Data System (ADS)
Tange, Yoshio; Nakazawa, Chikashi
Disturbance suppression is one of most required performances in process control. We recently proposed a new disturbance suppression mechanism applicable for model predictive control in order to enhance disturbance suppression performance for ramp-like disturbances. The proposed method utilized the prediction error of controlled values and generates a disturbance compensation signal by a constant gain feedback. In this paper, we propose an improved version of the disturbance suppression mechanism by applying a low-pass filter and parameter tuning methods by which we can make the mechanism more tolerant to various disturbances such as ramp, step, and other supposable ones. We also show numerical simulation results with an oil distillation tower plant.
Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control
Mehrabi, Naser; Sharif Razavian, Reza; Ghannadi, Borna; McPhee, John
2017-01-01
This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization. PMID:28133449
Predictive neuro-fuzzy controller for multilink robot manipulator
NASA Astrophysics Data System (ADS)
Kaymaz, Emre; Mitra, Sunanda
1995-10-01
A generalized controller based on fuzzy clustering and fuzzy generalized predictive control has been developed for nonlinear systems including multilink robot manipulators. The proposed controller is particularly useful when the dynamics of the nonlinear system to be controlled are difficult to yield exact solutions and the system specification can be obtained in terms of crisp input-output pairs. It inherits the advantages of both fuzzy logic and predictive control. The identification of the nonlinear mapping of the system to be controlled is realized by a three- layer feed-forward neural network model employing the input-output data obtained from the system. The speed of convergence of the neural network is improved by the introduction of a fuzzy logic controlled backpropagation learning algorithm. The neural network model is then used as a simulation tool to generate the input-output data for developing the predictive fuzzy logic controller for the chosen nonlinear system. The use of fuzzy clustering facilitates automatic generation of membership relations of the input-output data. Unlike the linguistic fuzzy logic controller which requires approximate knowledge of the shape and the numbers of the membership functions in the input and output universes of the discourse, this integrated neuro-fuzzy approach allows one to find the fuzzy relations and the membership functions more accurately. Furthermore, it is not necessary to tune the controller. For a two-link robot manipulator, the performance of this predictive fuzzy controller is shown to be superior to that of a conventional controller employing an ARMA model of the system in terms of accuracy and consumption of energy.
Improving active space telescope wavefront control using predictive thermal modeling
NASA Astrophysics Data System (ADS)
Gersh-Range, Jessica; Perrin, Marshall D.
2015-01-01
Active control algorithms for space telescopes are less mature than those for large ground telescopes due to differences in the wavefront control problems. Active wavefront control for space telescopes at L2, such as the James Webb Space Telescope (JWST), requires weighing control costs against the benefits of correcting wavefront perturbations that are a predictable byproduct of the observing schedule, which is known and determined in advance. To improve the control algorithms for these telescopes, we have developed a model that calculates the temperature and wavefront evolution during a hypothetical mission, assuming the dominant wavefront perturbations are due to changes in the spacecraft attitude with respect to the sun. Using this model, we show that the wavefront can be controlled passively by introducing scheduling constraints that limit the allowable attitudes for an observation based on the observation duration and the mean telescope temperature. We also describe the implementation of a predictive controller designed to prevent the wavefront error (WFE) from exceeding a desired threshold. This controller outperforms simpler algorithms even with substantial model error, achieving a lower WFE without requiring significantly more corrections. Consequently, predictive wavefront control based on known spacecraft attitude plans is a promising approach for JWST and other future active space observatories.
Semiconductor CMP Process Control Predicting Degradation Effect of Consumed Materials
NASA Astrophysics Data System (ADS)
Tamaki, Kenji; Kaneko, Shun'ichi
This paper describes a methodology to build a virtual metrology (VM) model for semiconductor chemical mechanical polishing (CMP) process control. The VM model predicts the polishing rate based on equipment-derived data as soon as allowed, and immediately applies the results to advanced process control (APC). The proposed methodology uses Markov chain Monte Carlo (MCMC) methods to build an analytical model with many parameters for individual consumed materials from historical data in small quantities. The mutual interference of two kinds of consumed materials: dresser and pad are modeled in a form of multilevel predictive model. The methodology uses MCMC methods again to identify the multilevel predictive model taking into account the assumed operation of an actual manufacturing line, for instance, using preliminary test result, learning a model parameter online, and being affected by metrology lag as disturbance. The simulation results show the APC with the proposed VM model is low sensitivity to metrology lag and high precision on polishing amount control.
Prediction and control of limit cycling motions in boosting rockets
NASA Astrophysics Data System (ADS)
Newman, Brett
An investigation concerning the prediction and control of observed limit cycling behavior in a boosting rocket is considered. The suspected source of the nonlinear behavior is the presence of Coulomb friction in the nozzle pivot mechanism. A classical sinusoidal describing function analysis is used to accurately recreate and predict the observed oscillatory characteristic. In so doing, insight is offered into the limit cycling mechanism and confidence is gained in the closed-loop system design. Nonlinear simulation results are further used to support and verify the results obtained from describing function theory. Insight into the limit cycling behavior is, in turn, used to adjust control system parameters in order to passively control the oscillatory tendencies. Tradeoffs with the guidance and control system stability/performance are also noted. Finally, active control of the limit cycling behavior, using a novel feedback algorithm to adjust the inherent nozzle sticking-unsticking characteristics, is considered.
Optimized continuous pharmaceutical manufacturing via model-predictive control.
Rehrl, Jakob; Kruisz, Julia; Sacher, Stephan; Khinast, Johannes; Horn, Martin
2016-08-20
This paper demonstrates the application of model-predictive control to a feeding blending unit used in continuous pharmaceutical manufacturing. The goal of this contribution is, on the one hand, to highlight the advantages of the proposed concept compared to conventional PI-controllers, and, on the other hand, to present a step-by-step guide for controller synthesis. The derivation of the required mathematical plant model is given in detail and all the steps required to develop a model-predictive controller are shown. Compared to conventional concepts, the proposed approach allows to conveniently consider constraints (e.g. mass hold-up in the blender) and offers a straightforward, easy to tune controller setup. The concept is implemented in a simulation environment. In order to realize it on a real system, additional aspects (e.g., state estimation, measurement equipment) will have to be investigated.
Model Predictive Control with Integral Action for Current Density Profile Tracking in NSTX-U
NASA Astrophysics Data System (ADS)
Ilhan, Z. O.; Wehner, W. P.; Schuster, E.; Boyer, M. D.
2016-10-01
Active control of the toroidal current density profile may play a critical role in non-inductively sustained long-pulse, high-beta scenarios in a spherical torus (ST) configuration, which is among the missions of the NSTX-U facility. In this work, a previously developed physics-based control-oriented model is embedded in a feedback control scheme based on a model predictive control (MPC) strategy to track a desired current density profile evolution specified indirectly by a desired rotational transform profile. An integrator is embedded into the standard MPC formulation to reject various modeling uncertainties and external disturbances. Neutral beam powers, electron density, and total plasma current are used as actuators. The proposed MPC strategy incorporates various state and actuator constraints directly into the control design process by solving a constrained optimization problem in real-time to determine the optimal actuator requests. The effectiveness of the proposed controller in regulating the current density profile in NSTX-U is demonstrated in closed-loop nonlinear simulations. Supported by the US DOE under DE-AC02-09CH11466.
Nonlinear model predictive control using parameter varying BP-ARX combination model
NASA Astrophysics Data System (ADS)
Yang, J.-F.; Xiao, L.-F.; Qian, J.-X.; Li, H.
2012-03-01
A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.
Predictive Feedback and Feedforward Control for Systems with Unknown Disturbances
NASA Technical Reports Server (NTRS)
Juang, Jer-Nan; Eure, Kenneth W.
1998-01-01
Predictive feedback control has been successfully used in the regulation of plate vibrations when no reference signal is available for feedforward control. However, if a reference signal is available it may be used to enhance regulation by incorporating a feedforward path in the feedback controller. Such a controller is known as a hybrid controller. This paper presents the theory and implementation of the hybrid controller for general linear systems, in particular for structural vibration induced by acoustic noise. The generalized predictive control is extended to include a feedforward path in the multi-input multi-output case and implemented on a single-input single-output test plant to achieve plate vibration regulation. There are cases in acoustic-induce vibration where the disturbance signal is not available to be used by the hybrid controller, but a disturbance model is available. In this case the disturbance model may be used in the feedback controller to enhance performance. In practice, however, neither the disturbance signal nor the disturbance model is available. This paper presents the theory of identifying and incorporating the noise model into the feedback controller. Implementations are performed on a test plant and regulation improvements over the case where no noise model is used are demonstrated.
Nonlinear Model Predictive Control with Constraint Satisfactions for a Quadcopter
NASA Astrophysics Data System (ADS)
Wang, Ye; Ramirez-Jaime, Andres; Xu, Feng; Puig, Vicenç
2017-01-01
This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of full dynamics can be transformed into the inequality constraints. Subsequently, the quadcopter can be controlled by an NMPC controller with the updated constraints generated by constraint satisfactions. Finally, the simulation results applied to a quadcopter simulator are provided to show the effectiveness of the proposed strategy.
Predicted torque equilibrium attitude utilization for Space Station attitude control
NASA Technical Reports Server (NTRS)
Kumar, Renjith R.; Heck, Michael L.; Robertson, Brent P.
1990-01-01
An approximate knowledge of the torque equilibrium attitude (TEA) is shown to improve the performance of a control moment gyroscope (CMG) momentum management/attitude control law for Space Station Freedom. The linearized equations of motion are used in conjunction with a state transformation to obtain a control law which uses full state feedback and the predicted TEA to minimize both attitude excursions and CMG peak and secular momentum. The TEA can be computationally determined either by observing the steady state attitude of a 'controlled' spacecraft using arbitrary initial attitude, or by simulating a fixed attitude spacecraft flying in desired orbit subject to realistic environmental disturbance models.
Neural Generalized Predictive Control: A Newton-Raphson Implementation
NASA Technical Reports Server (NTRS)
Soloway, Donald; Haley, Pamela J.
1997-01-01
An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant's nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm's implementation are also included.
Fuzzy Predictive Control Strategy in the Application of the Industrial Furnace Temperature Control
NASA Astrophysics Data System (ADS)
Dai, Luping; Chen, Xingliang; Chen, Liu; Liu, Xia
Ceramic kiln with large heat capacity, big lag and nonlinear characteristic, this paper proposes a combining fuzzy control and predictive control of the control algorithm, to enhance the tracking and anti-interference ability of the algorithm. The simulation results show, this method compared with the control of PID has the high steady precision and dynamic characteristic.
The predictive protective control of the heat exchanger
NASA Astrophysics Data System (ADS)
Nevriva, Pavel; Filipova, Blanka; Vilimec, Ladislav
2016-06-01
The paper deals with the predictive control applied to flexible cogeneration energy system FES. FES was designed and developed by the VITKOVICE POWER ENGINEERING joint-stock company and represents a new solution of decentralized cogeneration energy sources. In FES, the heating medium is flue gas generated by combustion of a solid fuel. The heated medium is power gas, which is a gas mixture of air and water steam. Power gas is superheated in the main heat exchanger and led to gas turbines. To protect the main heat exchanger against damage by overheating, the novel predictive protective control based on the mathematical model of exchanger was developed. The paper describes the principle, the design and the simulation of the predictive protective method applied to main heat exchanger of FES.
Prediction of active control of subsonic centrifugal compressor rotating stall
NASA Technical Reports Server (NTRS)
Lawless, Patrick B.; Fleeter, Sanford
1993-01-01
A mathematical model is developed to predict the suppression of rotating stall in a centrifugal compressor with a vaned diffuser. This model is based on the employment of a control vortical waveform generated upstream of the impeller inlet to damp weak potential disturbances that are the early stages of rotating stall. The control system is analyzed by matching the perturbation pressure in the compressor inlet and exit flow fields with a model for the unsteady behavior of the compressor. The model was effective at predicting the stalling behavior of the Purdue Low Speed Centrifugal Compressor for two distinctly different stall patterns. Predictions made for the effect of a controlled inlet vorticity wave on the stability of the compressor show that for minimum control wave magnitudes, on the order of the total inlet disturbance magnitude, significant damping of the instability can be achieved. For control waves of sufficient amplitude, the control phase angle appears to be the most important factor in maintaining a stable condition in the compressor.
Chang, Wen-Jer; Wu, Wen-Yuan; Ku, Cheung-Chieh
2011-01-01
The purpose of this paper is to study the H(∞) constrained fuzzy controller design problem for discrete-time Takagi-Sugeno (T-S) fuzzy systems with multiplicative noises by using the state observer feedback technique. The proposed fuzzy controller design approach is developed based on the Parallel Distributed Compensation (PDC) technique. Through the Lyapunov stability criterion, the stability analysis is completed to develop stability conditions for the closed-loop systems. Besides, the H(∞) performance constraints is also considered in the stability condition derivations for the worst case effect of disturbance on system states. Solving these stability conditions via the two-step Linear Matrix Inequality (LMI) algorithm, the observer-based fuzzy controller is obtained to achieve the stability and H(∞) performance constraints, simultaneously. Finally, a numerical example is provided to verify the applicability and effectiveness of the proposed fuzzy control approach.
Composite predictive flight control for airbreathing hypersonic vehicles
NASA Astrophysics Data System (ADS)
Yang, Jun; Zhao, Zhenhua; Li, Shihua; Zheng, Wei Xing
2014-09-01
The robust optimised tracking control problem for a generic airbreathing hypersonic vehicle (AHV) subject to nonvanishing mismatched disturbances/uncertainties is investigated in this paper. A baseline nonlinear model predictive control (MPC) method is firstly introduced for optimised tracking control of the nominal dynamics. A nonlinear-disturbance-observer-based control law is then developed for robustness enhancement in the presence of both external disturbances and uncertainties. Compared with the existing robust tracking control methods for AHVs, the proposed composite nonlinear MPC method obtains not only promising robustness and disturbance rejection performance but also optimised nominal tracking control performance. The merits of the proposed method are validated by implementing simulation studies on the AHV system.
Adaptive model predictive process control using neural networks
Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.
1997-01-01
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.
Adaptive model predictive process control using neural networks
Buescher, K.L.; Baum, C.C.; Jones, R.D.
1997-08-19
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.
Implementation of model predictive control on a hydrothermal oxidation reactor
Muske, K.R.; Dell`Orco, P.C.; Le, L.A.; Flesner, R.L.
1998-12-31
This paper describes the model-based control algorithm developed for a hydrothermal oxidation reactor at the Pantex Department of Energy facility in Amarillo, Texas. The combination of base hydrolysis and hydrothermal oxidation is used for the disposal of PBX 9404 high explosive at Pantex. The reactor oxidizes the organic compounds in the hydrolysate solutions obtained from the base hydrolysis process. The objective of the model predictive controller is to minimize the total aqueous nitrogen compounds in the effluent of the reactor. The controller also maintains a desired excess oxygen concentration in the reactor effluent to ensure the complete destruction of the organic carbon compounds in the hydrolysate.
Effects of modeling errors on trajectory predictions in air traffic control automation
NASA Technical Reports Server (NTRS)
Jackson, Michael R. C.; Zhao, Yiyuan; Slattery, Rhonda
1996-01-01
Air traffic control automation synthesizes aircraft trajectories for the generation of advisories. Trajectory computation employs models of aircraft performances and weather conditions. In contrast, actual trajectories are flown in real aircraft under actual conditions. Since synthetic trajectories are used in landing scheduling and conflict probing, it is very important to understand the differences between computed trajectories and actual trajectories. This paper examines the effects of aircraft modeling errors on the accuracy of trajectory predictions in air traffic control automation. Three-dimensional point-mass aircraft equations of motion are assumed to be able to generate actual aircraft flight paths. Modeling errors are described as uncertain parameters or uncertain input functions. Pilot or autopilot feedback actions are expressed as equality constraints to satisfy control objectives. A typical trajectory is defined by a series of flight segments with different control objectives for each flight segment and conditions that define segment transitions. A constrained linearization approach is used to analyze trajectory differences caused by various modeling errors by developing a linear time varying system that describes the trajectory errors, with expressions to transfer the trajectory errors across moving segment transitions. A numerical example is presented for a complete commercial aircraft descent trajectory consisting of several flight segments.
Health-aware Model Predictive Control of Pasteurization Plant
NASA Astrophysics Data System (ADS)
Karimi Pour, Fatemeh; Puig, Vicenç; Ocampo-Martinez, Carlos
2017-01-01
In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.
Nonlinear Dynamic Inversion Baseline Control Law: Architecture and Performance Predictions
NASA Technical Reports Server (NTRS)
Miller, Christopher J.
2011-01-01
A model reference dynamic inversion control law has been developed to provide a baseline control law for research into adaptive elements and other advanced flight control law components. This controller has been implemented and tested in a hardware-in-the-loop simulation; the simulation results show excellent handling qualities throughout the limited flight envelope. A simple angular momentum formulation was chosen because it can be included in the stability proofs for many basic adaptive theories, such as model reference adaptive control. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as basic as possible to simplify the addition of the adaptive elements. Those design choices are explained, along with their predicted impact on the handling qualities.
Decentralized robust nonlinear model predictive controller for unmanned aerial systems
NASA Astrophysics Data System (ADS)
Garcia Garreton, Gonzalo A.
The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1. A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2. A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3. An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4. A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible.
Tuning Proportional-Integral controllers to approximate simplified predictive control performance.
Mansour, S E
2009-10-01
An exact equivalence between PI (Proportional-Integral) and two-parameter SPC (Simplified Predictive Control) is developed to provide identical control of first order linear plants. A relationship between the PI control parameters and the SPC control parameters is described. This relationship that allows the same control in the case of first order linear plants is also found to provide tuning formulas that yield PI control which approximates SPC performance in the case of second order linear plants with widely separated Eigenvalues. Finally, an extension of the PI control algorithm to include future errors provides another exact PI-SPC equivalence for networked control of first order plants.
A nonlinear regression model-based predictive control algorithm.
Dubay, R; Abu-Ayyad, M; Hernandez, J M
2009-04-01
This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.
Model Predictive Control of Integrated Gasification Combined Cycle Power Plants
B. Wayne Bequette; Priyadarshi Mahapatra
2010-08-31
The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.
Predictive onboard flow control for packet switching satellites
NASA Technical Reports Server (NTRS)
Bobinsky, Eric A.
1992-01-01
We outline two alternate approaches to predicting the onset of congestion in a packet switching satellite, and argue that predictive, rather than reactive, flow control is necessary for the efficient operation of such a system. The first method discussed is based on standard, statistical techniques which are used to periodically calculate a probability of near-term congestion based on arrival rate statistics. If this probability exceeds a present threshold, the satellite would transmit a rate-reduction signal to all active ground stations. The second method discussed would utilize a neural network to periodically predict the occurrence of buffer overflow based on input data which would include, in addition to arrival rates, the distributions of packet lengths, source addresses, and destination addresses.
Model predictive control of a wind turbine modelled in Simpack
NASA Astrophysics Data System (ADS)
Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.
2014-06-01
Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to
Application of infinite model predictive control methodology to other advanced controllers.
Abu-Ayyad, M; Dubay, R; Hernandez, J M
2009-01-01
This paper presents an application of most recent developed predictive control algorithm an infinite model predictive control (IMPC) to other advanced control schemes. The IMPC strategy was derived for systems with different degrees of nonlinearity on the process gain and time constant. Also, it was shown that IMPC structure uses nonlinear open-loop modeling which is conducted while closed-loop control is executed every sampling instant. The main objective of this work is to demonstrate that the methodology of IMPC can be applied to other advanced control strategies making the methodology generic. The IMPC strategy was implemented on several advanced controllers such as PI controller using Smith-Predictor, Dahlin controller, simplified predictive control (SPC), dynamic matrix control (DMC), and shifted dynamic matrix (m-DMC). Experimental work using these approaches combined with IMPC was conducted on both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems and compared with the original forms of these advanced controllers. Computer simulations were performed on nonlinear plants demonstrating that the IMPC strategy can be readily implemented on other advanced control schemes providing improved control performance. Practical work included real-time control applications on a DC motor, plastic injection molding machine and a MIMO three zone thermal system.
Flutter prediction for a wing with active aileron control
NASA Technical Reports Server (NTRS)
Penning, K.; Sandlin, D. R.
1983-01-01
A method for predicting the vibrational stability of an aircraft with an analog active aileron flutter suppression system (FSS) is expained. Active aileron refers to the use of an active control system connected to the aileron to damp vibrations. Wing vibrations are sensed by accelerometers and the information is used to deflect the aileron. Aerodynamic force caused by the aileron deflection oppose wing vibrations and effectively add additional damping to the system.
Model predictive control for tracking randomly varying references
NASA Astrophysics Data System (ADS)
Falugi, Paola
2015-04-01
This paper proposes a model predictive control scheme for tracking a-priori unknown references varying in a wide range and analyses its performance. It is usual to assume that the reference eventually converges to a constant in which case convergence to zero of the tracking error can be established. In this note we remove this simplifying assumption and characterise the set to which the tracking error converges and the associated region of convergence.
Prediction and control of chaotic processes using nonlinear adaptive networks
Jones, R.D.; Barnes, C.W.; Flake, G.W.; Lee, K.; Lewis, P.S.; O'Rouke, M.K.; Qian, S.
1990-01-01
We present the theory of nonlinear adaptive networks and discuss a few applications. In particular, we review the theory of feedforward backpropagation networks. We then present the theory of the Connectionist Normalized Linear Spline network in both its feedforward and iterated modes. Also, we briefly discuss the theory of stochastic cellular automata. We then discuss applications to chaotic time series, tidal prediction in Venice lagoon, finite differencing, sonar transient detection, control of nonlinear processes, control of a negative ion source, balancing a double inverted pendulum and design advice for free electron lasers and laser fusion targets.
Punishment sensitivity predicts the impact of punishment on cognitive control.
Braem, Senne; Duthoo, Wout; Notebaert, Wim
2013-01-01
Cognitive control theories predict enhanced conflict adaptation after punishment. However, no such effect was found in previous work. In the present study, we demonstrate in a flanker task how behavioural adjustments following punishment signals are highly dependent on punishment sensitivity (as measured by the Behavioural Inhibition System (BIS) scale): Whereas low punishment-sensitive participants do show increased conflict adaptation after punishment, high punishment-sensitive participants show no such modulation. Interestingly, participants with a high punishment-sensitivity showed an overall reaction time increase after punishments. Our results stress the role of individual differences in explaining motivational modulations of cognitive control.
Experimental Investigations of Generalized Predictive Control for Tiltrotor Stability Augmentation
NASA Technical Reports Server (NTRS)
Nixon, Mark W.; Langston, Chester W.; Singleton, Jeffrey D.; Piatak, David J.; Kvaternik, Raymond G.; Bennett, Richard L.; Brown, Ross K.
2001-01-01
A team of researchers from the Army Research Laboratory, NASA Langley Research Center (LaRC), and Bell Helicopter-Textron, Inc. have completed hover-cell and wind-tunnel testing of a 1/5-size aeroelastically-scaled tiltrotor model using a new active control system for stability augmentation. The active system is based on a generalized predictive control (GPC) algorithm originally developed at NASA LaRC in 1997 for un-known disturbance rejection. Results of these investigations show that GPC combined with an active swashplate can significantly augment the damping and stability of tiltrotors in both hover and high-speed flight.
Applying new optimization algorithms to more predictive control
Wright, S.J.
1996-03-01
The connections between optimization and control theory have been explored by many researchers and optimization algorithms have been applied with success to optimal control. The rapid pace of developments in model predictive control has given rise to a host of new problems to which optimization has yet to be applied. Concurrently, developments in optimization, and especially in interior-point methods, have produced a new set of algorithms that may be especially helpful in this context. In this paper, we reexamine the relatively simple problem of control of linear processes subject to quadratic objectives and general linear constraints. We show how new algorithms for quadratic programming can be applied efficiently to this problem. The approach extends to several more general problems in straightforward ways.
Application of linear gauss pseudospectral method in model predictive control
NASA Astrophysics Data System (ADS)
Yang, Liang; Zhou, Hao; Chen, Wanchun
2014-03-01
This paper presents a model predictive control(MPC) method aimed at solving the nonlinear optimal control problem with hard terminal constraints and quadratic performance index. The method combines the philosophies of the nonlinear approximation model predictive control, linear quadrature optimal control and Gauss Pseudospectral method. The current control is obtained by successively solving linear algebraic equations transferred from the original problem via linearization and the Gauss Pseudospectral method. It is not only of high computational efficiency since it does not need to solve nonlinear programming problem, but also of high accuracy though there are a few discrete points. Therefore, this method is suitable for on-board applications. A design of terminal impact with a specified direction is carried out to evaluate the performance of this method. Augmented PN guidance law in the three-dimensional coordinate system is applied to produce the initial guess. And various cases for target with straight-line movements are employed to demonstrate the applicability in different impact angles. Moreover, performance of the proposed method is also assessed by comparison with other guidance laws. Simulation results indicate that this method is not only of high computational efficiency and accuracy, but also applicable in the framework of guidance design.
Casas, E.
1999-03-15
In this paper we are concerned with some optimal control problems governed by semilinear elliptic equations. The case of a boundary control is studied. We consider pointwise constraints on the control and a finite number of equality and inequality constraints on the state. The goal is to derive first- and second-order optimality conditions satisfied by locally optimal solutions of the problem.
NASA Astrophysics Data System (ADS)
Lai, Ying-Cheng; Harrison, Mary Ann F.; Frei, Mark G.; Osorio, Ivan
2004-09-01
Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy.
Control of nonlinear processes by using linear model predictive control algorithms.
Gu, Bingfeng; Gupta, Yash P
2008-04-01
Most chemical processes are inherently nonlinear. However, because of their simplicity, linear control algorithms have been used for the control of nonlinear processes. In this study, the use of the dynamic matrix control algorithm and a simplified model predictive control algorithm for control of a bench-scale pH neutralization process is investigated. The nonlinearity is handled by dividing the operating region into sub-regions and by switching the controller model as the process moves from one sub-region to another. A simple modification for model predictive control algorithms is presented to handle the switching. The simulation and experimental results show that the modification can provide a significant improvement in the control of nonlinear processes.
Jiang, Jiefeng; Beck, Jeffrey; Heller, Katherine; Egner, Tobias
2015-01-01
The anterior cingulate and lateral prefrontal cortices have been implicated in implementing context-appropriate attentional control, but the learning mechanisms underlying our ability to flexibly adapt the control settings to changing environments remain poorly understood. Here we show that human adjustments to varying control demands are captured by a reinforcement learner with a flexible, volatility-driven learning rate. Using model-based functional magnetic resonance imaging, we demonstrate that volatility of control demand is estimated by the anterior insula, which in turn optimizes the prediction of forthcoming demand in the caudate nucleus. The caudate's prediction of control demand subsequently guides the implementation of proactive and reactive attentional control in dorsal anterior cingulate and dorsolateral prefrontal cortices. These data enhance our understanding of the neuro-computational mechanisms of adaptive behaviour by connecting the classic cingulate-prefrontal cognitive control network to a subcortical control-learning mechanism that infers future demands by flexibly integrating remote and recent past experiences. PMID:26391305
A model predictive control approach for the Italian LBE XADS
NASA Astrophysics Data System (ADS)
Cammi, Antonio; Casella, Francesco; Luzzi, Lelio; Milano, Alessandro; Ricotti, Marco E.
2008-06-01
In this paper, model predictive control (MPC) is applied to the Italian 80 MW th experimental accelerator driven system (XADS), referring to a simple, non-linear model for the dynamic simulation of the plant, which has been developed and described in a previous work [A. Cammi, L. Luzzi, A.A. Porta, M.E. Ricotti, Prog. Nucl. Energ. 48 (2006) 578], in order to describe the interactions among the different subsystems: i.e., the accelerator-core coupling, the lead bismuth eutectic (LBE) primary system, the secondary system with diathermic oil and air coolers batteries, which reject the thermal power to the environment. Hereinafter, a model predictive controller is proposed, with the objective to minimize the difference between the average temperature of the diathermic oil and its reference value, while also minimizing the variations of the control input, which is the air coolers mass flow rate. The dynamic response of the LBE-XADS has been evaluated with reference to a reduction of 20% in the reactor power from nominal load conditions: this transient is very demanding for the overall plant, nevertheless the obtained results indicate the effectiveness of the proposed controller.
Prediction and control of neural responses to pulsatile electrical stimulation.
Campbell, Luke J; Sly, David James; O'Leary, Stephen John
2012-04-01
This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s(-1). A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s(-1). Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.
Prediction and control of neural responses to pulsatile electrical stimulation
NASA Astrophysics Data System (ADS)
Campbell, Luke J.; Sly, David James; O'Leary, Stephen John
2012-04-01
This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s-1. A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s-1. Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.
Error correction, sensory prediction, and adaptation in motor control.
Shadmehr, Reza; Smith, Maurice A; Krakauer, John W
2010-01-01
Motor control is the study of how organisms make accurate goal-directed movements. Here we consider two problems that the motor system must solve in order to achieve such control. The first problem is that sensory feedback is noisy and delayed, which can make movements inaccurate and unstable. The second problem is that the relationship between a motor command and the movement it produces is variable, as the body and the environment can both change. A solution is to build adaptive internal models of the body and the world. The predictions of these internal models, called forward models because they transform motor commands into sensory consequences, can be used to both produce a lifetime of calibrated movements, and to improve the ability of the sensory system to estimate the state of the body and the world around it. Forward models are only useful if they produce unbiased predictions. Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors.
Model predictive control power management strategies for HEVs: A review
NASA Astrophysics Data System (ADS)
Huang, Yanjun; Wang, Hong; Khajepour, Amir; He, Hongwen; Ji, Jie
2017-02-01
This paper presents a comprehensive review of power management strategy (PMS) utilized in hybrid electric vehicles (HEVs) with an emphasis on model predictive control (MPC) based strategies for the first time. Research on MPC-based power management systems for HEVs has intensified recently due to its many inherent merits. The categories of the existing PMSs are identified from the latest literature, and a brief study of each type is conducted. Then, the MPC approach is introduced and its advantages are discussed. Based on the acquisition method of driver behavior used for state prediction and the dynamic model used, the MPC is classified and elaborated. Factors that affect the performance of the MPC are put forward, including prediction accuracy, design parameters, and solvers. Finally, several important issues in the application of MPC-based power management strategies and latest developing trends are discussed. This paper not only provides a comprehensive analysis of MPC-based power management strategies for HEVs but also puts forward the future and emphasis of future study, which will promote the development of energy management controller with high performance and low cost for HEVs.
Structural Acoustic Prediction and Interior Noise Control Technology
NASA Technical Reports Server (NTRS)
Mathur, G. P.; Chin, C. L.; Simpson, M. A.; Lee, J. T.; Palumbo, Daniel L. (Technical Monitor)
2001-01-01
This report documents the results of Task 14, "Structural Acoustic Prediction and Interior Noise Control Technology". The task was to evaluate the performance of tuned foam elements (termed Smart Foam) both analytically and experimentally. Results taken from a three-dimensional finite element model of an active, tuned foam element are presented. Measurements of sound absorption and sound transmission loss were taken using the model. These results agree well with published data. Experimental performance data were taken in Boeing's Interior Noise Test Facility where 12 smart foam elements were applied to a 757 sidewall. Several configurations were tested. Noise reductions of 5-10 dB were achieved over the 200-800 Hz bandwidth of the controller. Accelerometers mounted on the panel provided a good reference for the controller. Configurations with far-field error microphones outperformed near-field cases.
Energy-efficient container handling using hybrid model predictive control
NASA Astrophysics Data System (ADS)
Xin, Jianbin; Negenborn, Rudy R.; Lodewijks, Gabriel
2015-11-01
The performance of container terminals needs to be improved to adapt the growth of containers while maintaining sustainability. This paper provides a methodology for determining the trajectory of three key interacting machines for carrying out the so-called bay handling task, involving transporting containers between a vessel and the stacking area in an automated container terminal. The behaviours of the interacting machines are modelled as a collection of interconnected hybrid systems. Hybrid model predictive control (MPC) is proposed to achieve optimal performance, balancing the handling capacity and energy consumption. The underlying control problem is hereby formulated as a mixed-integer linear programming problem. Simulation studies illustrate that a higher penalty on energy consumption indeed leads to improved sustainability using less energy. Moreover, simulations illustrate how the proposed energy-efficient hybrid MPC controller performs under different types of uncertainties.
Self-Tuning of Design Variables for Generalized Predictive Control
NASA Technical Reports Server (NTRS)
Lin, Chaung; Juang, Jer-Nan
2000-01-01
Three techniques are introduced to determine the order and control weighting for the design of a generalized predictive controller. These techniques are based on the application of fuzzy logic, genetic algorithms, and simulated annealing to conduct an optimal search on specific performance indexes or objective functions. Fuzzy logic is found to be feasible for real-time and on-line implementation due to its smooth and quick convergence. On the other hand, genetic algorithms and simulated annealing are applicable for initial estimation of the model order and control weighting, and final fine-tuning within a small region of the solution space, Several numerical simulations for a multiple-input and multiple-output system are given to illustrate the techniques developed in this paper.
Prakash, J; Srinivasan, K
2009-07-01
In this paper, the authors have represented the nonlinear system as a family of local linear state space models, local PID controllers have been designed on the basis of linear models, and the weighted sum of the output from the local PID controllers (Nonlinear PID controller) has been used to control the nonlinear process. Further, Nonlinear Model Predictive Controller using the family of local linear state space models (F-NMPC) has been developed. The effectiveness of the proposed control schemes has been demonstrated on a CSTR process, which exhibits dynamic nonlinearity.
Predictive control and estimation algorithms for the NASA/JPL 70-meter antennas
NASA Technical Reports Server (NTRS)
Gawronski, W.
1991-01-01
A modified output prediction procedure and a new controller design is presented based on the predictive control law. Also, a new predictive estimator is developed to complement the controller and to enhance system performance. The predictive controller is designed and applied to the tracking control of the Deep Space Network 70 m antennas. Simulation results show significant improvement in tracking performance over the linear quadratic controller and estimator presently in use.
Model Predictive Control for the Operation of Building Cooling Systems
Ma, Yudong; Borrelli, Francesco; Hencey, Brandon; Coffey, Brian; Bengea, Sorin; Haves, Philip
2010-06-29
A model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reduction in the central plant electricity cost and improvement of its efficiency.
NASA Astrophysics Data System (ADS)
Bu, Xiangwei; Wu, Xiaoyan; He, Guangjun; Huang, Jiaqi
2016-03-01
This paper investigates the design of a novel adaptive neural controller for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle with control input constraints. To reduce the complexity of controller design, the vehicle dynamics is decomposed into the velocity subsystem and the altitude subsystem, respectively. For each subsystem, only one neural network is utilized to approach the lumped unknown function. By employing a minimal-learning parameter method to estimate the norm of ideal weight vectors rather than their elements, there are only two adaptive parameters required for neural approximation. Thus, the computational burden is lower than the ones derived from neural back-stepping schemes. Specially, to deal with the control input constraints, additional systems are exploited to compensate the actuators. Lyapunov synthesis proves that all the closed-loop signals involved are uniformly ultimately bounded. Finally, simulation results show that the adopted compensation scheme can tackle actuator constraint effectively and moreover velocity and altitude can stably track their reference trajectories even when the physical limitations on control inputs are in effect.
Tuning the Model Predictive Control of a Crude Distillation Unit.
Yamashita, André Shigueo; Zanin, Antonio Carlos; Odloak, Darci
2016-01-01
Tuning the parameters of the Model Predictive Control (MPC) of an industrial Crude Distillation Unit (CDU) is considered here. A realistic scenario is depicted where the inputs of the CDU system have optimizing targets, which are provided by the Real Time Optimization layer of the control structure. It is considered the nominal case, in which both the CDU model and the MPC model are the same. The process outputs are controlled inside zones instead of at fixed set points. Then, the tuning procedure has to define the weights that penalize the output error with respect to the control zone, the weights that penalize the deviation of the inputs from their targets, as well as the weights that penalize the input moves. A tuning approach based on multi-objective optimization is proposed and applied to the MPC of the CDU system. The performance of the controller tuned with the proposed approach is compared through simulation with the results of an existing approach also based on multi-objective optimization. The simulation results are similar, but the proposed approach has a computational load significantly lower than the existing method. The tuning effort is also much lower than in the conventional practical approaches that are usually based on ad-hoc procedures.
NASA Astrophysics Data System (ADS)
Mendoza, Marco; Zavala-Río, Arturo; Santibáñez, Víctor; Reyes, Fernando
2015-10-01
In this paper, a globally stabilising PID-type control scheme with a generalised saturating structure for robot manipulators under input constraints is proposed. It gives rise to various families of bounded PID-type controllers whose implementation is released from the exact knowledge of the system parameters and model structure. Compared to previous approaches of the kind, the proposed scheme is not only characterised by its generalised structure but also by its very simple tuning criterion, the simplest hitherto obtained in the considered analytical framework. Experimental results on a 3-degree-of-freedom direct-drive manipulator corroborate the efficiency of the proposed approach.
NASA Technical Reports Server (NTRS)
Arneson, Heather M.; Dousse, Nicholas; Langbort, Cedric
2014-01-01
We consider control design for positive compartmental systems in which each compartment's outflow rate is described by a concave function of the amount of material in the compartment.We address the problem of determining the routing of material between compartments to satisfy time-varying state constraints while ensuring that material reaches its intended destination over a finite time horizon. We give sufficient conditions for the existence of a time-varying state-dependent routing strategy which ensures that the closed-loop system satisfies basic network properties of positivity, conservation and interconnection while ensuring that capacity constraints are satisfied, when possible, or adjusted if a solution cannot be found. These conditions are formulated as a linear programming problem. Instances of this linear programming problem can be solved iteratively to generate a solution to the finite horizon routing problem. Results are given for the application of this control design method to an example problem. Key words: linear programming; control of networks; positive systems; controller constraints and structure.
Preview Scheduled Model Predictive Control For Horizontal Axis Wind Turbines
NASA Astrophysics Data System (ADS)
Laks, Jason H.
This research investigates the use of model predictive control (MPC) in application to wind turbine operation from start-up to cut-out. The studies conducted are focused on the design of an MPC controller for a 650˜KW, three-bladed horizontal axis turbine that is in operation at the National Renewable Energy Laboratory's National Wind Technology Center outside of Golden, Colorado. This turbine is at the small end of utility scale turbines, but it provides advanced instrumentation and control capabilities, and there is a good probability that the approach developed in simulation for this thesis, will be field tested on the actual turbine. A contribution of this thesis is a method to combine the use of preview measurements with MPC while also providing regulation of turbine speed and cyclic blade loading. A common MPC technique provides integral-like control to achieve offset-free operation. At the same time in wind turbine applications, multiple studies have developed "feed-forward" controls based on applying a gain to an estimate of the wind speed changes obtained from an observer incorporating a disturbance model. These approaches are based on a technique that can be referred to as disturbance accommodating control (DAC). In this thesis, it is shown that offset-free tracking MPC is equivalent to a DAC approach when the disturbance gain is computed to satisfy a regulator equation. Although the MPC literature has recognized that this approach provides "structurally stable" disturbance rejection and tracking, this step is not typically divorced from the MPC computations repeated each sample hit. The DAC formulation is conceptually simpler, and essentially uncouples regulation considerations from MPC related issues. This thesis provides a self contained proof that the DAC formulation (an observer-controller and appropriate disturbance gain) provides structurally stable regulation.
Predictive models of procedural human supervisory control behavior
NASA Astrophysics Data System (ADS)
Boussemart, Yves
Human supervisory control systems are characterized by the computer-mediated nature of the interactions between one or more operators and a given task. Nuclear power plants, air traffic management and unmanned vehicles operations are examples of such systems. In this context, the role of the operators is typically highly proceduralized due to the time and mission-critical nature of the tasks. Therefore, the ability to continuously monitor operator behavior so as to detect and predict anomalous situations is a critical safeguard for proper system operation. In particular, such models can help support the decision J]l8king process of a supervisor of a team of operators by providing alerts when likely anomalous behaviors are detected By exploiting the operator behavioral patterns which are typically reinforced through standard operating procedures, this thesis proposes a methodology that uses statistical learning techniques in order to detect and predict anomalous operator conditions. More specifically, the proposed methodology relies on hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) to generate predictive models of unmanned vehicle systems operators. Through the exploration of the resulting HMMs in two distinct single operator scenarios, the methodology presented in this thesis is validated and shown to provide models capable of reliably predicting operator behavior. In addition, the use of HSMMs on the same data scenarios provides the temporal component of the predictions missing from the HMMs. The final step of this work is to examine how the proposed methodology scales to more complex scenarios involving teams of operators. Adopting a holistic team modeling approach, both HMMs and HSMMs are learned based on two team-based data sets. The results show that the HSMMs can provide valuable timing information in the single operator case, whereas HMMs tend to be more robust to increased team complexity. In addition, this thesis discusses the
Striatal prediction errors support dynamic control of declarative memory decisions
Scimeca, Jason M.; Katzman, Perri L.; Badre, David
2016-01-01
Adaptive memory requires context-dependent control over how information is retrieved, evaluated and used to guide action, yet the signals that drive adjustments to memory decisions remain unknown. Here we show that prediction errors (PEs) coded by the striatum support control over memory decisions. Human participants completed a recognition memory test that incorporated biased feedback to influence participants' recognition criterion. Using model-based fMRI, we find that PEs—the deviation between the outcome and expected value of a memory decision—correlate with striatal activity and predict individuals' final criterion. Importantly, the striatal PEs are scaled relative to memory strength rather than the expected trial outcome. Follow-up experiments show that the learned recognition criterion transfers to free recall, and targeting biased feedback to experimentally manipulate the magnitude of PEs influences criterion consistent with PEs scaled relative to memory strength. This provides convergent evidence that declarative memory decisions can be regulated via striatally mediated reinforcement learning signals. PMID:27713407
NASA Astrophysics Data System (ADS)
Li, Kunpeng
2017-01-01
The compatibility problem between rapidity and overshooting in the traditional predictive current control structure is inevitable and difficult to solve by reason of using PI controller. A novel predictive current control (PCC) algorithm for permanent magnet synchronous motor (PMSM) based on linear active disturbance rejection control (LADRC) is presented in this paper. In order to displace PI controller, the LADRC strategy which consisted of linear state error feedback (LSEF) control algorithm and linear extended state observer (LESO), is designed based on the mathematic model of PMSM. The purpose of LSEF is to make sure fast response to load mutation and system uncertainties, and LESO is designed to estimate the uncertain disturbances. The principal structures of the proposed system are speed outer loop based on LADRC and current inner loop based on predictive current control. Especially, the instruction value of qaxis current in inner loop is derived from the control quantity which is designed in speed outer loop. The simulation is carried out in Matlab/Simulink software, and the results illustrate that the dynamic and static performances of proposed system are satisfied. Moreover the robust against model parameters mismatch is enhanced obviously.
Humans are sensitive to attention control when predicting others’ actions
Pesquita, Ana; Chapman, Craig S.; Enns, James T.
2016-01-01
Studies of social perception report acute human sensitivity to where another’s attention is aimed. Here we ask whether humans are also sensitive to how the other’s attention is deployed. Observers viewed videos of actors reaching to targets without knowing that those actors were sometimes choosing to reach to one of the targets (endogenous control) and sometimes being directed to reach to one of the targets (exogenous control). Experiments 1 and 2 showed that observers could respond more rapidly when actors chose where to reach, yet were at chance when guessing whether the reach was chosen or directed. This implicit sensitivity to attention control held when either actor’s faces or limbs were masked (experiment 3) and when only the earliest actor’s movements were visible (experiment 4). Individual differences in sensitivity to choice correlated with an independent measure of social aptitude. We conclude that humans are sensitive to attention control through an implicit kinematic process linked to empathy. The findings support the hypothesis that social cognition involves the predictive modeling of others’ attentional states. PMID:27436897
Design and Performance Analysis of Incremental Networked Predictive Control Systems.
Pang, Zhong-Hua; Liu, Guo-Ping; Zhou, Donghua
2016-06-01
This paper is concerned with the design and performance analysis of networked control systems with network-induced delay, packet disorder, and packet dropout. Based on the incremental form of the plant input-output model and an incremental error feedback control strategy, an incremental networked predictive control (INPC) scheme is proposed to actively compensate for the round-trip time delay resulting from the above communication constraints. The output tracking performance and closed-loop stability of the resulting INPC system are considered for two cases: 1) plant-model match case and 2) plant-model mismatch case. For the former case, the INPC system can achieve the same output tracking performance and closed-loop stability as those of the corresponding local control system. For the latter case, a sufficient condition for the stability of the closed-loop INPC system is derived using the switched system theory. Furthermore, for both cases, the INPC system can achieve a zero steady-state output tracking error for step commands. Finally, both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method.
Study on Noise Prediction Model and Control Schemes for Substation
Gao, Yang; Liu, Songtao
2014-01-01
With the government's emphasis on environmental issues of power transmission and transformation project, noise pollution has become a prominent problem now. The noise from the working transformer, reactor, and other electrical equipment in the substation will bring negative effect to the ambient environment. This paper focuses on using acoustic software for the simulation and calculation method to control substation noise. According to the characteristics of the substation noise and the techniques of noise reduction, a substation's acoustic field model was established with the SoundPLAN software to predict the scope of substation noise. On this basis, 4 reasonable noise control schemes were advanced to provide some helpful references for noise control during the new substation's design and construction process. And the feasibility and application effect of these control schemes can be verified by using the method of simulation modeling. The simulation results show that the substation always has the problem of excessive noise at boundary under the conventional measures. The excess noise can be efficiently reduced by taking the corresponding noise reduction methods. PMID:24672356
Morisset, J. B.; Mothe, F.; Bock, J.; Bréda, N.; Colin, F.
2012-01-01
Background and Aims There is increasing evidence that suppressed bud burst and thus epicormic shoot emergence (sprouting) are controlled by water–carbohydrate supplies to entire trees and buds. This direct evidence is still lacking for oak. In other respects, recent studies focused on sessile oak, Quercus petraea, have confirmed the important constraints of sprouting by epicormic ontogeny. The main objective of this paper was thus to provide provisional confirmation of the water–carbohydrate control and direct evidence of the ontogenic constraints by bringing together results already published in separate studies on water status and distribution of carbohydrates, and on accompanying vegetation and epicormics, which also quantify epicormic ontogeny. Methods This paper analyses results gained from a sessile oak experiment in which part of the site was free from fairly tall, dense accompanying vegetation. This experiment was initially focused on stand water status and more recently on the carbohydrate distribution of dominant trees. External observations of the epicormic composition and internal observations with X-ray computer tomography were undertaken on 60 and six trees, respectively. Key Results Sprouting was more intense in the part of the stand free from accompanying vegetation and on upper trunk segments. A clear effect of epicormic ontogeny was demonstrated as well: the more epicormics a trunk segment bears, the more chances it had to bear sprouts. Conclusions These results indirectly infer water–carbohydrate control and show direct evidence of constraints by epicormic ontogeny. These results have far-reaching consequences related to the quantification of all functions fulfilled by any type of epicormic structure in any part of the tree. PMID:22147545
NASA Astrophysics Data System (ADS)
Fei, H. Z.; Zheng, G. T.; Liu, Z. G.
2006-09-01
We report the results of a recent study for the active vibration isolation with whole-spacecraft vibration isolation as an application background into which three parts are divided: (i) energy source control, (ii) nonlinearity and time delay, (iii) implementation and experiment. This paper is the first in this three-part series report, which presents theoretical and experimental investigations into pressure tracking system for energy source control of the isolator. Considering the special environment of the rocket and expected characteristics of actuators, where the isolator will be arranged between the rocket and the spacecraft, pneumatic actuator is proposed to realize the active isolation control. In order to improve the dynamic characteristics of the pneumatic isolator, a cascade control algorithm with double loop structure and predictive control algorithm for pressure tracking control of the inner loop are proposed. In the current paper, a pressure tracking control system using model predictive control (MPC) is studied first. A pneumatic model around pressure work point is built firstly by simplifying the flow equation of valve's orifices and pressure differential equation of the chambers. With this model, an MPC algorithm in the state space is developed, and problems including control parameter choice and command horizon generator are discussed in detail. In addition, by adding model error correction loop and velocity compensation feedback, effects of model uncertainty and volume variation of chambers are reduced greatly. Thus with this design, the real-time pressure tracking can be guaranteed, and so that the active control system can work at higher frequency range.
NASA Astrophysics Data System (ADS)
Ali, Zeeshan; Popov, Atanas A.; Charles, Guy
2013-06-01
A vehicle following control law, based on the model predictive control method, to perform transition manoeuvres (TMs) for a nonlinear adaptive cruise control (ACC) vehicle is presented in this paper. The TM controller ultimately establishes a steady-state following distance behind a preceding vehicle to avoid collision, keeping account of acceleration limits, safe distance, and state constraints. The vehicle dynamics model is for continuous-time domain and captures the real dynamics of the sub-vehicle models for steady-state and transient operations. The ACC vehicle can execute the TM successfully and achieves a steady-state in the presence of complex dynamics within the constraint boundaries.
NASA Astrophysics Data System (ADS)
Hofer, Christoph; Papritz, Andreas
2011-10-01
The article describes the R-package constrainedKriging, a tool for spatial prediction problems that involve change of support. The package provides software for spatial interpolation by constrained (CK), covariance-matching constrained (CMCK), and customary universal (UK) kriging. CK and CMCK yield approximately unbiased predictions of nonlinear functionals of target quantities under change of support and are therefore an attractive alternative to conditional Gaussian simulations. The constrainedKriging package computes CK, CMCK, and UK predictions for points or blocks of arbitrary shape from data observed at points in a two-dimensional survey domain. Predictions are computed for a random process model that involves a nonstationary mean function (modeled by a linear regression) and a weakly stationary, isotropic covariance function (or variogram). CK, CMCK, and UK require the point-block and block-block averages of the covariance function if the prediction targets are blocks. The constrainedKriging package uses numerically efficient approximations to compute these averages. The article contains, apart from a brief summary of CK and CMCK, a detailed description of the algorithm used to compute the point-block and block-block covariances, and it describes the functionality of the software in detail. The practical use of the package is illustrated by a comparison of universal and constrained lognormal block kriging for the Meuse Bank heavy metal data set.
Jacox, Michael G.; Hazen, Elliott L.; Bograd, Steven J.
2016-01-01
In Eastern Boundary Current systems, wind-driven upwelling drives nutrient-rich water to the ocean surface, making these regions among the most productive on Earth. Regulation of productivity by changing wind and/or nutrient conditions can dramatically impact ecosystem functioning, though the mechanisms are not well understood beyond broad-scale relationships. Here, we explore bottom-up controls during the California Current System (CCS) upwelling season by quantifying the dependence of phytoplankton biomass (as indicated by satellite chlorophyll estimates) on two key environmental parameters: subsurface nitrate concentration and surface wind stress. In general, moderate winds and high nitrate concentrations yield maximal biomass near shore, while offshore biomass is positively correlated with subsurface nitrate concentration. However, due to nonlinear interactions between the influences of wind and nitrate, bottom-up control of phytoplankton cannot be described by either one alone, nor by a combined metric such as nitrate flux. We quantify optimal environmental conditions for phytoplankton, defined as the wind/nitrate space that maximizes chlorophyll concentration, and present a framework for evaluating ecosystem change relative to environmental drivers. The utility of this framework is demonstrated by (i) elucidating anomalous CCS responses in 1998–1999, 2002, and 2005, and (ii) providing a basis for assessing potential biological impacts of projected climate change. PMID:27278260
NASA Astrophysics Data System (ADS)
Jacox, Michael G.; Hazen, Elliott L.; Bograd, Steven J.
2016-06-01
In Eastern Boundary Current systems, wind-driven upwelling drives nutrient-rich water to the ocean surface, making these regions among the most productive on Earth. Regulation of productivity by changing wind and/or nutrient conditions can dramatically impact ecosystem functioning, though the mechanisms are not well understood beyond broad-scale relationships. Here, we explore bottom-up controls during the California Current System (CCS) upwelling season by quantifying the dependence of phytoplankton biomass (as indicated by satellite chlorophyll estimates) on two key environmental parameters: subsurface nitrate concentration and surface wind stress. In general, moderate winds and high nitrate concentrations yield maximal biomass near shore, while offshore biomass is positively correlated with subsurface nitrate concentration. However, due to nonlinear interactions between the influences of wind and nitrate, bottom-up control of phytoplankton cannot be described by either one alone, nor by a combined metric such as nitrate flux. We quantify optimal environmental conditions for phytoplankton, defined as the wind/nitrate space that maximizes chlorophyll concentration, and present a framework for evaluating ecosystem change relative to environmental drivers. The utility of this framework is demonstrated by (i) elucidating anomalous CCS responses in 1998–1999, 2002, and 2005, and (ii) providing a basis for assessing potential biological impacts of projected climate change.
NASA Astrophysics Data System (ADS)
Carlota, Clara; Chá, Sílvia; Ornelas, António
2016-07-01
We generalize the Liapunov convexity theorem's version for vectorial control systems driven by linear ODEs of first-order p = 1, in any dimension d ∈ N, by including a pointwise state-constraint. More precisely, given a x ‾ (ṡ) ∈W p , 1 ([ a , b ] ,Rd) solving the convexified p-th order differential inclusion Lp x ‾ (t) ∈ co {u0 (t) ,u1 (t) , … ,um (t) } a.e., consider the general problem consisting in finding bang-bang solutions (i.e. Lp x ˆ (t) ∈ {u0 (t) ,u1 (t) , … ,um (t) } a.e.) under the same boundary-data, x ˆ (k) (a) =x ‾ (k) (a) &x ˆ (k) (b) =x ‾ (k) (b) (k = 0 , 1 , … , p - 1); but restricted, moreover, by a pointwise state constraint of the type < x ˆ (t) , ω > ≤ < x ‾ (t) , ω > ∀ t ∈ [ a , b ] (e.g. ω = (1 , 0 , … , 0) yielding xˆ1 (t) ≤x‾1 (t)). Previous results in the scalar d = 1 case were the pioneering Amar & Cellina paper (dealing with Lp x (ṡ) =x‧ (ṡ)), followed by Cerf & Mariconda results, who solved the general case of linear differential operators Lp of order p ≥ 2 with C0 ([ a , b ]) -coefficients. This paper is dedicated to: focus on the missing case p = 1, i.e. using Lp x (ṡ) =x‧ (ṡ) + A (ṡ) x (ṡ) ; generalize the dimension of x (ṡ) , from the scalar case d = 1 to the vectorial d ∈ N case; weaken the coefficients, from continuous to integrable, so that A (ṡ) now becomes a d × d-integrable matrix; and allow the directional vector ω to become a moving AC function ω (ṡ) . Previous vectorial results had constant ω, no matrix (i.e. A (ṡ) ≡ 0) and considered: constant control-vertices (Amar & Mariconda) and, more recently, integrable control-vertices (ourselves).
Two-Step Design Method of Engine Control System Based on Generalized Predictive Control
NASA Astrophysics Data System (ADS)
Hashimoto, Seiji; Okuda, Hiroyuki; Okada, Yasushi; Adachi, Shuichi; Niwa, Shinji; Kajitani, Mitsunobu
Conservation of the environment has become critical to the automotive industry. Recently, requirements for on-board diagnostic and engine control systems have been strictly enforced. In the present paper, in order to meet the requirements for a low-emissions vehicle, a novel construction method of the air-fuel ratio (A/F) control system is proposed. The construction method of the system is divided into two steps. The first step is to design the A/F control system for the engine based on an open loop design. The second step is to design the A/F control system for the catalyst system. The design method is based on the generalized predictive control in order to satisfy the robustness to open loop control as well as model uncertainty. The effectiveness of the proposed A/F control system is verified through experiments using full-scale products.
Montgomery, P.; Farr, M.R.; Franseen, E.K.; Goldstein, R.H.
2001-01-01
A high-resolution chronostratigraphy has been developed for Miocene shallow-water carbonate strata in the Cabo de Gata region of SE Spain for evaluation of local, regional and global factors that controlled platform architecture prior to and during the Messinian salinity crisis. Paleomagnetic data were collected from strata at three localities. Mean natural remanent magnetization (NRM) ranges between 1.53 ?? 10-8 and 5.2 ?? 10-3 Am2/kg. Incremental thermal and alternating field demagnetization isolated the characteristic remanent magnetization (ChRM). Rock magnetic studies show that the dominant magnetic mineral is magnetite, but mixtures of magnetite and hematite occur. A composite chronostratigraphy was derived from five stratigraphic sections. Regional stratigraphic data, biostratigraphic data, and an 40Ar/39Ar date of 8.5 ?? 0.1 Ma, for an interbedded volcanic flow, place the strata in geomagnetic polarity Chrons C4r to C3r. Sequence-stratigraphic and diagenetic evidence indicate a major unconformity at the base of depositional sequence (DS)3 that contains a prograding reef complex, suggesting that approximately 250 000 yr of record (Subchrons C3Br.2r to 3Br.1r) are missing near the Messinian-Tortonian boundary. Correlation to the GPTS shows that the studied strata represent five third- to fourth-order DSs. Basal units are temperate to subtropical ramps (DS1A, DS1B, DS2); these are overlain by subtropical to tropical reefal platforms (DS3), which are capped by subtropical to tropical cyclic carbonates (Terminal Carbonate Complex, TCC). Correlation of the Cabo de Gata record to the Melilla area of Morocco, and the Sorbas basin of Spain indicate that early - Late Tortonian ramp strata from these areas are partially time-equivalent. Similar strata are extensively developed in the Western Mediterranean and likely were influenced by a cool climate or influx of nutrients during an overall rise in global sea-level. After ramp deposition, a sequence boundary (SB3) in
Inlet Flow Control and Prediction Technologies for Embedded Propulsion Systems
NASA Technical Reports Server (NTRS)
McMillan, Michelle L.; Gissen, Abe; Vukasinovic, Bojan; Lakebrink, Matthew T.; Glezer, Ari; Mani, Mori; Mace, James
2010-01-01
Fail-safe inlet flow control may enable high-speed cruise efficiency, low noise signature, and reduced fuel-burn goals for hybrid wing-body aircraft. The objectives of this program are to develop flow control and prediction methodologies for boundary-layer ingesting (BLI) inlets used in these aircraft. This report covers the second of a three year program. The approach integrates experiments and numerical simulations. Both passive and active flow-control devices were tested in a small-scale wind tunnel. Hybrid actuation approaches, combining a passive microvane and active synthetic jet, were tested in various geometric arrangements. Detailed flow measurements were taken to provide insight into the flow physics. Results of the numerical simulations were correlated against experimental data. The sensitivity of results to grid resolution and turbulence models was examined. Aerodynamic benefits from microvanes and microramps were assessed when installed in an offset BLI inlet. Benefits were quantified in terms of recovery and distortion changes. Microvanes were more effective than microramps at improving recovery and distortion.
Inlet Flow Control and Prediction Technologies for Embedded Propulsion Systems
NASA Technical Reports Server (NTRS)
McMillan, Michelle L.; Mackie, Scott A.; Gissen, Abe; Vukasinovic, Bojan; Lakebrink, Matthew T.; Glezer, Ari; Mani, Mori; Mace, James L.
2011-01-01
Fail-safe, hybrid, flow control (HFC) is a promising technology for meeting high-speed cruise efficiency, low-noise signature, and reduced fuel-burn goals for future, Hybrid-Wing-Body (HWB) aircraft with embedded engines. This report details the development of HFC technology that enables improved inlet performance in HWB vehicles with highly integrated inlets and embedded engines without adversely affecting vehicle performance. In addition, new test techniques for evaluating Boundary-Layer-Ingesting (BLI)-inlet flow-control technologies developed and demonstrated through this program are documented, including the ability to generate a BLI-like inlet-entrance flow in a direct-connect, wind-tunnel facility, as well as, the use of D-optimal, statistically designed experiments to optimize test efficiency and enable interpretation of results. Validated improvements in numerical analysis tools and methods accomplished through this program are also documented, including Reynolds-Averaged Navier-Stokes CFD simulations of steady-state flow physics for baseline, BLI-inlet diffuser flow, as well as, that created by flow-control devices. Finally, numerical methods were employed in a ground-breaking attempt to directly simulate dynamic distortion. The advances in inlet technologies and prediction tools will help to meet and exceed "N+2" project goals for future HWB aircraft.
Prediction-based association control scheme in dense femtocell networks
Pham, Ngoc-Thai; Huynh, Thong; Hwang, Won-Joo; You, Ilsun; Choo, Kim-Kwang Raymond
2017-01-01
The deployment of large number of femtocell base stations allows us to extend the coverage and efficiently utilize resources in a low cost manner. However, the small cell size of femtocell networks can result in frequent handovers to the mobile user, and consequently throughput degradation. Thus, in this paper, we propose predictive association control schemes to improve the system’s effective throughput. Our design focuses on reducing handover frequency without impacting on throughput. The proposed schemes determine handover decisions that contribute most to the network throughput and are proper for distributed implementations. The simulation results show significant gains compared with existing methods in terms of handover frequency and network throughput perspective. PMID:28328992
Redesigned Predictive Event-Triggered Controller for Networked Control System With Delays.
Wu, Di; Sun, Xi-Ming; Wen, Changyun; Wang, Wei
2016-10-01
Event-triggered control (ETC) is a control strategy which can effectively reduce communication traffic in control networks. In the case where communication resources are scarce, ETC plays an important role in updating and communicating data. When network-induced delays are involved, two unsynchronized phenomena will appear if the existing ETC strategy, designed for networked control systems (NCSs) free of delays, is adopted. This paper deals with the ETC problem for NCS with delays existing in both sensor-to-controller and controller-to-actuator channels. A new predictive ETC strategy is proposed to solve both unsynchronized problems. It is shown that the stability of the resulting closed-loop system can be guaranteed under such an ETC strategy. Finally, both simulation studies and experimental tests are carried out to illustrate the proposed technique and verify its effectiveness.
Predictive wavefront control for Adaptive Optics with arbitrary control loop delays
Poyneer, L A; Veran, J
2007-10-30
We present a modification of the closed-loop state space model for AO control which allows delays that are a non-integer multiple of the system frame rate. We derive the new forms of the Predictive Fourier Control Kalman filters for arbitrary delays and show that they are linear combinations of the whole-frame delay terms. This structure of the controller is independent of the delay. System stability margins and residual error variance both transition gracefully between integer-frame delays.
NASA Astrophysics Data System (ADS)
Cihan, A.; Birkholzer, J. T.; Bianchi, M.
2014-12-01
Injection of large volume of CO2 into deep geological reservoirs for geologic carbon sequestration (GCS) is expected to cause significant pressure perturbations in subsurface. Large-scale pressure increases in injection reservoirs during GCS operations, if not controlled properly, may limit dynamic storage capacity and increase risk of environmental impacts. The high pressure may impact caprock integrity, induce fault slippage, and cause leakage of brine and/or CO2 into shallow fresh groundwater resources. Thus, monitoring and controlling pressure buildup are critically important for environmentally safe implementation of GCS projects. Extraction of native brine during GCS operations is a pressure management approach to reduce significant pressure buildup. Extracted brine can be transferred to the surface for utilization or re-injected into overlying/underlying saline aquifers. However, pumping, transportation, treatment and disposal of extracted brine can be challenging and costly. Therefore, minimizing volume of extracted brine, while maximizing CO2 storage, is an essential objective of the pressure management with brine extraction schemes. Selection of optimal well locations and extraction rates are critical for maximizing storage and minimizing brine extraction during GCS. However, placing of injection and extraction wells is not intuitive because of heterogeneity in reservoir properties and complex reservoir geometry. Efficient computerized algorithms combining reservoir models and optimization methods are needed to make proper decisions on well locations and control parameters. This study presents a global optimization methodology for pressure management during geologic CO2 sequestration. A constrained differential evolution (CDE) algorithm is introduced for solving optimization problems involving well placement and injection/extraction control. The CDE methodology is tested and applied for realistic CO2 storage scenarios with the presence of uncertainty in
Predictive active disturbance rejection control for processes with time delay.
Zheng, Qinling; Gao, Zhiqiang
2014-07-01
Active disturbance rejection control (ADRC) has been shown to be an effective tool in dealing with real world problems of dynamic uncertainties, disturbances, nonlinearities, etc. This paper addresses its existing limitations with plants that have a large transport delay. In particular, to overcome the delay, the extended state observer (ESO) in ADRC is modified to form a predictive ADRC, leading to significant improvements in the transient response and stability characteristics, as shown in extensive simulation studies and hardware-in-the-loop tests, as well as in the frequency response analysis. In this research, it is assumed that the amount of delay is approximately known, as is the approximated model of the plant. Even with such uncharacteristic assumptions for ADRC, the proposed method still exhibits significant improvements in both performance and robustness over the existing methods such as the dead-time compensator based on disturbance observer and the Filtered Smith Predictor, in the context of some well-known problems of chemical reactor and boiler control problems.
Factors Predicting Atypical Development of Nighttime Bladder Control
Sullivan, Sarah; Heron, Jon
2015-01-01
ABSTRACT: Objective: To derive latent classes (longitudinal “phenotypes”) of frequency of bedwetting from 4 to 9 years and to examine their association with developmental delay, parental history of bedwetting, length of gestation and birth weight. Method: The authors used data from 8,769 children from the UK Avon Longitudinal Study of Parents and Children cohort. Mothers provided repeated reports on their child's frequency of bedwetting from 4 to 9 years. The authors used longitudinal latent class analysis to derive latent classes of bedwetting and examined their association with sex, developmental level at 18 months, parental history of wetting, birth weight, and gestational length. Results: The authors identified 5 latent classes: (1) “normative”—low probability of bedwetting; (2) “infrequent delayed”—delayed attainment of nighttime bladder control with bedwetting
A Disturbance Rejection for Model Predictive Control Using a Multivariable Disturbance Observer
NASA Astrophysics Data System (ADS)
Tange, Yoshio; Matsui, Tetsuro; Matsumoto, Koji; Nishida, Hideyuki
Model predictive control has been widely used in industrial applications. And more efficient and more precise control is being required to meet growing demands such as energy savings and fewer emissions in industrial plants. In this paper, we focus on step response model based predictive control, which is one of most applied predictive control methods, and propose a new disturbance rejection method to overcome control performance degradation caused by unmeasured ramp-like disturbances.
Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control
Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.
1997-01-01
One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.
Predictive powertrain control using powertrain history and GPS data
Weslati, Feisel; Krupadanam, Ashish A
2015-03-03
A method and powertrain apparatus that predicts a route of travel for a vehicle and uses historical powertrain loads and speeds for the predicted route of travel to optimize at least one powertrain operation for the vehicle.
EPR oxygen images predict tumor control by a 50 percent tumor control radiation dose
Elas, Martyna; Magwood, Jessica M.; Butler, Brandi; Li, Chanel; Wardak, Rona; Barth, Eugene D.; Epel, Boris; Rubinstein, Samuel; Pelizzari, Charles A.; Weichselbaum, Ralph R.; Halpern, Howard J.
2013-01-01
Clinical trials to ameliorate hypoxia as a strategy to relieve the radiation resistance it causes have prompted a need to assay the precise extent and location of hypoxia in tumors. Electron Paramagnetic Resonance oxygen imaging (EPR O2 imaging) provides a non-invasive means to address this need. To obtain a preclinical proof of principle that EPR O2 images could predict radiation control, we treated mouse tumors at or near doses required to achieve 50 percent control (TCD50). Mice with FSa fibrosarcoma or MCa4 carcinoma were subjected to EPR O2 imaging and immediately radiated to a TCD50 or TCD50 ±10 Gy.. Statistical analysis was permitted by collection of ~ 1300 tumor pO2 image voxels, including the fraction of tumor voxels with pO2 less than 10 mm Hg (HF10). Tumors were followed for 90 days (FSa) or 120 days (MCa4) to determine local control or failure. HF10 obtained from EPR images showed statistically significant differences between tumors that were controlled by the TCD50 and those that were not controlled for both FSa and MCa4. Kaplan-Meier analysis of both types of tumors showed ~90% of mildly hypoxic tumors were controlled (HF10<10%), and only 37% (FSA) and 23% (MCa4) tumors controlled if hypoxic. EPR pO2 image voxel distributions in these ~0.5 ml tumors provide a prediction of radiation curability independent of radiation dose. These data confirm the significance of EPR pO2 hypoxic fractions. The ~90% control of low HF10 tumors argue that ½ ml subvolumes of tumors may be more sensitive to radiation and may need less radiation for high tumor control rates. PMID:23861469
Predictable SCR co-benefits for mercury control
Pritchard, S.
2009-01-15
A test program, performed in cooperation with Dominion Power and the Babcock and Wilcox Co., was executed at Dominion Power's Mount Storm power plant in Grant County, W. Va. The program was focused on both the selective catalytic reduction (SCR) catalyst capability to oxide mercury as well as the scrubber's capability to capture and retain the oxidized mercury. This article focuses on the SCR catalyst performance aspects. The Mount Storm site consists of three units totaling approximately 1,660 MW. All units are equipped with SCR systems for NOx control. A full-scale test to evaluate the effect of the SCR was performed on Unit 2, a 550 MWT-fired boiler firing a medium sulfur bituminous coal. This test program demonstrated that the presence of an SCR catalyst can significantly affect the mercury speciation profile. Observation showed that in the absence of an SCR catalyst, the extent of oxidation of element a mercury at the inlet of the flue gas desulfurization system was about 64%. The presence of a Cornertech SCR catalyst improved this oxidation to levels greater than 95% almost all of which was captured by the downstream wet FGD system. Cornertech's proprietary SCR Hg oxidation model was used to accurately predict the field results. 1 ref., 2 figs., 1 tab.
NASA Technical Reports Server (NTRS)
Kvaternik, Raymond G.; Juang, Jer-Nan; Bennett, Richard L.
2000-01-01
The Aeroelasticity Branch at NASA Langley Research Center has a long and substantive history of tiltrotor aeroelastic research. That research has included a broad range of experimental investigations in the Langley Transonic Dynamics Tunnel (TDT) using a variety of scale models and the development of essential analyses. Since 1994, the tiltrotor research program has been using a 1/5-scale, semispan aeroelastic model of the V-22 designed and built by Bell Helicopter Textron Inc. (BHTI) in 1981. That model has been refurbished to form a tiltrotor research testbed called the Wing and Rotor Aeroelastic Test System (WRATS) for use in the TDT. In collaboration with BHTI, studies under the current tiltrotor research program are focused on aeroelastic technology areas having the potential for enhancing the commercial and military viability of tiltrotor aircraft. Among the areas being addressed, considerable emphasis is being directed to the evaluation of modern adaptive multi-input multi- output (MIMO) control techniques for active stability augmentation and vibration control of tiltrotor aircraft. As part of this investigation, a predictive control technique known as Generalized Predictive Control (GPC) is being studied to assess its potential for actively controlling the swashplate of tiltrotor aircraft to enhance aeroelastic stability in both helicopter and airplane modes of flight. This paper summarizes the exploratory numerical and experimental studies that were conducted as part of that investigation.
NASA Astrophysics Data System (ADS)
Wang, Liuping; Gan, Lu
2013-08-01
Linear controllers with gain scheduling have been successfully used in the control of nonlinear systems for the past several decades. This paper proposes the design of gain scheduled continuous-time model predictive controller with constraints. Using induction machine as an illustrative example, the paper will show the four steps involved in the design of a gain scheduled predictive controller: (i) linearisation of a nonlinear plant according to operating conditions; (ii) the design of linear predictive controllers for the family of linear models; (iii) gain scheduled predictive control law that will optimise a multiple model objective function with constraints, which will also ensure smooth transitions (i.e. bumpless transfer) between the predictive controllers; (iv) experimental validation of the gain scheduled predictive control system with constraints.
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.
Predictive Poincaré control: A control theory for chaotic systems
NASA Astrophysics Data System (ADS)
Schweizer, Jörg; Kennedy, Michael Peter
1995-11-01
One of the most interesting features of chaotic systems is the large number of unstable orbits embedded in a chaotic attractor. In this work, we propose a global chaos-control technique called predictive Poincaré control (PPC) that permits stabilization of a predefined solution, using only small control pulses. We prove this result for a large class of n-dimensional chaotic systems. The predefined solution can be a periodic or nonperiodic oscillation, expressed by a periodic or nonperiodic symbolic sequence [S. Hayes, C. Grebogi, and E. Ott, Phys. Rev. Lett. 70, 3031 (1993)]. We apply the general PPC scheme to the well known Lorenz model and study its robustness with respect to parasitic effects.
Analysis, prediction and control of radio frequency interference with respect to DSN
NASA Technical Reports Server (NTRS)
Degroot, N. F.
1982-01-01
Susceptibility modeling, prediction of radio frequency interference from satellites, operational radio frequency interference control, and international regulations are considered. The existing satellite interference prediction program DSIP2 is emphasized. A summary status evaluation and recommendations for future work are given.
Haves, Phillip; Hencey, Brandon; Borrell, Francesco; Elliot, John; Ma, Yudong; Coffey, Brian; Bengea, Sorin; Wetter, Michael
2010-06-29
constrained and often determined by the chilled water return temperature (CHWR). The CHWR temperature is primarily comprised of warm water from the top of the TES tank. The CHWR temperature falls substantially as the thermocline approaches the top of the tank, which reduces the chiller loading. As a result, it has been determined that overcharging the TES tank can be detrimental to the chilled water plant efficiency. The resulting MPC policy differs from the current practice of fully charging the TES tank. A heuristic rule could possible avoid this problem without using predictive control. Similarly, the COP improvements from the change in CWS set-point were largely captured by a static set-point change by the operators. Further research is required to determine how much of the MPC savings could be garnered through simplified rules (based on the MPC study), with and without prediction.
Constraining Galileon inflation
Regan, Donough; Anderson, Gemma J.; Hull, Matthew; Seery, David E-mail: G.Anderson@sussex.ac.uk E-mail: D.Seery@sussex.ac.uk
2015-02-01
In this short paper, we present constraints on the Galileon inflationary model from the CMB bispectrum. We employ a principal-component analysis of the independent degrees of freedom constrained by data and apply this to the WMAP 9-year data to constrain the free parameters of the model. A simple Bayesian comparison establishes that support for the Galileon model from bispectrum data is at best weak.
NASA Technical Reports Server (NTRS)
Huang, J.; Karthikeyan, M.; Plawsky, J.; Wayner, P. C., Jr.
1999-01-01
The nonisothermal Constrained Vapor Bubble, CVB, is being studied to enhance the understanding of passive systems controlled by interfacial phenomena. The study is multifaceted: 1) it is a basic scientific study in interfacial phenomena, fluid physics and thermodynamics; 2) it is a basic study in thermal transport; and 3) it is a study of a heat exchanger. The research is synergistic in that CVB research requires a microgravity environment and the space program needs thermal control systems like the CVB. Ground based studies are being done as a precursor to flight experiment. The results demonstrate that experimental techniques for the direct measurement of the fundamental operating parameters (temperature, pressure, and interfacial curvature fields) have been developed. Fluid flow and change-of-phase heat transfer are a function of the temperature field and the vapor bubble shape, which can be measured using an Image Analyzing Interferometer. The CVB for a microgravity environment, has various thin film regions that are of both basic and applied interest. Generically, a CVB is formed by underfilling an evacuated enclosure with a liquid. Classification depends on shape and Bond number. The specific CVB discussed herein was formed in a fused silica cell with inside dimensions of 3x3x40 mm and, therefore, can be viewed as a large version of a micro heat pipe. Since the dimensions are relatively large for a passive system, most of the liquid flow occurs under a small capillary pressure difference. Therefore, we can classify the discussed system as a low capillary pressure system. The studies discussed herein were done in a 1-g environment (Bond Number = 3.6) to obtain experience to design a microgravity experiment for a future NASA flight where low capillary pressure systems should prove more useful. The flight experiment is tentatively scheduled for the year 2000. The SCR was passed on September 16, 1997. The RDR is tentatively scheduled for October, 1998.
Application of Sampling Based Model Predictive Control to an Autonomous Underwater Vehicle
2010-07-01
55 Application of Sampling Based Model Predictive Control to an Autonomous Underwater Vehicle Unmanned Underwater Vehicles (UUVs) can be utilized...the vehicle can feasibly traverse. As a result, Sampling- Based Model Predictive Control (SBMPC) is proposed to simultaneously generate control...inputs and system trajectories for an autonomous underwater vehicle (AUV). The algorithm combines the benefits of sampling- based motion planning with
Predictive motor control of sensory dynamics in auditory active sensing.
Morillon, Benjamin; Hackett, Troy A; Kajikawa, Yoshinao; Schroeder, Charles E
2015-04-01
Neuronal oscillations present potential physiological substrates for brain operations that require temporal prediction. We review this idea in the context of auditory perception. Using speech as an exemplar, we illustrate how hierarchically organized oscillations can be used to parse and encode complex input streams. We then consider the motor system as a major source of rhythms (temporal priors) in auditory processing, that act in concert with attention to sharpen sensory representations and link them across areas. We discuss the circuits that could mediate this audio-motor interaction, notably the potential role of the somatosensory system. Finally, we reposition temporal predictions in the context of internal models, discussing how they interact with feature-based or spatial predictions. We argue that complementary predictions interact synergistically according to the organizational principles of each sensory system, forming multidimensional filters crucial to perception.
Predictive Techniques for Spacecraft Cabin Air Quality Control
NASA Technical Reports Server (NTRS)
Perry, J. L.; Cromes, Scott D. (Technical Monitor)
2001-01-01
As assembly of the International Space Station (ISS) proceeds, predictive techniques are used to determine the best approach for handling a variety of cabin air quality challenges. These techniques use equipment offgassing data collected from each ISS module before flight to characterize the trace chemical contaminant load. Combined with crew metabolic loads, these data serve as input to a predictive model for assessing the capability of the onboard atmosphere revitalization systems to handle the overall trace contaminant load as station assembly progresses. The techniques for predicting in-flight air quality are summarized along with results from early ISS mission analyses. Results from groundbased analyses of in-flight air quality samples are compared to the predictions to demonstrate the technique's relative conservatism.
Stochastic Prediction and Feedback Control of Router Queue Size in a Virtual Network Environment
2014-09-18
STOCHASTIC PREDICTION AND FEEDBACK CONTROL OF ROUTER QUEUE SIZE IN A VIRTUAL NETWORK ENVIRONMENT THESIS Muflih Alqahtani, First...AFIT-ENG-T-14-S-10 STOCHASTIC PREDICTION AND FEEDBACK CONTROL OF ROUTER QUEUE SIZE IN A VIRTUAL NETWORK ENVIRONMENT THESIS Presented to the...UNLIMITED AFIT-ENG-T-14-S-10 STOCHASTIC PREDICTION AND FEEDBACK CONTROL OF ROUTER QUEUE SIZE IN A VIRTUAL NETWORK ENVIRONMENT Muflih Alqahtani
Shared developmental programme strongly constrains beak shape diversity in songbirds.
Fritz, Joerg A; Brancale, Joseph; Tokita, Masayoshi; Burns, Kevin J; Hawkins, M Brent; Abzhanov, Arhat; Brenner, Michael P
2014-04-16
The striking diversity of bird beak shapes is an outcome of natural selection, yet the relative importance of the limitations imposed by the process of beak development on generating such variation is unclear. Untangling these factors requires mapping developmental mechanisms over a phylogeny far exceeding model systems studied thus far. We address this issue with a comparative morphometric analysis of beak shape in a diverse group of songbirds. Here we show that the dynamics of the proliferative growth zone must follow restrictive rules to explain the observed variation, with beak diversity constrained to a three parameter family of shapes, parameterized by length, depth and the degree of shear. We experimentally verify these predictions by analysing cell proliferation in the developing embryonic beaks of the zebra finch. Our findings indicate that beak shape variability in many songbirds is strongly constrained by shared properties of the developmental programme controlling the growth zone.
NASA Technical Reports Server (NTRS)
Conway, Sheila R.
2006-01-01
Simple agent-based models may be useful for investigating air traffic control strategies as a precursory screening for more costly, higher fidelity simulation. Of concern is the ability of the models to capture the essence of the system and provide insight into system behavior in a timely manner and without breaking the bank. The method is put to the test with the development of a model to address situations where capacity is overburdened and potential for propagation of the resultant delay though later flights is possible via flight dependencies. The resultant model includes primitive representations of principal air traffic system attributes, namely system capacity, demand, airline schedules and strategy, and aircraft capability. It affords a venue to explore their interdependence in a time-dependent, dynamic system simulation. The scope of the research question and the carefully-chosen modeling fidelity did allow for the development of an agent-based model in short order. The model predicted non-linear behavior given certain initial conditions and system control strategies. Additionally, a combination of the model and dimensionless techniques borrowed from fluid systems was demonstrated that can predict the system s dynamic behavior across a wide range of parametric settings.
Prediction Models are Basis for Rational Air Quality Control
ERIC Educational Resources Information Center
Daniels, Anders; Bach, Wilfrid
1973-01-01
An air quality control scheme employing meteorological diffusion, time averaging and frequency, and cost-benefit models is discussed. The methods outlined provide a constant feedback system for air quality control. Flow charts and maps are included. (BL)
NASA Technical Reports Server (NTRS)
Maughmer, Mark D.; Ozoroski, L.; Ozoroski, T.; Straussfogel, D.
1990-01-01
Many types of hypersonic aircraft configurations are currently being studied for feasibility of future development. Since the control of the hypersonic configurations throughout the speed range has a major impact on acceptable designs, it must be considered in the conceptual design stage. The ability of the aerodynamic analysis methods contained in an industry standard conceptual design system, APAS II, to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds is considered. Predicted control forces and moments generated by various control effectors are compared with previously published wind tunnel and flight test data for three configurations: the North American X-15, the Space Shuttle Orbiter, and a hypersonic research airplane concept. Qualitative summaries of the results are given for each longitudinal force and moment and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage. Results for most lateral/directional control derivatives are acceptable for conceptual design purposes; however, predictions at supersonic Mach numbers for the change in yawing moment due to aileron deflection and the change in rolling moment due to rudder deflection are found to be unacceptable. Including shielding effects in the analysis is shown to have little effect on lift and pitching moment predictions while improving drag predictions.
Maternal Illusory Control Predicts Socialization Strategies and Toddler Compliance.
ERIC Educational Resources Information Center
Donovan, Wilberta L.; Leavitt, Lewis A.; Walsh, Reghan O.
2000-01-01
Examined the relation between mothers' perception of their capacity for controlling infant crying and a later measure of compliance with parent requests by toddlers. Found that toddlers of mothers in the low and high illusion of control (overestimating of maternal control) groups were more likely to be highly defiant than were toddlers of mothers…
Dynamical Epidemic Suppression Using Stochastic Prediction and Control
2004-10-28
reduce the rate of input of susceptibles. By using the PDF flux, we are able to distinguish regions used in other chaos control schemes that are...use this information in a control algo- stochastic chaos control methods that account specifically for rithm to prevent bursting dynamics (that is, to
Hurricane prediction and control: impact of large computers.
Hammond, A L
1973-08-17
This is the third is a continuing series of articles on natural disasters, their prediction and mnodification, and progress in understanding the physical bases of these phenomena. Two earlier articles (Science, 25 May, p. 851, and 1 June, p. 940) reported advances in earthquake prediction. Hurricanes are the subject here. Generally less devastating than major earthquakes-although a single hurricane in 1970 killed an estimated 200,000 persons in Bangladesh-these storms are still the most destructive of all atmospheric phenomena. A recent report of the National Academy of Sciences (see box) recommends that efforts to modify hurricanes and other severe storms become a national goal.
Adaptive Data-based Predictive Control for Short Take-off and Landing (STOL) Aircraft
NASA Technical Reports Server (NTRS)
Barlow, Jonathan Spencer; Acosta, Diana Michelle; Phan, Minh Q.
2010-01-01
Data-based Predictive Control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. The characteristics of adaptive data-based predictive control are particularly appropriate for the control of nonlinear and time-varying systems, such as Short Take-off and Landing (STOL) aircraft. STOL is a capability of interest to NASA because conceptual Cruise Efficient Short Take-off and Landing (CESTOL) transport aircraft offer the ability to reduce congestion in the terminal area by utilizing existing shorter runways at airports, as well as to lower community noise by flying steep approach and climb-out patterns that reduce the noise footprint of the aircraft. In this study, adaptive data-based predictive control is implemented as an integrated flight-propulsion controller for the outer-loop control of a CESTOL-type aircraft. Results show that the controller successfully tracks velocity while attempting to maintain a constant flight path angle, using longitudinal command, thrust and flap setting as the control inputs.
Kunz, Martin; Liddle, Andrew R.; Parkinson, David; Gao Changjun
2009-10-15
Cosmological observations are normally fit under the assumption that the dark sector can be decomposed into dark matter and dark energy components. However, as long as the probes remain purely gravitational, there is no unique decomposition and observations can only constrain a single dark fluid; this is known as the dark degeneracy. We use observations to directly constrain this dark fluid in a model-independent way, demonstrating, in particular, that the data cannot be fit by a dark fluid with a single constant equation of state. Parametrizing the dark fluid equation of state by a variety of polynomials in the scale factor a, we use current kinematical data to constrain the parameters. While the simplest interpretation of the dark fluid remains that it is comprised of separate dark matter and cosmological constant contributions, our results cover other model types including unified dark energy/matter scenarios.
Zhang, Jianming
2017-03-01
An improved proportional-integral-derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers.
Predictive Display Design for a Two-Axis Control Task.
1982-08-01
elementary processes (Sheridan and Rouse 1971, Rouse 1973, Van Heusden 1977). The problem is particularly acute in flight systems and missile guidance...26. VAN HEUSDEN , A R (1977) Models for human prediction of process variables. In Proceedings of Symposium on Human Operators and Simulation
Gaining Control and Predictability of Software-Intensive Systems Development and Sustainment
2015-02-04
this would be a major design driver for the software architect (Naegle & Petross, 2007). Primary Software Acquisition Problem Areas Addressed The...control and produces significantly more predictability in the program management realm. The research conclusions and recommendations are designed to...provide more control and predictability to software-intensive systems development. Due to the TOC and architectural design focus, system sustainability
Inhibitory Control Predicts Language Switching Performance in Trilingual Speech Production
ERIC Educational Resources Information Center
Linck, Jared A.; Schwieter, John W.; Sunderman, Gretchen
2012-01-01
This study investigated the role of domain-general inhibitory control in trilingual speech production. Taking an individual differences approach, we examined the relationship between performance on a non-linguistic measure of inhibitory control (the Simon task) and a multilingual language switching task for a group of fifty-six native English (L1)…
Predicting Changes in Older Adults' Interpersonal Control Strivings
ERIC Educational Resources Information Center
Sorkin, Dara H.; Rook, Karen S.; Heckhausen, Jutta; Billimek, John
2009-01-01
People vary in the importance they ascribe to, and efforts they invest in, maintaining positive relationships with others. Research has linked such variation in interpersonal control strivings to the quality of social exchanges experienced, but little work has examined the predictors of interpersonal control strivings. Given the importance of…
Active Constrained Layer Damping of Thin Cylindrical Shells
NASA Astrophysics Data System (ADS)
RAY, M. C.; OH, J.; BAZ, A.
2001-03-01
The effectiveness of the active constrained layer damping (ACLD) treatments in enhancing the damping characteristics of thin cylindrical shells is presented. A finite element model (FEM) is developed to describe the dynamic interaction between the shells and the ACLD treatments. Experiments are performed to verify the numerical predictions. The obtained results suggest the potential of the ACLD treatments in controlling the vibration of cylindrical shells which constitute the major building block of many critical structures such as cabins of aircrafts, hulls of submarines and bodies of rockets and missiles.
ERIC Educational Resources Information Center
Kopystynska, Olena; Spinrad, Tracy L.; Seay, Danielle M.; Eisenberg, Nancy
2016-01-01
The goal of this work was to examine the complex interrelation of mothers' early gentle control and sensitivity in predicting children's effortful control (EC) and academic functioning. Maternal gentle control, maternal sensitivity, and children's EC were measured when children were 18, 30, and 42 months of age (T1, T2, and T3, respectively), and…
A Roll, Fin, and Fin Controller Prediction Computer Program.
1980-06-01
Reference 1, and specific details of this improve- ment will be published in a future report currently under preparation by Cox. *A complete listing of...effects. CONCLUDING RMARKS This report provides a user’s guide to FINCON, a roll, fin, fin con- troller prediction computer program. No attempt to...180. FLOATIMUOIINU) ROLL I# OA14PUINUI a OUCIIV,1 ROLL 19 IF ( ITEPATE .EQ.0) O T3 98’ ROLL 106 Ise NTIY 0 ROLL lot To 0.0 ROLL lit s0 NTRY - NTYRY I
Noise prediction and control of Pudong International Airport expansion project.
Lei, Bin; Yang, Xin; Yang, Jianguo
2009-04-01
The Environmental Impact Assessment (EIA) process of the third runway building project of Pudong International Airport is briefly introduced in the paper. The basic principle, the features, and the operation steps of newly imported FAA's Integrated Noise Model (INM) are discussed for evaluating the aircraft noise impacts. The prediction of the aircraft noise and the countermeasures for the noise mitigation are developed, which includes the reasonable runway location, the optimized land use, the selection of low noise aircrafts, the Fly Quit Program, the relocation of sensitive receptors and the noise insulation of sensitive buildings. Finally, the expansion project is justified and its feasibility is confirmed.
NASA Technical Reports Server (NTRS)
Cull, R. C.; Eltimsahy, A. H.
1983-01-01
The present investigation is concerned with the formulation of energy management strategies for stand-alone photovoltaic (PV) systems, taking into account a basic control algorithm for a possible predictive, (and adaptive) controller. The control system controls the flow of energy in the system according to the amount of energy available, and predicts the appropriate control set-points based on the energy (insolation) available by using an appropriate system model. Aspects of adaptation to the conditions of the system are also considered. Attention is given to a statistical analysis technique, the analysis inputs, the analysis procedure, and details regarding the basic control algorithm.
Predictive control of SOFC based on a GA-RBF neural network model
NASA Astrophysics Data System (ADS)
Wu, Xiao-Juan; Zhu, Xin-Jian; Cao, Guang-Yi; Tu, Heng-Yong
Transients in a load have a significant impact on the performance and durability of a solid oxide fuel cell (SOFC) system. One of the main reasons is that the fuel utilization changes drastically due to the load change. Therefore, in order to guarantee the fuel utilization to operate within a safe range, a nonlinear model predictive control (MPC) method is proposed to control the stack terminal voltage as a proper constant in this paper. The nonlinear predictive controller is based on an improved radial basis function (RBF) neural network identification model. During the process of modeling, the genetic algorithm (GA) is used to optimize the parameters of RBF neural networks. And then a nonlinear predictive control algorithm is applied to track the voltage of the SOFC. Compared with the constant fuel utilization control method, the simulation results show that the nonlinear predictive control algorithm based on the GA-RBF model performs much better.
Constrained density functional for noncollinear magnetism
NASA Astrophysics Data System (ADS)
Ma, Pui-Wai; Dudarev, S. L.
2015-02-01
Energies of arbitrary small- and large-angle noncollinear excited magnetic configurations are computed using a highly accurate constrained density functional theory approach. Numerical convergence and accuracy are controlled by the choice of Lagrange multipliers λI entering the constraining conditions. The penalty part Ep of the constrained energy functional at its minimum is shown to be inversely proportional to λI, enabling a simple, robust, and accurate iterative procedure to be followed to find a convergent solution. The method is implemented as a part of ab initio vasp package, and applied to the investigation of noncollinear B2-like and <001 > double-layer antiferromagnetic configurations of bcc iron, Fe2 dimer, and amorphous iron. Forces acting on atoms depend on the orientations of magnetic moments, and the proposed approach enables constrained self-consistent noncollinear magnetic and structural relaxation of large atomic systems to be carried out.
BICEP2 constrains composite inflation
NASA Astrophysics Data System (ADS)
Channuie, Phongpichit
2014-07-01
In light of BICEP2, we re-examine single field inflationary models in which the inflation is a composite state stemming from various four-dimensional strongly coupled theories. We study in the Einstein frame a set of cosmological parameters, the primordial spectral index ns and tensor-to-scalar ratio r, predicted by such models. We confront the predicted results with the joint Planck data, and with the recent BICEP2 data. We constrain the number of e-foldings for composite models of inflation in order to obtain a successful inflation. We find that the minimal composite inflationary model is fully consistent with the Planck data. However it is in tension with the recent BICEP2 data. The observables predicted by the glueball inflationary model can be consistent with both Planck and BICEP2 contours if a suitable number of e-foldings are chosen. Surprisingly, the super Yang-Mills inflationary prediction is significantly consistent with the Planck and BICEP2 observations.
Haase, Claudia M; Poulin, Michael J; Heckhausen, Jutta
2012-08-01
What motivates individuals to invest time and effort and overcome obstacles (i.e., strive for primary control) when pursuing important goals? We propose that positive affect predicts primary control striving for career and educational goals, and we explore the mediating role of control beliefs. In Study 1, positive affect predicted primary control striving for career goals in a two-wave longitudinal study of a U.S. sample. In Study 2, positive affect predicted primary control striving for career and educational goals and objective career outcomes in a six-wave longitudinal study of a German sample. Control beliefs partially mediated the longitudinal associations with primary control striving. Thus, when individuals experience positive affect, they become more motivated to invest time and effort, and overcome obstacles when pursuing their goals, in part because they believe they have more control over attaining their goals.
Meditation-induced states predict attentional control over time.
Colzato, Lorenza S; Sellaro, Roberta; Samara, Iliana; Baas, Matthijs; Hommel, Bernhard
2015-12-01
Meditation is becoming an increasingly popular topic for scientific research and various effects of extensive meditation practice (ranging from weeks to several years) on cognitive processes have been demonstrated. Here we show that extensive practice may not be necessary to achieve those effects. Healthy adult non-meditators underwent a brief single session of either focused attention meditation (FAM), which is assumed to increase top-down control, or open monitoring meditation (OMM), which is assumed to weaken top-down control, before performing an Attentional Blink (AB) task - which assesses the efficiency of allocating attention over time. The size of the AB was considerably smaller after OMM than after FAM, which suggests that engaging in meditation immediately creates a cognitive-control state that has a specific impact on how people allocate their attention over time.
Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking
2015-07-01
feedback control to generate desired lateral and angular velocities to compensate for vehicle slip rates. Finally, they use the robot’s inverse dynamics to...Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking Chris J. Ostafew Institute for Aerospace Studies...paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithm to achieve high-performance path tracking in challenging off-road
Adaptive and predictive control of a simulated robot arm.
Tolu, Silvia; Vanegas, Mauricio; Garrido, Jesús A; Luque, Niceto R; Ros, Eduardo
2013-06-01
In this work, a basic cerebellar neural layer and a machine learning engine are embedded in a recurrent loop which avoids dealing with the motor error or distal error problem. The presented approach learns the motor control based on available sensor error estimates (position, velocity, and acceleration) without explicitly knowing the motor errors. The paper focuses on how to decompose the input into different components in order to facilitate the learning process using an automatic incremental learning model (locally weighted projection regression (LWPR) algorithm). LWPR incrementally learns the forward model of the robot arm and provides the cerebellar module with optimal pre-processed signals. We present a recurrent adaptive control architecture in which an adaptive feedback (AF) controller guarantees a precise, compliant, and stable control during the manipulation of objects. Therefore, this approach efficiently integrates a bio-inspired module (cerebellar circuitry) with a machine learning component (LWPR). The cerebellar-LWPR synergy makes the robot adaptable to changing conditions. We evaluate how this scheme scales for robot-arms of a high number of degrees of freedom (DOFs) using a simulated model of a robot arm of the new generation of light weight robots (LWRs).
Integration of Predictive Routing Information with Dynamic Traffic Signal Control
1994-05-01
vehicles without the on-board guidance aid (Harris, S., Rabone , A., et.al., 1992). The simulation developed was called ROute GUidance Simulation (ROGUS...Florida. Harris, S., Rabone , A., et.al. 1992. ROGUS: A Simulation of Dynamic Route Guidance Systems. Traffic Engineering and Control(33)327-329
Cognitive Control Predicts Academic Achievement in Kindergarten Children
ERIC Educational Resources Information Center
Coldren, Jeffrey T.
2013-01-01
Children's ability to shift behavior in response to changing environmental demands is critical for successful intellectual functioning. While the processes underlying the development of cognitive control have been thoroughly investigated, its functioning in an ecologically relevant setting such as school is less well understood. Given the alarming…
New technologies in predicting, preventing and controlling emerging infectious diseases.
Christaki, Eirini
2015-01-01
Surveillance of emerging infectious diseases is vital for the early identification of public health threats. Emergence of novel infections is linked to human factors such as population density, travel and trade and ecological factors like climate change and agricultural practices. A wealth of new technologies is becoming increasingly available for the rapid molecular identification of pathogens but also for the more accurate monitoring of infectious disease activity. Web-based surveillance tools and epidemic intelligence methods, used by all major public health institutions, are intended to facilitate risk assessment and timely outbreak detection. In this review, we present new methods for regional and global infectious disease surveillance and advances in epidemic modeling aimed to predict and prevent future infectious diseases threats.
New technologies in predicting, preventing and controlling emerging infectious diseases
Christaki, Eirini
2015-01-01
Surveillance of emerging infectious diseases is vital for the early identification of public health threats. Emergence of novel infections is linked to human factors such as population density, travel and trade and ecological factors like climate change and agricultural practices. A wealth of new technologies is becoming increasingly available for the rapid molecular identification of pathogens but also for the more accurate monitoring of infectious disease activity. Web-based surveillance tools and epidemic intelligence methods, used by all major public health institutions, are intended to facilitate risk assessment and timely outbreak detection. In this review, we present new methods for regional and global infectious disease surveillance and advances in epidemic modeling aimed to predict and prevent future infectious diseases threats. PMID:26068569
Intermittent redesign of continuous controllers
NASA Astrophysics Data System (ADS)
Gawthrop, Peter J.; Wang, Liuping
2010-08-01
The reverse-engineering idea developed by Maciejowski in the context of model-based predictive control is applied to the redesign of continuous-time compensators as intermittent controllers. Not only does this give a way of designing constrained input and state versions of continuous-time compensators but also provides a method for turning continuous-time compensators into event-driven versions. The procedure is illustrated by three examples: an event-driven PID controller relevant to the human balance control problem, a constrained version of the classical mechanical vibration absorber of den Hartog and an event driven and constrained vibration absorber.
Prediction and control of slender-wing rock
NASA Technical Reports Server (NTRS)
Kandil, Osama A.; Salman, Ahmed A.
1992-01-01
The unsteady Euler equations and the Euler equations of rigid-body dynamics, both written in the moving frame of reference, are sequentially solved to simulate the limit-cycle rock motion of slender delta wings. The governing equations of the fluid flow and the dynamics of the present multidisciplinary problem are solved using an implicit, approximately-factored, central-difference-like, finite-volume scheme and a four-stage Runge-Kutta scheme, respectively. For the control of wing-rock motion, leading-edge flaps are forced to oscillate anti-symmetrically at prescribed frequency and amplitude, which are tuned in order to suppress the rock motion. Since the computational grid deforms due to the leading-edge flaps motion, the grid is dynamically deformed using the Navier-displacement equations. Computational applications cover locally-conical and three-dimensional solutions for the wing-rock simulation and its control.
Constrained noninformative priors
Atwood, C.L.
1994-10-01
The Jeffreys noninformative prior distribution for a single unknown parameter is the distribution corresponding to a uniform distribution in the transformed model where the unknown parameter is approximately a location parameter. To obtain a prior distribution with a specified mean but with diffusion reflecting great uncertainty, a natural generalization of the noninformative prior is the distribution corresponding to the constrained maximum entropy distribution in the transformed model. Examples are given.
Motor prediction in Brain-Computer Interfaces for controlling mobile robots.
Geng, Tao; Gan, John Q
2008-01-01
EEG-based Brain-Computer Interface (BCI) can be regarded as a new channel for motor control except that it does not involve muscles. Normal neuromuscular motor control has two fundamental components: (1) to control the body, and (2) to predict the consequences of the control command, which is called motor prediction. In this study, after training with a specially designed BCI paradigm based on motor imagery, two subjects learnt to predict the time course of some features of the EEG signals. It is shown that, with this newly-obtained motor prediction skill, subjects can use motor imagery of feet to directly control a mobile robot to avoid obstacles and reach a small target in a time-critical scenario.
A Novel Method to Predict Circulation Control Noise
2016-03-17
OASPL computed from 1 to 16 kHz. . . . . . . . 162 5 LIST OF FIGURES 1.1 A sample circulation control airfoil...Doppler effect included and (c),(d) Doppler effect removed. . .......... . . 82 3.32 (a) Spectral and (b) time series data comparison between Curle ’s...analogy employing the DNS full forces and DNS pressure at r = 75, () = 80°. 83 3.33 (a) Spectral and (b) time series data comparison between Curle’s
A computerized test of self-control predicts classroom behavior.
Hoerger, Marguerite L; Mace, F Charles
2006-01-01
We assessed choices on a computerized test of self-control (CTSC) for a group of children with features of attention deficit hyperactivity disorder (ADHD) and a group of controls. Thirty boys participated in the study. Fifteen of the children had been rated by their parents as hyperactive and inattentive, and 15 were age- and gender-matched controls in the same classroom. The children were observed in the classroom for three consecutive mornings, and data were collected on their activity levels and attention. The CTSC consisted of two tasks. In the delay condition, children chose to receive three rewards after a delay of 60 s or one reward immediately. In the task-difficulty condition, the children chose to complete a difficult math problem and receive three rewards or complete an easier problem for one reward. The children with ADHD features made more impulsive choices than their peers during both conditions, and these choices correlated with measures of their activity and attention in the classroom.
Duan, Yingyao; Zuo, Xin; Liu, Jianwei
2016-01-01
Networked predictive control system (NPCS) has been proposed to address random delays and data dropouts in networked control systems (NCSs). A remaining challenge of this approach is that the controller has uncertain information about the actual control inputs, which leads to the predicted control input errors. The main contribution of this paper is to develop an explicit mechanism running in the distributed network nodes asynchronously, which enables the controller node to keep informed of the states of the actuator node without a priori knowledge about the network. Based on this mechanism, a novel proactive compensation strategy is proposed to develop asynchronous update based networked predictive control system (AUBNPCS). The stability criterion of AUBNPCS is derived analytically. A simulation experiment based on Truetime demonstrates the effectiveness of the scheme.
Nonlinear model identification and adaptive model predictive control using neural networks.
Akpan, Vincent A; Hassapis, George D
2011-04-01
This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time.
Puig, V; Cembrano, G; Romera, J; Quevedo, J; Aznar, B; Ramón, G; Cabot, J
2009-01-01
This paper deals with the global control of the Riera Blanca catchment in the Barcelona sewer network using a predictive optimal control approach. This catchment has been modelled using a conceptual modelling approach based on decomposing the catchments in subcatchments and representing them as virtual tanks. This conceptual modelling approach allows real-time model calibration and control of the sewer network. The global control problem of the Riera Blanca catchment is solved using a optimal/predictive control algorithm. To implement the predictive optimal control of the Riera Blanca catchment, a software tool named CORAL is used. The on-line control is simulated by interfacing CORAL with a high fidelity simulator of sewer networks (MOUSE). CORAL interchanges readings from the limnimeters and gate commands with MOUSE as if it was connected with the real SCADA system. Finally, the global control results obtained using the predictive optimal control are presented and compared against the results obtained using current local control system. The results obtained using the global control are very satisfactory compared to those obtained using the local control.
The Minimal Control Principle Predicts Strategy Shifts in the Abstract Decision Making Task
ERIC Educational Resources Information Center
Taatgen, Niels A.
2011-01-01
The minimal control principle (Taatgen, 2007) predicts that people strive for problem-solving strategies that require as few internal control states as possible. In an experiment with the Abstract Decision Making task (ADM task; Joslyn & Hunt, 1998) the reward structure was manipulated to make either a low-control strategy or a high-strategy…
Order-constrained linear optimization.
Tidwell, Joe W; Dougherty, Michael R; Chrabaszcz, Jeffrey S; Thomas, Rick P
2017-02-27
Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. In this paper, we develop an order-constrained linear least-squares (OCLO) optimization algorithm that maximizes the linear least-squares fit to the data conditional on maximizing the ordinal fit based on Kendall's τ. The algorithm builds on the maximum rank correlation estimator (Han, 1987, Journal of Econometrics, 35, 303) and the general monotone model (Dougherty & Thomas, 2012, Psychological Review, 119, 321). Analyses of simulated data indicate that when modelling data that adhere to the assumptions of ordinary least squares, OCLO shows minimal bias, little increase in variance, and almost no loss in out-of-sample predictive accuracy. In contrast, under conditions in which data include a small number of extreme scores (fat-tailed distributions), OCLO shows less bias and variance, and substantially better out-of-sample predictive accuracy, even when the outliers are removed. We show that the advantages of OCLO over ordinary least squares in predicting new observations hold across a variety of scenarios in which researchers must decide to retain or eliminate extreme scores when fitting data.
A predictive controller based on transient simulations for controlling a power plant
NASA Astrophysics Data System (ADS)
Svingen, B.
2016-11-01
A predictive governor based on an embedded, online transient simulation was commissioned at Tonstad power plant in Norway in December 2014. This governor controls each individual turbine governor by feeding them modified setpoints. Tonstad power plant consists of 4 × 160 MW + 1 × 320 MW high head Francis turbines. With a yearly production of 3888 GWh, it is the largest in Norway. The plant is a typical high head Norwegian plant with very long tunnels and correspondingly active dynamic behaviour. This new governor system continuously simulates the entire plant, and appropriate actions are taken automatically by special algorithms. The simulations are based on the method of characteristics (MOC). The governing system has been in full operational mode since December 19 2014. The testing period also included special acceptance tests to be able to deliver FRR, both on the Nordic grid and on DC cable to Denmark. Although in full operational mode, this system is still a prototype under constant development. It shows a new way of using transient analysis that may become increasingly important in the future with added power from un-regulated sources such as wind, solar and bio.
Model analysis of remotely controlled rendezvous and docking with display prediction
NASA Technical Reports Server (NTRS)
Milgram, P.; Wewerinke, P. H.
1986-01-01
Manual control of rendezvous and docking (RVD) of two spacecraft in low earth orbit by a remote human operator is discussed. Experimental evidence has shown that control performance degradation for large transmission delays (between spacecraft and operations control center) can be substantially improved by the introduction of predictor displays. An intial Optimal Control Model (OCM) analysis of RVD translational and rotational perturbation control was performed, with emphasis placed on the predictive capabilities of the combined Kalman estimator/optimal predictor with respect to control performance, for a range of time delays, motor noise levels and tracking axes. OCM predictions are then used as a reference for comparing tracking performance with a simple predictor display, as well as with no display prediction at all. Use is made here of an imperfect internal model formulation, whereby it is assumed that the human operator has no knowledge of the system transmission delay.
Jet Engine Noise Generation, Prediction and Control. Chapter 86
NASA Technical Reports Server (NTRS)
Huff, Dennis L.; Envia, Edmane
2004-01-01
. An example of this type of engine is shown in Figure IC, which is a schematic of the Honeywell T55 engine that powers the CH-47 Chinook helicopter. Since the noise from the propellers or helicopter rotors is usually dominant for turbo-shaft engines, less attention has been paid to these engines in so far as community noise considerations are concerned. This chapter will concentrate mostly on turbofan engine noise and will highlight common methods for their noise prediction and reduction.
Prediction and Control of Vortex Dominated and Vortex-wake Flows
NASA Technical Reports Server (NTRS)
Kandil, Osama
1996-01-01
This report describes the activities and accomplishments under this research grant, including a list of publications and dissertations, produced in the field of prediction and control of vortex dominated and vortex wake flows.
Well-being and control in older persons: the prediction of well-being from control measures.
Smits, C H; Deeg, D J; Bosscher, R J
1995-01-01
The interrelation of six facets of control and their ability to predict well-being in older persons were studied in an age and gender stratified community sample aged fifty-five to eighty-nine. An extended conceptual framework of control facets is introduced including "established" facets, such as mastery, self-efficacy and internal health locus of control and "new" control facets such as neuroticism, social inadequacy, and sense of coherence. An interview and a postal questionnaire included measures of the control facets and the Affect Balance Scale. Correlations between control measures were mostly modest. Negative affect was predicted by neuroticism and sense of coherence. Tendencies of independent association of mastery with global well-being and of social inadequacy with positive affect were established.
Auxiliary particle filter-model predictive control of the vacuum arc remelting process
NASA Astrophysics Data System (ADS)
Lopez, F.; Beaman, J.; Williamson, R.
2016-07-01
Solidification control is required for the suppression of segregation defects in vacuum arc remelting of superalloys. In recent years, process controllers for the VAR process have been proposed based on linear models, which are known to be inaccurate in highly-dynamic conditions, e.g. start-up, hot-top and melt rate perturbations. A novel controller is proposed using auxiliary particle filter-model predictive control based on a nonlinear stochastic model. The auxiliary particle filter approximates the probability of the state, which is fed to a model predictive controller that returns an optimal control signal. For simplicity, the estimation and control problems are solved using Sequential Monte Carlo (SMC) methods. The validity of this approach is verified for a 430 mm (17 in) diameter Alloy 718 electrode melted into a 510 mm (20 in) diameter ingot. Simulation shows a more accurate and smoother performance than the one obtained with an earlier version of the controller.
The role of action prediction and inhibitory control for joint action coordination in toddlers.
Meyer, M; Bekkering, H; Haartsen, R; Stapel, J C; Hunnius, S
2015-11-01
From early in life, young children eagerly engage in social interactions. Yet, they still have difficulties in performing well-coordinated joint actions with others. Adult literature suggests that two processes are important for smooth joint action coordination: action prediction and inhibitory control. The aim of the current study was to disentangle the potential role of these processes in the early development of joint action coordination. Using a simple turn-taking game, we assessed 2½-year-old toddlers' joint action coordination, focusing on timing variability and turn-taking accuracy. In two additional tasks, we examined their action prediction capabilities with an eye-tracking paradigm and examined their inhibitory control capabilities with a classic executive functioning task (gift delay task). We found that individual differences in action prediction and inhibitory action control were distinctly related to the two aspects of joint action coordination. Toddlers who showed more precision in their action predictions were less variable in their action timing during the joint play. Furthermore, toddlers who showed more inhibitory control in an individual context were more accurate in their turn-taking performance during the joint action. On the other hand, no relation between timing variability and inhibitory control or between turn-taking accuracy and action prediction was found. The current results highlight the distinct role of action prediction and inhibitory action control for the quality of joint action coordination in toddlers. Underlying neurocognitive mechanisms and implications for processes involved in joint action coordination in general are discussed.
Monadi, Mahmoud; Firouzjahi, Alireza; Hosseini, Amin; Javadian, Yahya; Sharbatdaran, Majid; Heidari, Behzad
2016-01-01
Background: Increased serum high sensitive C-reactive protein (hs-CRP) in asthma and its association with disease severity has been investigated in many studies. This study aimed to determine serum hs-CRP status in asthma versus healthy controls and to examine its ability in predicting asthma control. Methods: Serum CRP was measured by ELISA method using a high sensitive CRP kit. Severity of asthma was determined using Asthma Control Test. Spearman and chi square tests were used for association and correlation respectively. The predictive ability was determined by receiver operating characteristics (ROC) analysis. Accuracy was determined by determination of area under the ROC curve (AUC). Results: A total of 120 patients and 115 controls were studied. Median serum hs-CRP in asthma was higher than control (P=0.001. In well controlled asthma the hs-CRP decreased significantly compared with poorly controlled (P=0.024) but still was higher than control (P=0.017). Serum hs-CRP at cutoff level of 1.45 mg/L differentiated the patients and controls with accuracy of 63.5 % (AUC= 0.635±0.037, P=0.001). Serum hs-CRP ≤ 2.15 mg/L predicted well controlled asthma with accuracy of 62.5% (AUC= 0.625±0.056, p=0.025). After adjusting for age, sex, weight and smoking, there was an independent association between serum hs-CRP >1.45 mg/L and asthma by adjusted OR=2.49, p=0.018). Conclusion: These findings indicate that serum hs-CRP in asthma is higher than healthy control and increases with severity of asthma and decreases with. Thus, serum hs-CRP measurement can be helpful in predicting asthma control and treatment response. PMID:26958331
NASA Astrophysics Data System (ADS)
Jin, N.; Yang, F.; Shang, S. Y.; Tao, T.; Liu, J. S.
2016-08-01
According to the limitations of the LVRT technology of traditional photovoltaic inverter existed, this paper proposes a low voltage ride through (LVRT) control method based on model current predictive control (MCPC). This method can effectively improve the photovoltaic inverter output characteristics and response speed. The MCPC method of photovoltaic grid-connected inverter designed, the sum of the absolute value of the predictive current and the given current error is adopted as the cost function with the model predictive control method. According to the MCPC, the optimal space voltage vector is selected. Photovoltaic inverter has achieved automatically switches of priority active or reactive power control of two control modes according to the different operating states, which effectively improve the inverter capability of LVRT. The simulation and experimental results proves that the proposed method is correct and effective.
NASA Technical Reports Server (NTRS)
Mercer, Joey S.; Bienert, Nancy; Gomez, Ashley; Hunt, Sarah; Kraut, Joshua; Martin, Lynne; Morey, Susan; Green, Steven M.; Prevot, Thomas; Wu, Minghong G.
2013-01-01
A Human-In-The-Loop air traffic control simulation investigated the impact of uncertainties in trajectory predictions on NextGen Trajectory-Based Operations concepts, seeking to understand when the automation would become unacceptable to controllers or when performance targets could no longer be met. Retired air traffic controllers staffed two en route transition sectors, delivering arrival traffic to the northwest corner-post of Atlanta approach control under time-based metering operations. Using trajectory-based decision-support tools, the participants worked the traffic under varying levels of wind forecast error and aircraft performance model error, impacting the ground automations ability to make accurate predictions. Results suggest that the controllers were able to maintain high levels of performance, despite even the highest levels of trajectory prediction errors.
Is It Really Self-Control? Examining the Predictive Power of the Delay of Gratification Task
Duckworth, Angela L.; Tsukayama, Eli; Kirby, Teri A.
2013-01-01
This investigation tests whether the predictive power of the delay of gratification task (colloquially known as the “marshmallow test”) derives from its assessment of self-control or of theoretically unrelated traits. Among 56 school-age children in Study 1, delay time was associated with concurrent teacher ratings of self-control and Big Five conscientiousness—but not with other personality traits, intelligence, or reward-related impulses. Likewise, among 966 preschool children in Study 2, delay time was consistently associated with concurrent parent and caregiver ratings of self-control but not with reward-related impulses. While delay time in Study 2 was also related to concurrently measured intelligence, predictive relations with academic, health, and social outcomes in adolescence were more consistently explained by ratings of effortful control. Collectively, these findings suggest that delay task performance may be influenced by extraneous traits, but its predictive power derives primarily from its assessment of self-control. PMID:23813422
Is it really self-control? Examining the predictive power of the delay of gratification task.
Duckworth, Angela L; Tsukayama, Eli; Kirby, Teri A
2013-07-01
This investigation tests whether the predictive power of the delay of gratification task (colloquially known as the "marshmallow test") derives from its assessment of self-control or of theoretically unrelated traits. Among 56 school-age children in Study 1, delay time was associated with concurrent teacher ratings of self-control and Big Five conscientiousness-but not with other personality traits, intelligence, or reward-related impulses. Likewise, among 966 preschool children in Study 2, delay time was consistently associated with concurrent parent and caregiver ratings of self-control but not with reward-related impulses. While delay time in Study 2 was also related to concurrently measured intelligence, predictive relations with academic, health, and social outcomes in adolescence were more consistently explained by ratings of effortful control. Collectively, these findings suggest that delay task performance may be influenced by extraneous traits, but its predictive power derives primarily from its assessment of self-control.
Crocker, Justin; Ilsley, Garth R; Stern, David L
2016-03-01
Genes are regulated by transcription factors that bind to regions of genomic DNA called enhancers. Considerable effort is focused on identifying transcription factor binding sites, with the goal of predicting gene expression from DNA sequence. Despite this effort, general, predictive models of enhancer function are currently lacking. Here we combine quantitative models of enhancer function with manipulations using engineered transcription factors to examine the extent to which enhancer function can be controlled in a quantitatively predictable manner. Our models, which incorporate few free parameters, can accurately predict the contributions of ectopic transcription factor inputs. These models allow the predictable 'tuning' of enhancers, providing a framework for the quantitative control of enhancers with engineered transcription factors.
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.
Kumar, Aditya; Shi, Ruijie; Kumar, Rajeeva; Dokucu, Mustafa
2013-04-09
Control system and method for controlling an integrated gasification combined cycle (IGCC) plant are provided. The system may include a controller coupled to a dynamic model of the plant to process a prediction of plant performance and determine a control strategy for the IGCC plant over a time horizon subject to plant constraints. The control strategy may include control functionality to meet a tracking objective and control functionality to meet an optimization objective. The control strategy may be configured to prioritize the tracking objective over the optimization objective based on a coordinate transformation, such as an orthogonal or quasi-orthogonal projection. A plurality of plant control knobs may be set in accordance with the control strategy to generate a sequence of coordinated multivariable control inputs to meet the tracking objective and the optimization objective subject to the prioritization resulting from the coordinate transformation.
Controllability Modulates the Neural Response to Predictable but not Unpredictable Threat in Humans
Wood, Kimberly H.; Wheelock, Muriah D.; Shumen, Joshua R.; Bowen, Kenton H.; Ver Hoef, Lawrence W.; Knight, David C.
2015-01-01
Stress resilience is mediated, in part, by our ability to predict and control threats within our environment. Therefore, determining the neural mechanisms that regulate the emotional response to predictable and controllable threat may provide important new insight into the processes that mediate resilience to emotional dysfunction and guide the future development of interventions for anxiety disorders. To better understand the effect of predictability and controllability on threat-related brain activity in humans, two groups of healthy volunteers participated in a yoked Pavlovian fear conditioning study during functional magnetic resonance imaging (fMRI). Threat predictability was manipulated by presenting an aversive unconditioned stimulus (UCS) that was either preceded by a conditioned stimulus (i.e., predictable) or by presenting the UCS alone (i.e., unpredictable). Similar to animal model research that has employed yoked fear conditioning procedures, one group (Controllable Condition; CC), but not the other group (Uncontrollable Condition; UC) was able to terminate the UCS. The fMRI signal response within the dorsolateral prefrontal cortex (PFC), dorsomedial PFC, ventromedial PFC, and posterior cingulate was diminished during predictable compared to unpredictable threat (i.e., UCS). In addition, threat-related activity within the ventromedial PFC and bilateral hippocampus was diminished only to threats that were both predictable and controllable. These findings provide insight into how threat predictability and controllability affects the activity of brain regions (i.e., ventromedial PFC and hippocampus) involved in emotion regulation, and may have important implications for better understanding neural processes that mediate emotional resilience to stress. PMID:26149610
Kiryu, T; Minagawa, H
2013-01-01
Several types of electric motor assists have been developed, as a result, it is important to control muscular fatigue on-site in terms of health promotion and motor rehabilitation. Predicting the perceived fatigue by several biosignal-related variables with the multiple regression model and polynomial approximation, we try to propose a self control design for the electrically assisted bicycle (EAB). We also determine the meaningful muscles during pedaling by muscle synergies in relation to the motion maturity. In field experiments, prediction of ongoing perceived physical fatigue could have the potential of suitable control of EAB.
Model predictive control of a combined heat and power plant using local linear models
Kikstra, J.F.; Roffel, B.; Schoen, P.
1998-10-01
Model predictive control has been applied to control of a combined heat and power plant. One of the main features of this plant is that it exhibits nonlinear process behavior due to large throughput swings. In this application, the operating window of the plant has been divided into a number of smaller windows in which the nonlinear process behavior has been approximated by linear behavior. For each operating window, linear step weight models were developed from a detailed nonlinear first principles model, and the model prediction is calculated based on interpolation between these linear models. The model output at each operating point can then be calculated from four basic linear models, and the required control action can subsequently be calculated with the standard model predictive control approach using quadratic programming.
Artificial neural network implementation of a near-ideal error prediction controller
NASA Technical Reports Server (NTRS)
Mcvey, Eugene S.; Taylor, Lynore Denise
1992-01-01
A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error
Constrained space camera assembly
Heckendorn, Frank M.; Anderson, Erin K.; Robinson, Casandra W.; Haynes, Harriet B.
1999-01-01
A constrained space camera assembly which is intended to be lowered through a hole into a tank, a borehole or another cavity. The assembly includes a generally cylindrical chamber comprising a head and a body and a wiring-carrying conduit extending from the chamber. Means are included in the chamber for rotating the body about the head without breaking an airtight seal formed therebetween. The assembly may be pressurized and accompanied with a pressure sensing means for sensing if a breach has occurred in the assembly. In one embodiment, two cameras, separated from their respective lenses, are installed on a mounting apparatus disposed in the chamber. The mounting apparatus includes means allowing both longitudinal and lateral movement of the cameras. Moving the cameras longitudinally focuses the cameras, and moving the cameras laterally away from one another effectively converges the cameras so that close objects can be viewed. The assembly further includes means for moving lenses of different magnification forward of the cameras.
Sense of control predicts depressive and anxious symptoms across the transition to parenthood.
Keeton, Courtney Pierce; Perry-Jenkins, Maureen; Sayer, Aline G
2008-04-01
In this study, the authors examined the relationship between sense of control and depressive and anxious symptoms for mothers and fathers during the 1st year of parenthood. Participants were 153 dual-earner, working-class couples who were recruited during the 3rd trimester of pregnancy at prenatal education courses. Data were collected 1 month antenatally and 1, 4, 6, and 12 months postnatally. Sense of control was decomposed into 2 distinct parts: an enduring component and a malleable component that changes with context. Consistent with a cognitive theory of emotional problems, results demonstrated that a sense of control served a protective function for mental health outcomes. A higher sense of enduring control predicted lower levels of psychological distress for new parents, and increases in control over time predicted decreases in depression and anxiety. Findings hold implications for interventions with expectant parents, such as expanding prenatal education courses to include strategies for enhancing and maintaining a sense of personal control.
NASA Astrophysics Data System (ADS)
Kamel, Ouari; Mohand, Ouhrouche; Toufik, Rekioua; Taib, Nabil
2015-01-01
In order to improvement of the performances for wind energy conversions systems (WECS), an advanced control techniques must be used. In this paper, as an alternative to conventional PI-type control methods, a nonlinear predictive control (NPC) approach is developed for DFIG-based wind turbine. To enhance the robustness of the controller, a disturbance observer is designed to estimate the aerodynamic torque which is considered as an unknown perturbation. An explicitly analytical form of the optimal predictive controller is given consequently on-line optimization is not necessary The DFIG is fed through the rotor windings by a back-to-back converter controlled by Pulse Width Modulation (PWM), where the stator winding is directly connected to the grid. The presented simulation results show a good performance in trajectory tracking of the proposed strategy and rejection of disturbances is successfully achieved.
Model predictive control based on reduced order models applied to belt conveyor system.
Chen, Wei; Li, Xin
2016-11-01
In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system.
NASA Astrophysics Data System (ADS)
Balbi, V.; Kuhl, E.; Ciarletta, P.
2015-05-01
With nine meters in length, the gastrointestinal tract is not only our longest, but also our structurally most diverse organ. During embryonic development, it evolves as a bilayered tube with an inner endodermal lining and an outer mesodermal layer. Its inner surface displays a wide variety of morphological patterns, which are closely correlated to digestive function. However, the evolution of these intestinal patterns remains poorly understood. Here we show that geometric and mechanical factors can explain intestinal pattern formation. Using the nonlinear field theories of mechanics, we model surface morphogenesis as the instability problem of constrained differential growth. To allow for internal and external expansion, we model the gastrointestinal tract with homogeneous Neumann boundary conditions. To establish estimates for the folding pattern at the onset of folding, we perform a linear stability analysis supplemented by the perturbation theory. To predict pattern evolution in the post-buckling regime, we perform a series of nonlinear finite element simulations. Our model explains why longitudinal folds emerge in the esophagus with a thick and stiff outer layer, whereas circumferential folds emerge in the jejunum with a thinner and softer outer layer. In intermediate regions like the feline esophagus, longitudinal and circumferential folds emerge simultaneously. Our model could serve as a valuable tool to explain and predict alterations in esophageal morphology as a result of developmental disorders or certain digestive pathologies including food allergies.
Operational flood control of a low-lying delta system using large time step Model Predictive Control
NASA Astrophysics Data System (ADS)
Tian, Xin; van Overloop, Peter-Jules; Negenborn, Rudy R.; van de Giesen, Nick
2015-01-01
The safety of low-lying deltas is threatened not only by riverine flooding but by storm-induced coastal flooding as well. For the purpose of flood control, these deltas are mostly protected in a man-made environment, where dikes, dams and other adjustable infrastructures, such as gates, barriers and pumps are widely constructed. Instead of always reinforcing and heightening these structures, it is worth considering making the most of the existing infrastructure to reduce the damage and manage the delta in an operational and overall way. In this study, an advanced real-time control approach, Model Predictive Control, is proposed to operate these structures in the Dutch delta system (the Rhine-Meuse delta). The application covers non-linearity in the dynamic behavior of the water system and the structures. To deal with the non-linearity, a linearization scheme is applied which directly uses the gate height instead of the structure flow as the control variable. Given the fact that MPC needs to compute control actions in real-time, we address issues regarding computational time. A new large time step scheme is proposed in order to save computation time, in which different control variables can have different control time steps. Simulation experiments demonstrate that Model Predictive Control with the large time step setting is able to control a delta system better and much more efficiently than the conventional operational schemes.
PLIO: a generic tool for real-time operational predictive optimal control of water networks.
Cembrano, G; Quevedo, J; Puig, V; Pérez, R; Figueras, J; Verdejo, J M; Escaler, I; Ramón, G; Barnet, G; Rodríguez, P; Casas, M
2011-01-01
This paper presents a generic tool, named PLIO, that allows to implement the real-time operational control of water networks. Control strategies are generated using predictive optimal control techniques. This tool allows the flow management in a large water supply and distribution system including reservoirs, open-flow channels for water transport, water treatment plants, pressurized water pipe networks, tanks, flow/pressure control elements and a telemetry/telecontrol system. Predictive optimal control is used to generate flow control strategies from the sources to the consumer areas to meet future demands with appropriate pressure levels, optimizing operational goals such as network safety volumes and flow control stability. PLIO allows to build the network model graphically and then to automatically generate the model equations used by the predictive optimal controller. Additionally, PLIO can work off-line (in simulation) and on-line (in real-time mode). The case study of Santiago-Chile is presented to exemplify the control results obtained using PLIO off-line (in simulation).
Predictive models of glucose control: roles for glucose-sensing neurones.
Kosse, C; Gonzalez, A; Burdakov, D
2015-01-01
The brain can be viewed as a sophisticated control module for stabilizing blood glucose. A review of classical behavioural evidence indicates that central circuits add predictive (feedforward/anticipatory) control to the reactive (feedback/compensatory) control by peripheral organs. The brain/cephalic control is constructed and engaged, via associative learning, by sensory cues predicting energy intake or expenditure (e.g. sight, smell, taste, sound). This allows rapidly measurable sensory information (rather than slowly generated internal feedback signals, e.g. digested nutrients) to control food selection, glucose supply for fight-or-flight responses or preparedness for digestion/absorption. Predictive control is therefore useful for preventing large glucose fluctuations. We review emerging roles in predictive control of two classes of widely projecting hypothalamic neurones, orexin/hypocretin (ORX) and melanin-concentrating hormone (MCH) cells. Evidence is cited that ORX neurones (i) are activated by sensory cues (e.g. taste, sound), (ii) drive hepatic production, and muscle uptake, of glucose, via sympathetic nerves, (iii) stimulate wakefulness and exploration via global brain projections and (iv) are glucose-inhibited. MCH neurones are (i) glucose-excited, (ii) innervate learning and reward centres to promote synaptic plasticity, learning and memory and (iii) are critical for learning associations useful for predictive control (e.g. using taste to predict nutrient value of food). This evidence is unified into a model for predictive glucose control. During associative learning, inputs from some glucose-excited neurones may promote connections between the 'fast' senses and reward circuits, constructing neural shortcuts for efficient action selection. In turn, glucose-inhibited neurones may engage locomotion/exploration and coordinate the required fuel supply. Feedback inhibition of the latter neurones by glucose would ensure that glucose fluxes they stimulate
NASA Technical Reports Server (NTRS)
Acikmese, Behcet A.; Carson, John M., III
2005-01-01
A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees the resolvability of the associated finite-horizon optimal control problem in a receding-horizon implementation. The control consists of two components; (i) feedforward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives, and derivatives in polytopes. An illustrative numerical example is also provided.
NASA Technical Reports Server (NTRS)
Acikmese, Ahmet Behcet; Carson, John M., III
2006-01-01
A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees resolvability. With resolvability, initial feasibility of the finite-horizon optimal control problem implies future feasibility in a receding-horizon framework. The control consists of two components; (i) feed-forward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives and derivatives in polytopes. An illustrative numerical example is also provided.
Application of time-variant predictive control to modelling driver steering skill
NASA Astrophysics Data System (ADS)
Keen, Steven D.; Cole, David J.
2011-04-01
The paper addresses the need for improved mathematical models of human steering control. A multiple-model structure for a driver's internal model of a nonlinear vehicle is proposed. The multiple-model structure potentially offers a straightforward way to represent a range of driver expertise. The internal model is combined with a model predictive steering controller. The controller generates a steering command through the minimisation of a cost function involving vehicle path error. A study of the controller performance during an aggressive, nonlinear steering manoeuvre is provided. Analysis of the controller performance reveals a reduction in the closed-loop controller bandwidth with increasing tyre saturation and fixed controller gains. A parameter study demonstrates that increasing the multiple-model density, increasing the weights on the path error, and increasing the controller knowledge range all improved the path following accuracy of the controller.
Control, Filtering and Prediction for Phased Arrays in Directed Energy Systems
2016-04-30
AFRL-RD-PS- AFRL-RD-PS- TR-2016-0029 TR-2016-0029 CONTROL, FILTERING AND PREDICTION FOR PHASED ARRAYS IN DIRECTED ENERGY SYSTEMS Steve Gibson...UNLIMITED. AIR FORCE RESEARCH LABORATORY Directed Energy Directorate 3550 Aberdeen Ave SE AIR FORCE MATERIEL COMMAND KIRTLAND AIR FORCE BASE, NM...Filtering and Prediction for Phased Arrays in Directed Energy Systems 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER
Job, Veronika; Bernecker, Katharina; Miketta, Stefanie; Friese, Malte
2015-10-01
Past research indicates that peoples' implicit theories about the nature of willpower moderate the ego-depletion effect. Only people who believe or were led to believe that willpower is a limited resource (limited-resource theory) showed lower self-control performance after an initial demanding task. As of yet, the underlying processes explaining this moderating effect by theories about willpower remain unknown. Here, we propose that the exertion of self-control activates the goal to preserve and replenish mental resources (rest goal) in people with a limited-resource theory. Five studies tested this hypothesis. In Study 1, individual differences in implicit theories about willpower predicted increased accessibility of a rest goal after self-control exertion. Furthermore, measured (Study 2) and manipulated (Study 3) willpower theories predicted an increased preference for rest-conducive objects. Finally, Studies 4 and 5 provide evidence that theories about willpower predict actual resting behavior: In Study 4, participants who held a limited-resource theory took a longer break following self-control exertion than participants with a nonlimited-resource theory. Longer resting time predicted decreased rest goal accessibility afterward. In Study 5, participants with an induced limited-resource theory sat longer on chairs in an ostensible product-testing task when they had engaged in a task requiring self-control beforehand. This research provides consistent support for a motivational shift toward rest after self-control exertion in people holding a limited-resource theory about willpower.
Predictive-model-based dynamic coordination control strategy for power-split hybrid electric bus
NASA Astrophysics Data System (ADS)
Zeng, Xiaohua; Yang, Nannan; Wang, Junnian; Song, Dafeng; Zhang, Nong; Shang, Mingli; Liu, Jianxin
2015-08-01
Parameter-matching methods and optimal control strategies of the top-selling hybrid electric vehicle (HEV), namely, power-split HEV, are widely studied. In particular, extant research on control strategy focuses on the steady-state energy management strategy to obtain better fuel economy. However, given that multi-power sources are highly coupled in power-split HEVs and influence one another during mode shifting, conducting research on dynamic coordination control strategy (DCCS) to achieve riding comfort is also important. This paper proposes a predictive-model-based DCCS. First, the dynamic model of the objective power-split HEV is built and the mode shifting process is analyzed based on the developed model to determine the reason for the system shock generated. Engine torque estimation algorithm is then designed according to the principle of the nonlinear observer, and the prediction model of the degree of shock is established based on the theory of model predictive control. Finally, the DCCS with adaptation for a complex driving cycle is realized by combining the feedback control and the predictive model. The presented DCCS is validated on the co-simulation platform of AMESim and Simulink. Results show that the shock during mode shifting is well controlled, thereby improving riding comfort.
NASA Astrophysics Data System (ADS)
Zhang, Qian; Wang, Qunjing; Li, Guoli
2016-05-01
This article deals with the identification of nonlinear model and Nonlinear Predictive Functional Controller (NPFC) design based on the Hammerstein structure for the turntable servo system. As a mechanism with multi-mass rotational system, nonlinearities significantly influence the system operation, especially when the turntable is in the states of zero-crossing distortion or rapid acceleration/deceleration, etc. The field data from identification experiments are processed by Comprehensive Learning Particle Swarm Optimization (CLPSO). As a result, Hammerstein model can be derived to describe the input-output relationship globally, considering all the linear and nonlinear factors of the turntable system. Cross validation results demonstrate good correspondence between the real equipment and the identified model. In the second part of this manuscript, a nonlinear control strategy based on the genetic algorithm and predictive control is developed. The global nonlinear predictive controller is carried out by two steps: (i) build the linear predictive functional controller with state space equations for the linear subsystem of Hammerstein model, and (ii) optimize the global control variable by minimizing the cost function through genetic algorithm. On the basis of distinguish model for turntable and the effectiveness of NPFC, the good performance of tracking ability is achieved in the simulation results.
Lenaert, Bert; Barry, Tom J; Schruers, Koen; Vervliet, Bram; Hermans, Dirk
2016-01-01
Hypothalamic-pituitary-adrenal (HPA) axis irregularities have been associated with several psychological disorders. Hence, the identification of individual difference variables that predict variations in HPA-axis activity represents an important challenge for psychiatric research. We investigated whether self-reported attentional control in emotionally demanding situations prospectively predicted changes in diurnal salivary cortisol secretion following exposure to a prolonged psychosocial stressor. Low ability to voluntarily control attention has previously been associated with anxiety and depressive symptomatology. Attentional control was assessed using the Emotional Attentional Control Scale. In students who were preparing for academic examination, salivary cortisol was assessed before (time 1) and after (time 2) examination. Results showed that lower levels of self-reported emotional attentional control at time 1 (N=90) predicted higher absolute diurnal cortisol secretion and a slower decline in cortisol throughout the day at time 2 (N=71). Difficulty controlling attention during emotional experiences may lead to chronic HPA-axis hyperactivity after prolonged exposure to stress. These results indicate that screening for individual differences may foster prediction of HPA-axis disturbances, paving the way for targeted disorder prevention.
NASA Astrophysics Data System (ADS)
van Heeringen, Klaas-Jan; Gooijer, Jan; Knot, Floris; Talsma, Jan
2015-04-01
In the Netherlands, flood protection has always been a key issue to protect settlements against storm surges and riverine floods. Whereas flood protection traditionally focused on structural measures, nowadays the availability of meteorological and hydrological forecasts enable the application of more advanced real-time control techniques for operating the existing hydraulic infrastructure in an anticipatory and more efficient way. Model Predictive Control (MPC) is a powerful technique to derive optimal control variables with the help of model based predictions evaluated against a control objective. In a project for the regional water authority Noorderzijlvest in the north of the Netherlands, it has been shown that MPC can increase the safety level of the system during flood events by an anticipatory pre-release of water. Furthermore, energy costs of pumps can be reduced by making tactical use of the water storage and shifting pump activities during normal operating conditions to off-peak hours. In this way cheap energy is used in combination of gravity flow through gates during low tide periods. MPC has now been implemented for daily operational use of the whole water system of the water authority Noorderzijlvest. The system developed to a real time decision support system which not only supports the daily operation but is able to directly implement the optimal control settings at the structures. We explain how we set-up and calibrated a prediction model (RTC-Tools) that is accurate and fast enough for optimization purposes, and how we integrated it in the operational flood early warning system (Delft-FEWS). Beside the prediction model, the weights and the factors of the objective function are an important element of MPC, since they shape the control objective. We developed special features in Delft-FEWS to allow the operators to adjust the objective function in order to meet changing requirements and to evaluate different control strategies.
Multi input single output model predictive control of non-linear bio-polymerization process
Arumugasamy, Senthil Kumar; Ahmad, Z.
2015-05-15
This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state space model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.
NASA Technical Reports Server (NTRS)
He, Yuning
2015-01-01
Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.
A predictive control framework for optimal energy extraction of wind farms
NASA Astrophysics Data System (ADS)
Vali, M.; van Wingerden, J. W.; Boersma, S.; Petrović, V.; Kühn, M.
2016-09-01
This paper proposes an adjoint-based model predictive control for optimal energy extraction of wind farms. It employs the axial induction factor of wind turbines to influence their aerodynamic interactions through the wake. The performance index is defined here as the total power production of the wind farm over a finite prediction horizon. A medium-fidelity wind farm model is utilized to predict the inflow propagation in advance. The adjoint method is employed to solve the formulated optimization problem in a cost effective way and the first part of the optimal solution is implemented over the control horizon. This procedure is repeated at the next controller sample time providing the feedback into the optimization. The effectiveness and some key features of the proposed approach are studied for a two turbine test case through simulations.
Validation of engineering methods for predicting hypersonic vehicle controls forces and moments
NASA Technical Reports Server (NTRS)
Maughmer, M.; Straussfogel, D.; Long, L.; Ozoroski, L.
1991-01-01
This work examines the ability of the aerodynamic analysis methods contained in an industry standard conceptual design code, the Aerodynamic Preliminary Analysis System (APAS II), to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds. Predicted control forces and moments generated by various control effectors are compared with previously published wind-tunnel and flight-test data for three vehicles: the North American X-15, a hypersonic research airplane concept, and the Space Shuttle Orbiter. Qualitative summaries of the results are given for each force and moment coefficient and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage.
On Application of Model Predictive Control to Power Converter with Switching
NASA Astrophysics Data System (ADS)
Zanma, Tadanao; Fukuta, Junichi; Doki, Shinji; Ishida, Muneaki; Okuma, Shigeru; Matsumoto, Takashi; Nishimori, Eiji
This paper concerns a DC-DC converter control. In DC-DC converters, there exist both continuous components such as inductance, conductance and resistance and discrete ones, IGBT and MOSFET as semiconductor switching elements. Such a system can be regarded as a hybrid dynamical system. Thus, this paper presents a dc-dc control technique based on the model predictive control. Specifically, a case in which the load of the dc-dc converter changes from active to sleep is considered. In the case, a control method which makes the output voltage follow to the reference quickly in transition, and the switching frequency be constant in steady state. In addition, in applying the model predictive control to power electronics circuits, the switching characteristic of the device and the restriction condition for protection are also considered. The effectiveness of the proposed method is illustrated by comparing a conventional method through some simulation results.
LMI-Based Generation of Feedback Laws for a Robust Model Predictive Control Algorithm
NASA Technical Reports Server (NTRS)
Acikmese, Behcet; Carson, John M., III
2007-01-01
This technical note provides a mathematical proof of Corollary 1 from the paper 'A Nonlinear Model Predictive Control Algorithm with Proven Robustness and Resolvability' that appeared in the 2006 Proceedings of the American Control Conference. The proof was omitted for brevity in the publication. The paper was based on algorithms developed for the FY2005 R&TD (Research and Technology Development) project for Small-body Guidance, Navigation, and Control [2].The framework established by the Corollary is for a robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems that guarantees the resolvability of the associated nite-horizon optimal control problem in a receding-horizon implementation. Additional details of the framework are available in the publication.
Model predictive control of non-linear systems over networks with data quantization and packet loss.
Yu, Jimin; Nan, Liangsheng; Tang, Xiaoming; Wang, Ping
2015-11-01
This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur. The non-linear controlled plant in the networked control system (NCS) is represented by a Tagaki-Sugeno (T-S) model. The sensed data and control signal are quantized in both links and described as sector bound uncertainties by applying sector bound approach. Then, the quantized data are transmitted in the communication networks and may suffer from the effect of packet losses, which are modeled as Bernoulli process. A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.
Neural Network Assisted Inverse Dynamic Guidance for Terminally Constrained Entry Flight
Chen, Wanchun
2014-01-01
This paper presents a neural network assisted entry guidance law that is designed by applying Bézier approximation. It is shown that a fully constrained approximation of a reference trajectory can be made by using the Bézier curve. Applying this approximation, an inverse dynamic system for an entry flight is solved to generate guidance command. The guidance solution thus gotten ensures terminal constraints for position, flight path, and azimuth angle. In order to ensure terminal velocity constraint, a prediction of the terminal velocity is required, based on which, the approximated Bézier curve is adjusted. An artificial neural network is used for this prediction of the terminal velocity. The method enables faster implementation in achieving fully constrained entry flight. Results from simulations indicate improved performance of the neural network assisted method. The scheme is expected to have prospect for further research on automated onboard control of terminal velocity for both reentry and terminal guidance laws. PMID:24723821
Imamizu, Hiroshi; Kawato, Mitsuo
2009-07-01
Humans can guide their actions toward the realization of their intentions. Flexible, rapid and precise realization of intentions and goals relies on the brain learning to control its actions on external objects and to predict the consequences of this control. Neural mechanisms that mimic the input-output properties of our own body and other objects can be used to support prediction and control, and such mechanisms are called internal models. We first summarize functional neuroimaging, behavioral and computational studies of the brain mechanisms related to acquisition, modular organization, and the predictive switching of internal models mainly for tool use. These mechanisms support predictive control and flexible switching of intentional actions. We then review recent studies demonstrating that internal models are crucial for the execution of not only immediate actions but also higher-order cognitive functions, including optimization of behaviors toward long-term goals, social interactions based on prediction of others' actions and mental states, and language processing. These studies suggest that a concept of internal models can consistently explain the neural mechanisms and computational principles needed for fundamental sensorimotor functions as well as higher-order cognitive functions.
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.
Predicting Human Error in Air Traffic Control Decision Support Tools and Free Flight Concepts
NASA Technical Reports Server (NTRS)
Mogford, Richard; Kopardekar, Parimal
2001-01-01
The document is a set of briefing slides summarizing the work the Advanced Air Transportation Technologies (AATT) Project is doing on predicting air traffic controller and airline pilot human error when using new decision support software tools and when involved in testing new air traffic control concepts. Previous work in this area is reviewed as well as research being done jointly with the FAA. Plans for error prediction work in the AATT Project are discussed. The audience is human factors researchers and aviation psychologists from government and industry.
Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control
Deshpande, Sunil; Nandola, Naresh N.; Rivera, Daniel E.; Younger, Jarred W.
2014-01-01
The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health. PMID:25506132
Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control.
Deshpande, Sunil; Nandola, Naresh N; Rivera, Daniel E; Younger, Jarred W
2014-12-01
The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.
Constrained space camera assembly
Heckendorn, F.M.; Anderson, E.K.; Robinson, C.W.; Haynes, H.B.
1999-05-11
A constrained space camera assembly which is intended to be lowered through a hole into a tank, a borehole or another cavity is disclosed. The assembly includes a generally cylindrical chamber comprising a head and a body and a wiring-carrying conduit extending from the chamber. Means are included in the chamber for rotating the body about the head without breaking an airtight seal formed therebetween. The assembly may be pressurized and accompanied with a pressure sensing means for sensing if a breach has occurred in the assembly. In one embodiment, two cameras, separated from their respective lenses, are installed on a mounting apparatus disposed in the chamber. The mounting apparatus includes means allowing both longitudinal and lateral movement of the cameras. Moving the cameras longitudinally focuses the cameras, and moving the cameras laterally away from one another effectively converges the cameras so that close objects can be viewed. The assembly further includes means for moving lenses of different magnification forward of the cameras. 17 figs.
Constrained Allocation Flux Balance Analysis
Mori, Matteo; Hwa, Terence; Martin, Olivier C.
2016-01-01
New experimental results on bacterial growth inspire a novel top-down approach to study cell metabolism, combining mass balance and proteomic constraints to extend and complement Flux Balance Analysis. We introduce here Constrained Allocation Flux Balance Analysis, CAFBA, in which the biosynthetic costs associated to growth are accounted for in an effective way through a single additional genome-wide constraint. Its roots lie in the experimentally observed pattern of proteome allocation for metabolic functions, allowing to bridge regulation and metabolism in a transparent way under the principle of growth-rate maximization. We provide a simple method to solve CAFBA efficiently and propose an “ensemble averaging” procedure to account for unknown protein costs. Applying this approach to modeling E. coli metabolism, we find that, as the growth rate increases, CAFBA solutions cross over from respiratory, growth-yield maximizing states (preferred at slow growth) to fermentative states with carbon overflow (preferred at fast growth). In addition, CAFBA allows for quantitatively accurate predictions on the rate of acetate excretion and growth yield based on only 3 parameters determined by empirical growth laws. PMID:27355325
Offset-Free Model Predictive Control of Open Water Channel Based on Moving Horizon Estimation
NASA Astrophysics Data System (ADS)
Ekin Aydin, Boran; Rutten, Martine
2016-04-01
Model predictive control (MPC) is a powerful control option which is increasingly used by operational water managers for managing water systems. The explicit consideration of constraints and multi-objective management are important features of MPC. However, due to the water loss in open water systems by seepage, leakage and evaporation a mismatch between the model and the real system will be created. These mismatch affects the performance of MPC and creates an offset from the reference set point of the water level. We present model predictive control based on moving horizon estimation (MHE-MPC) to achieve offset free control of water level for open water canals. MHE-MPC uses the past predictions of the model and the past measurements of the system to estimate unknown disturbances and the offset in the controlled water level is systematically removed. We numerically tested MHE-MPC on an accurate hydro-dynamic model of the laboratory canal UPC-PAC located in Barcelona. In addition, we also used well known disturbance modeling offset free control scheme for the same test case. Simulation experiments on a single canal reach show that MHE-MPC outperforms disturbance modeling offset free control scheme.
Buss, Kristin A.; Kiel, Elizabeth J.; Morales, Santiago; Robinson, Emily
2013-01-01
Poor inhibitory control and bold-approach have been found to predict the development of externalizing behavior problems in young children. Less research has examined how positive affect may influence the development of externalizing behavior in the context of low inhibitory control and high approach. We used a multimethod approach to examine how observed toddler inhibitory control, bold-approach, and positive affect predicted externalizing outcomes (observed, adult- and self-reported) in additive and interactive ways at the beginning of kindergarten. 24-month-olds (N = 110) participated in a laboratory visit and 84 were followed up in kindergarten for externalizing behaviors. Overall, children who were low in inhibitory control, high in bold-approach, and low in positive affect at 24-months of age were at greater risk for externalizing behaviors during kindergarten. PMID:25018589
Design of a robust model predictive controller with reduced computational complexity.
Razi, M; Haeri, M
2014-11-01
The practicality of robust model predictive control of systems with model uncertainties depends on the time consumed for solving a defined optimization problem. This paper presents a method for the computational complexity reduction in a robust model predictive control. First a scaled state vector is defined such that the objective function contours in the defined optimization problem become vertical or horizontal ellipses or circles, and then the control input is determined at each sampling time as a state feedback that minimizes the infinite horizon objective function by solving some linear matrix inequalities. The simulation results show that the number of iterations to solve the problem at each sampling interval is reduced while the control performance does not alter noticeably.
A new methane control and prediction software suite for longwall mines
NASA Astrophysics Data System (ADS)
Dougherty, Heather N.; Özgen Karacan, C.
2011-09-01
This paper presents technical and application aspects of a new software suite, MCP (Methane Control and Prediction), developed for addressing some of the methane and methane control issues in longwall coal mines. The software suite consists of dynamic link library (DLL) extensions to MS-Access TM, written in C++. In order to create the DLLs, various statistical, mathematical approaches, prediction and classification artificial neural network (ANN) methods were used. The current version of MCP suite (version 1.3) discussed in this paper has four separate modules that (a) predict the dynamic elastic properties of coal-measure rocks, (b) predict ventilation emissions from longwall mines, (c) determine the type of degasification system that needs to be utilized for given situations and (d) assess the production performance of gob gas ventholes that are used to extract methane from longwall gobs. These modules can be used with the data from basic logs, mining, longwall panel, productivity, and coal bed characteristics. The applications of these modules separately or in combination for methane capture and control related problems will help improve the safety of mines. The software suite's version 1.3 is discussed in this paper. Currently, it's new version 2.0 is available and can be downloaded from http://www.cdc.gov/niosh/mining/products/product180.htm free of charge. The models discussed in this paper can be found under "ancillary models" and under "methane prediction models" for specific U.S. conditions in the new version.
Buceta, David; Tojo, Concha; Vukmirovic, Miomir B; Deepak, Francis Leonard; López-Quintela, M Arturo
2015-07-14
We present a theoretical model to predict the atomic structure of Au/Pt nanoparticles synthesized in microemulsions. Excellent concordance with the experimental results shows that the structure of the nanoparticles can be controlled at subnanometer resolution simply by changing the reactant concentration. The results of this study not only offer a better understanding of the complex mechanisms governing reactions in microemulsions, but open up a simple new way to synthesize bimetallic nanoparticles with ad hoc controlled nanostructures.
T-S fuzzy model predictive speed control of electrical vehicles.
Khooban, Mohammad Hassan; Vafamand, Navid; Niknam, Taher
2016-09-01
This paper proposes a novel nonlinear model predictive controller (MPC) in terms of linear matrix inequalities (LMIs). The proposed MPC is based on Takagi-Sugeno (TS) fuzzy model, a non-parallel distributed compensation (non-PDC) fuzzy controller and a non-quadratic Lyapunov function (NQLF). Utilizing the non-PDC controller together with the Lyapunov theorem guarantees the stabilization issue of this MPC. In this approach, at each sampling time a quadratic cost function with an infinite prediction and control horizon is minimized such that constraints on the control input Euclidean norm are satisfied. To show the merits of the proposed approach, a nonlinear electric vehicle (EV) system with parameter uncertainty is considered as a case study. Indeed, the main goal of this study is to force the speed of EV to track a desired value. The experimental data, a new European driving cycle (NEDC), is used in order to examine the performance of the proposed controller. First, the equivalent TS model of the original nonlinear system is derived. After that, in order to evaluate the proficiency of the proposed controller, the achieved results of the proposed approach are compared with those of the conventional MPC controller and the optimal Fuzzy PI controller (OFPI), which are the latest research on the problem in hand.
Kwon, Kideok; Yang, Jihoon; Yoo, Younghwan
2015-04-24
A number of research works has studied packet scheduling policies in energy scavenging wireless sensor networks, based on the predicted amount of harvested energy. Most of them aim to achieve energy neutrality, which means that an embedded system can operate perpetually while meeting application requirements. Unlike other renewable energy sources, solar energy has the feature of distinct periodicity in the amount of harvested energy over a day. Using this feature, this paper proposes a packet transmission control policy that can enhance the network performance while keeping sensor nodes alive. Furthermore, this paper suggests a novel solar energy prediction method that exploits the relation between cloudiness and solar radiation. The experimental results and analyses show that the proposed packet transmission policy outperforms others in terms of the deadline miss rate and data throughput. Furthermore, the proposed solar energy prediction method can predict more accurately than others by 6.92%.
NASA Astrophysics Data System (ADS)
Abrahamse, Augusta
2010-12-01
Future advances in cosmology will depend on the next generation of cosmological observations and how they shape our theoretical understanding of the universe. Current theoretical ideas, however, have an important role to play in guiding the design of such observational programs. The work presented in this thesis concerns the intersection of observation and theory, particularly as it relates to advancing our understanding of the accelerated expansion of the universe (or the dark energy). Chapters 2 - 4 make use of the simulated data sets developed by the Dark Energy Task Force (DETF) for a number of cosmological observations currently in the experimental pipeline. We use these forecast data in the analysis of four quintessence models of dark energy: the PNGB, Exponential, Albrecht-Skordis and Inverse Power Law (IPL) models. Using Markov Chain Monte Carlo sampling techniques we examine the ability of each simulated data set to constrain the parameter space of these models. We examine the potential of the data for differentiating time-varying models from a pure cosmological constant. Additionally, we introduce an abstract parameter space to facilitate comparison between models and investigate the ability of future data to distinguish between these quintessence models. In Chapter 5 we present work towards understanding the effects of systematic errors associated with photometric redshift estimates. Due to the need to sample a vast number of deep and faint galaxies, photometric redshifts will be used in a wide range of future cosmological observations including gravitational weak lensing, baryon accoustic oscillations and type 1A supernovae observations. The uncertainty in the redshift distributions of galaxies has a significant potential impact on the cosmological parameter values inferred from such observations. We introduce a method for parameterizing uncertainties in modeling assumptions affecting photometric redshift calculations and for propagating these
A web-based noise control prediction model for rooms using the method of images
NASA Astrophysics Data System (ADS)
Dance, Stephen
2002-11-01
Previous simple models could only predict sound levels in untreated rooms. Now, using the method of images, it has become possible to accurately predict the sound level in fitted industrial rooms from any computer on the Internet. Thus, a powerful tool in an acoustician's armory is available to all, while requiring only the minimal amount of input data to construct the model. This is only achievable if the scope of the model is reduced to one or two acoustic parameters. Now, two common noise control techniques have been implemented into the image source model: acoustic barriers and absorptive patches. Predictions using the model with and without noise control techniques will be demonstrated, so the advantages can be clearly seen in typical industrial rooms. The models are now available on the web, running directly inside Netscape or Internet Explorer.
Effective variable switching point predictive current control for ac low-voltage drives
NASA Astrophysics Data System (ADS)
Stolze, Peter; Karamanakos, Petros; Kennel, Ralph; Manias, Stefanos; Endisch, Christian
2015-07-01
This paper presents an effective model predictive current control scheme for induction machines driven by a three-level neutral point clamped inverter, called variable switching point predictive current control. Despite the fact that direct, enumeration-based model predictive control (MPC) strategies are very popular in the field of power electronics due to their numerous advantages such as design simplicity and straightforward implementation procedure, they carry two major drawbacks. These are the increased computational effort and the high ripples on the controlled variables, resulting in a limited applicability of such methods. The high ripples occur because in direct MPC algorithms the actuating variable can only be changed at the beginning of a sampling interval. A possible remedy for this would be to change the applied control input within the sampling interval, and thus to apply it for a shorter time than one sample. However, since such a solution would lead to an additional overhead which is crucial especially for multilevel inverters, a heuristic preselection of the optimal control action is adopted to keep the computational complexity at bay. Experimental results are provided to verify the potential advantages of the proposed strategy.
Tully, Laura M; Lincoln, Sarah Hope; Hooker, Christine I
2014-01-01
LPFC dysfunction is a well-established neural impairment in schizophrenia and is associated with worse symptoms. However, how LPFC activation influences symptoms is unclear. Previous findings in healthy individuals demonstrate that lateral prefrontal cortex (LPFC) activation during cognitive control of emotional information predicts mood and behavior in response to interpersonal conflict, thus impairments in these processes may contribute to symptom exacerbation in schizophrenia. We investigated whether schizophrenia participants show LPFC deficits during cognitive control of emotional information, and whether these LPFC deficits prospectively predict changes in mood and symptoms following real-world interpersonal conflict. During fMRI, 23 individuals with schizophrenia or schizoaffective disorder and 24 healthy controls completed the Multi-Source Interference Task superimposed on neutral and negative pictures. Afterwards, schizophrenia participants completed a 21-day online daily-diary in which they rated the extent to which they experienced mood and schizophrenia-spectrum symptoms, as well as the occurrence and response to interpersonal conflict. Schizophrenia participants had lower dorsal LPFC activity (BA9) during cognitive control of task-irrelevant negative emotional information. Within schizophrenia participants, DLPFC activity during cognitive control of emotional information predicted changes in positive and negative mood on days following highly distressing interpersonal conflicts. Results have implications for understanding the specific role of LPFC in response to social stress in schizophrenia, and suggest that treatments targeting LPFC-mediated cognitive control of emotion could promote adaptive response to social stress in schizophrenia.
Model of Predictive Control of a Direct-Fire Projectile Equipped With Canards
2005-03-01
matrices, which are sent to the MPC routine. The MPC routine calculates the optimal control sequence over the length of the update interval. When... 0767 . 19. Burchett, B.; Peterson, A.; Costello, M. Prediction of Swerving Motion of a Dual-Spin Projectile With Lateral Pulsejets in Atmospheric
Predictive Value of Morphological Features in Patients with Autism versus Normal Controls
ERIC Educational Resources Information Center
Ozgen, H.; Hellemann, G. S.; de Jonge, M. V.; Beemer, F. A.; van Engeland, H.
2013-01-01
We investigated the predictive power of morphological features in 224 autistic patients and 224 matched-pairs controls. To assess the relationship between the morphological features and autism, we used the receiver operator curves (ROC). In addition, we used recursive partitioning (RP) to determine a specific pattern of abnormalities that is…
ERIC Educational Resources Information Center
Bond, Frank W.; Flaxman, Paul E.
2006-01-01
This longitudinal study tested the degree to which an individual characteristic, psychological flexibility, and a work organization variable, job control, predicted ability to learn new skills at work, job performance, and mental health, amongst call center workers in the United Kingdom (N = 448). As hypothesized, results indicated that job…
NASA Technical Reports Server (NTRS)
Srokowski, A. J.
1978-01-01
The problem of obtaining accurate estimates of suction requirements on swept laminar flow control wings was discussed. A fast accurate computer code developed to predict suction requirements by integrating disturbance amplification rates was described. Assumptions and approximations used in the present computer code are examined in light of flow conditions on the swept wing which may limit their validity.
Application of Output Predictive Algorithmic Control to a Terrain Following Aircraft System.
1982-03-01
Preface I would like to thank my thesis advisor/ Dr. J. Gary Reid, and my thesis committee consisting of Capt. James silverthorn , Dr. John...June 1979, pp 387-392. 3.5 Reid, J. G., Chaffin, D. E., Silverthorn J. T. Output Predictive Algorithmic Control: Precision Tracking With
How Minimal Grade Goals and Self-Control Capacity Interact in Predicting Test Grades
ERIC Educational Resources Information Center
Bertrams, Alex
2012-01-01
The present research examined the prediction of school students' grades in an upcoming math test via their minimal grade goals (i.e., the minimum grade in an upcoming test one would be satisfied with). Due to its significance for initiating and maintaining goal-directed behavior, self-control capacity was expected to moderate the relation between…
Predictive control of hollow-fiber bioreactors for the production of monoclonal antibodies.
Dowd, J E; Weber, I; Rodriguez, B; Piret, J M; Kwok, K E
1999-05-20
The selection of medium feed rates for perfusion bioreactors represents a challenge for process optimization, particularly in bioreactors that are sampled infrequently. When the present and immediate future of a bioprocess can be adequately described, predictive control can minimize deviations from set points in a manner that can maximize process consistency. Predictive control of perfusion hollow-fiber bioreactors was investigated in a series of hybridoma cell cultures that compared operator control to computer estimation of feed rates. Adaptive software routines were developed to estimate the current and predict the future glucose uptake and lactate production of the bioprocess at each sampling interval. The current and future glucose uptake rates were used to select the perfusion feed rate in a designed response to deviations from the set point values. The routines presented a graphical user interface through which the operator was able to view the up-to-date culture performance and assess the model description of the immediate future culture performance. In addition, fewer samples were taken in the computer-estimated cultures, reducing labor and analytical expense. The use of these predictive controller routines and the graphical user interface decreased the glucose and lactate concentration variances up to sevenfold, and antibody yields increased by 10% to 43%.
Parenting and Child "DRD4" Genotype Interact to Predict Children's Early Emerging Effortful Control
ERIC Educational Resources Information Center
Smith, Heather J.; Sheikh, Haroon I.; Dyson, Margaret W.; Olino, Thomas M.; Laptook, Rebecca S.; Durbin, C. Emily; Hayden, Elizabeth P.; Singh, Shiva M.; Klein, Daniel N.
2012-01-01
Effortful control (EC), or the trait-like capacity to regulate dominant responses, has important implications for children's development. Although genetic factors and parenting likely influence EC, few studies have examined whether they interact to predict its development. This study examined whether the "DRD4" exon III variable number tandem…
Sridhar, Upasana Manimegalai; Govindarajan, Anand; Rhinehart, R Russell
2016-01-01
This work reveals the applicability of a relatively new optimization technique, Leapfrogging, for both nonlinear regression modeling and a methodology for nonlinear model-predictive control. Both are relatively simple, yet effective. The application on a nonlinear, pilot-scale, shell-and-tube heat exchanger reveals practicability of the techniques.
Cosmogenic photons strongly constrain UHECR source models
NASA Astrophysics Data System (ADS)
van Vliet, Arjen
2017-03-01
With the newest version of our Monte Carlo code for ultra-high-energy cosmic ray (UHECR) propagation, CRPropa 3, the flux of neutrinos and photons due to interactions of UHECRs with extragalactic background light can be predicted. Together with the recently updated data for the isotropic diffuse gamma-ray background (IGRB) by Fermi LAT, it is now possible to severely constrain UHECR source models. The evolution of the UHECR sources especially plays an important role in the determination of the expected secondary photon spectrum. Pure proton UHECR models are already strongly constrained, primarily by the highest energy bins of Fermi LAT's IGRB, as long as their number density is not strongly peaked at recent times.
NASA Astrophysics Data System (ADS)
Barlow, Jonathan S.
A direct method to design data-based model predictive controllers is presented. The design method uses system identification techniques to identify model predictive controller gains directly from a set of excitation input and disturbance corrupted output. The design is direct in that the controller gains can be designed directly from input and disturbance corrupted output data without an intermediate identification step. The direct design is simpler than previous two-step designs and reduces computation time for the design of the controller. The direct design also enables an adaptive implementation capable of identifying controller gains online. The direct data-based controllers can be used for vibration suppression, disturbance rejection, tracking and is applied to structures, robot swarms and aircraft. For the cases of vibration suppression and disturbance rejection, the data-based controller has the advantage that any disturbances present in the design data are automatically rejected without needing to know the details of the disturbances. For the case of robot swarms, extensions are made for formation control and obstacle avoidance, and the controller can be implemented as a decentralized controller in real time and in parallel on individual vehicles with communication limited to past input and past output data. A formulation for improving the robustness of the controller to parametric variations is also developed. Finally, the adaptive implementation is shown to be useful for the control of linear time-varying systems and has been successfully implemented to control a linear time-varying model of a Cruise Efficient Short Take-Off and Landing (CESTOL) type aircraft.
Shakouri, Payman; Ordys, Andrzej; Askari, Mohamad R
2012-09-01
In the design of adaptive cruise control (ACC) system two separate control loops - an outer loop to maintain the safe distance from the vehicle traveling in front and an inner loop to control the brake pedal and throttle opening position - are commonly used. In this paper a different approach is proposed in which a single control loop is utilized. The objective of the distance tracking is incorporated into the single nonlinear model predictive control (NMPC) by extending the original linear time invariant (LTI) models obtained by linearizing the nonlinear dynamic model of the vehicle. This is achieved by introducing the additional states corresponding to the relative distance between leading and following vehicles, and also the velocity of the leading vehicle. Control of the brake and throttle position is implemented by taking the state-dependent approach. The model demonstrates to be more effective in tracking the speed and distance by eliminating the necessity of switching between the two controllers. It also offers smooth variation in brake and throttle controlling signal which subsequently results in a more uniform acceleration of the vehicle. The results of proposed method are compared with other ACC systems using two separate control loops. Furthermore, an ACC simulation results using a stop&go scenario are shown, demonstrating a better fulfillment of the design requirements.
2013-01-01
Background Identification of malaria vector breeding sites can enhance control activities. Although associations between malaria vector breeding sites and topography are well recognized, practical models that predict breeding sites from topographic information are lacking. We used topographic variables derived from remotely sensed Digital Elevation Models (DEMs) to model the breeding sites of malaria vectors. We further compared the predictive strength of two different DEMs and evaluated the predictability of various habitat types inhabited by Anopheles larvae. Methods Using GIS techniques, topographic variables were extracted from two DEMs: 1) Shuttle Radar Topography Mission 3 (SRTM3, 90-m resolution) and 2) the Advanced Spaceborne Thermal Emission Reflection Radiometer Global DEM (ASTER, 30-m resolution). We used data on breeding sites from an extensive field survey conducted on an island in western Kenya in 2006. Topographic variables were extracted for 826 breeding sites and for 4520 negative points that were randomly assigned. Logistic regression modelling was applied to characterize topographic features of the malaria vector breeding sites and predict their locations. Model accuracy was evaluated using the area under the receiver operating characteristics curve (AUC). Results All topographic variables derived from both DEMs were significantly correlated with breeding habitats except for the aspect of SRTM. The magnitude and direction of correlation for each variable were similar in the two DEMs. Multivariate models for SRTM and ASTER showed similar levels of fit indicated by Akaike information criterion (3959.3 and 3972.7, respectively), though the former was slightly better than the latter. The accuracy of prediction indicated by AUC was also similar in SRTM (0.758) and ASTER (0.755) in the training site. In the testing site, both SRTM and ASTER models showed higher AUC in the testing sites than in the training site (0.829 and 0.799, respectively). The
Nonlinear model predictive control of SOFC based on a Hammerstein model
NASA Astrophysics Data System (ADS)
Huo, Hai-Bo; Zhu, Xin-Jian; Hu, Wan-Qi; Tu, Heng-Yong; Li, Jian; Yang, Jie
To protect solid oxide fuel cell (SOFC) stack and meet the voltage demand of DC type loads, two control loops are designed for controlling fuel utilization and output voltage, respectively. A Hammerstein model of the SOFC is first presented for developing effective control strategies, in which the nonlinear static part is approximated by a radial basis function neural network (RBFNN) and the linear dynamic part is modeled by an autoregressive with exogenous input (ARX) model. As we know, the output voltage of the SOFC changes with load variations. After a primary control loop is designed to keep the fuel utilization as a steady-state constant, a nonlinear model predictive control (MPC) based on the Hammerstein model is developed to control the output voltage of the SOFC. The performance of the MPC controller is compared with that of the PI controller developed in [Y.H. Li, S.S. Choi, S. Rajakaruna, An analysis of the control and operation of a solid oxide fuel-cell power plant in an isolated system, IEEE Trans. Energy Convers. 20 (2) (2005) 381-387]. Simulation results demonstrate the potential of the proposed Hammerstein model for application to the control of the SOFC, while the excellence of the nonlinear MPC controller for voltage control of the SOFC is proved.
Model Predictive Optimal Control of a Time-Delay Distributed-Parameter Systems
NASA Technical Reports Server (NTRS)
Nguyen, Nhan
2006-01-01
This paper presents an optimal control method for a class of distributed-parameter systems governed by first order, quasilinear hyperbolic partial differential equations that arise in many physical systems. Such systems are characterized by time delays since information is transported from one state to another by wave propagation. A general closed-loop hyperbolic transport model is controlled by a boundary control embedded in a periodic boundary condition. The boundary control is subject to a nonlinear differential equation constraint that models actuator dynamics of the system. The hyperbolic equation is thus coupled with the ordinary differential equation via the boundary condition. Optimality of this coupled system is investigated using variational principles to seek an adjoint formulation of the optimal control problem. The results are then applied to implement a model predictive control design for a wind tunnel to eliminate a transport delay effect that causes a poor Mach number regulation.
Mixed H2/H∞ robust model predictive control with saturated inputs
NASA Astrophysics Data System (ADS)
Huang, He; Li, Dewei; Xi, Yugeng
2014-12-01
In this paper, we investigate the mixed H2/H∞ robust model predictive control (RMPC) for polytopic uncertain systems, which refers to the infinite horizon optimal guaranteed cost control (OGCC). To fully use the capability of actuators, we adopt a saturating feedback control law as the control strategy of RMPC. As the saturating feedback control law can be effectively represented by the convex hull of a group of auxiliary linear feedback laws, the auxiliary feedback laws allow us to design the actual feedback control law without consideration of the input constraints directly to achieve the improved performance. Moreover, we suggest the relative weights on the actual and auxiliary feedback laws to the RMPC, which in turn improves the closed-loop system performance. Furthermore, an off-line design of the proposed RMPC is also developed to make it more practical. Numerical studies demonstrate the effectiveness of the proposed algorithm.
Higher Self-Control Capacity Predicts Lower Anxiety-Impaired Cognition during Math Examinations
Bertrams, Alex; Baumeister, Roy F.; Englert, Chris
2016-01-01
We assumed that self-control capacity, self-efficacy, and self-esteem would enable students to keep attentional control during tests. Therefore, we hypothesized that the three personality traits would be negatively related to anxiety-impaired cognition during math examinations. Secondary school students (N = 158) completed measures of self-control capacity, self-efficacy, and self-esteem at the beginning of the school year. Five months later, anxiety-impaired cognition during math examinations was assessed. Higher self-control capacity, but neither self-efficacy nor self-esteem, predicted lower anxiety-impaired cognition 5 months later, over and above baseline anxiety-impaired cognition. Moreover, self-control capacity was indirectly related to math grades via anxiety-impaired cognition. The findings suggest that improving self-control capacity may enable students to deal with anxiety-related problems during school tests. PMID:27065013
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.
A cerebellar model for predictive motor control tested in a brain-based device.
McKinstry, Jeffrey L; Edelman, Gerald M; Krichmar, Jeffrey L
2006-02-28
The cerebellum is known to be critical for accurate adaptive control and motor learning. We propose here a mechanism by which the cerebellum may replace reflex control with predictive control. This mechanism is embedded in a learning rule (the delayed eligibility trace rule) in which synapses onto a Purkinje cell or onto a cell in the deep cerebellar nuclei become eligible for plasticity only after a fixed delay from the onset of suprathreshold presynaptic activity. To investigate the proposal that the cerebellum is a general-purpose predictive controller guided by a delayed eligibility trace rule, a computer model based on the anatomy and dynamics of the cerebellum was constructed. It contained components simulating cerebellar cortex and deep cerebellar nuclei, and it received input from a middle temporal visual area and the inferior olive. The model was incorporated in a real-world brain-based device (BBD) built on a Segway robotic platform that learned to traverse curved paths. The BBD learned which visual motion cues predicted impending collisions and used this experience to avoid path boundaries. During learning, the BBD adapted its velocity and turning rate to successfully traverse various curved paths. By examining neuronal activity and synaptic changes during this behavior, we found that the cerebellar circuit selectively responded to motion cues in specific receptive fields of simulated middle temporal visual areas. The system described here prompts several hypotheses about the relationship between perception and motor control and may be useful in the development of general-purpose motor learning systems for machines.
Prediction of Regulation Reserve Requirements in California ISO Control Area based on BAAL Standard
Etingov, Pavel V.; Makarov, Yuri V.; Samaan, Nader A.; Ma, Jian; Loutan, Clyde
2013-07-21
This paper presents new methodologies developed at Pacific Northwest National Laboratory (PNNL) to estimate regulation capacity requirements in the California ISO control area. Two approaches have been developed: (1) an approach based on statistical analysis of actual historical area control error (ACE) and regulation data, and (2) an approach based on balancing authority ACE limit control performance standard. The approaches predict regulation reserve requirements on a day-ahead basis including upward and downward requirements, for each operating hour of a day. California ISO data has been used to test the performance of the proposed algorithms. Results show that software tool allows saving up to 30% on the regulation procurements cost .
Improving the feed-forward compensator in predictive control for setpoint tracking.
Valencia-Palomo, G; Rossiter, J A; López-Estrada, F R
2014-05-01
Simple predictive control (MPC) algorithms produce a feed-forward compensator that may be a suboptimal choice. This paper gives some insights into this issue and simple means of modifying the feed-forward to produce a more systematic and optimal design. In particular, it is shown that the optimum procedure depends upon the underlying loop tuning and also that there are, as yet under utilised, potential benefits with regard to constraint handling procedures, which helps to improve the computational efficiency of the online controller implementation. A laboratory test in a programmable logic controller (PLC) was carried out to demonstrate the code on real hardware and the effectiveness of the solution.
Output-Feedback Model Predictive Control of a Pasteurization Pilot Plant based on an LPV model
NASA Astrophysics Data System (ADS)
Karimi Pour, Fatemeh; Ocampo-Martinez, Carlos; Puig, Vicenç
2017-01-01
This paper presents a model predictive control (MPC) of a pasteurization pilot plant based on an LPV model. Since not all the states are measured, an observer is also designed, which allows implementing an output-feedback MPC scheme. However, the model of the plant is not completely observable when augmented with the disturbance models. In order to solve this problem, the following strategies are used: (i) the whole system is decoupled into two subsystems, (ii) an inner state-feedback controller is implemented into the MPC control scheme. A real-time example based on the pasteurization pilot plant is simulated as a case study for testing the behavior of the approaches.
Modulation of grasping force in prosthetic hands using neural network-based predictive control.
Pasluosta, Cristian F; Chiu, Alan W L
2015-01-01
This chapter describes the implementation of a neural network-based predictive control system for driving a prosthetic hand. Nonlinearities associated with the electromechanical aspects of prosthetic devices present great challenges for precise control of this type of device. Model-based controllers may overcome this issue. Moreover, given the complexity of these kinds of electromechanical systems, neural network-based modeling arises as a good fit for modeling the fingers' dynamics. The results of simulations mimicking potential situations encountered during activities of daily living demonstrate the feasibility of this technique.
Toward Proof of Concept of a One Health Approach to Disease Prediction and Control
Kock, Richard; Kachani, Malika; Kunkel, Rebekah; Thomas, Jason; Gilbert, Jeffrey; Wallace, Robert; Blackmore, Carina; Wong, David; Karesh, William; Natterson, Barbara; Dugas, Raymond; Rubin, Carol
2013-01-01
A One Health approach considers the role of changing environments with regard to infectious and chronic disease risks affecting humans and nonhuman animals. Recent disease emergence events have lent support to a One Health approach. In 2010, the Stone Mountain Working Group on One Health Proof of Concept assembled and evaluated the evidence regarding proof of concept of the One Health approach to disease prediction and control. Aspects examined included the feasibility of integrating human, animal, and environmental health and whether such integration could improve disease prediction and control efforts. They found evidence to support each of these concepts but also identified the need for greater incorporation of environmental and ecosystem factors into disease assessments and interventions. The findings of the Working Group argue for larger controlled studies to evaluate the comparative effectiveness of the One Health approach. PMID:24295136
Modeling a multivariable reactor and on-line model predictive control.
Yu, D W; Yu, D L
2005-10-01
A nonlinear first principle model is developed for a laboratory-scaled multivariable chemical reactor rig in this paper and the on-line model predictive control (MPC) is implemented to the rig. The reactor has three variables-temperature, pH, and dissolved oxygen with nonlinear dynamics-and is therefore used as a pilot system for the biochemical industry. A nonlinear discrete-time model is derived for each of the three output variables and their model parameters are estimated from the real data using an adaptive optimization method. The developed model is used in a nonlinear MPC scheme. An accurate multistep-ahead prediction is obtained for MPC, where the extended Kalman filter is used to estimate system unknown states. The on-line control is implemented and a satisfactory tracking performance is achieved. The MPC is compared with three decentralized PID controllers and the advantage of the nonlinear MPC over the PID is clearly shown.
Implementation of model predictive control for resistive wall mode stabilization on EXTRAP T2R
NASA Astrophysics Data System (ADS)
Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.
2015-10-01
A model predictive control (MPC) method for stabilization of the resistive wall mode (RWM) in the EXTRAP T2R reversed-field pinch is presented. The system identification technique is used to obtain a linearized empirical model of EXTRAP T2R. MPC employs the model for prediction and computes optimal control inputs that satisfy performance criterion. The use of a linearized form of the model allows for compact formulation of MPC, implemented on a millisecond timescale, that can be used for real-time control. The design allows the user to arbitrarily suppress any selected Fourier mode. The experimental results from EXTRAP T2R show that the designed and implemented MPC successfully stabilizes the RWM.
MacLean, R C
2010-03-01
Epistatic interactions between mutations are thought to play a crucial role in a number of evolutionary processes, including adaptation and sex. Evidence for epistasis is abundant, but tests of general theoretical models that can predict epistasis are lacking. In this study, I test the ability of metabolic control theory to predict epistasis using a novel experimental approach that combines phenotypic and genetic perturbations of enzymes involved in gene expression and protein synthesis in the bacterium Pseudomonas aeruginosa. These experiments provide experimental support for two key predictions of metabolic control theory: (i) epistasis between genes involved in the same pathway is antagonistic; (ii) epistasis becomes increasingly antagonistic as mutational severity increases. Metabolic control theory is a general theory that applies to any set of genes that are involved in the same linear processing chain, not just metabolic pathways, and I argue that this theory is likely to have important implications for predicting epistasis between functionally coupled genes, such as those involved in antibiotic resistance. Finally, this study highlights the fact that phenotypic manipulations of gene activity provide a powerful method for studying epistasis that complements existing genetic methods.
Longitudinal wave motion in width-constrained auxetic plates
NASA Astrophysics Data System (ADS)
Lim, Teik-Cheng
2016-05-01
This paper investigates the longitudinal wave velocity in auxetic plates in comparison to conventional ones, in which the plate is constrained from motion in the width direction. By taking into account the thickness change of the plate and its corresponding change in density, the developed wave velocity is casted not only as a function of Young’s modulus and density, but also in terms of Poisson’s ratio and longitudinal strain. Results show that density and thickness variations compensate for one another when the Poisson’s ratio is positive, but add up when the Poisson’s ratio is negative. Results also reveal that the classical model of longitudinal wave velocity for the plate is accurate when the Poisson’s ratio is about 1/3; at this Poisson’s ratio the influence from density and thickness variations cancel each other. Comparison between the current corrected model and the density-corrected Rayleigh-Lamb model reveals a number of consistent trends, while the discrepancies are elucidated. If the plate material possesses a negative Poisson’s ratio, the deviation of the actual wave velocity from the classical model becomes significant; auxeticity suppresses and enhances the wave velocity in compressive and tensile impacts, respectively. Hence the use of the corrected model is proposed when predicting longitudinal waves in width-constrained auxetic plates, and auxetic materials can be harnessed for effectively controlling wave velocities in thin-walled structures.
Wu, Hsin-Yun; Gong, Cihun-Siyong Alex; Lin, Shih-Pin; Chang, Kuang-Yi; Tsou, Mei-Yung; Ting, Chien-Kun
2016-06-01
Patient-controlled epidural analgesia (PCEA) has been applied to reduce postoperative pain in orthopedic surgical patients. Unfortunately, PCEA is occasionally accompanied by nausea and vomiting. The logistic regression (LR) model is widely used to predict vomiting, and recently support vector machines (SVM), a supervised machine learning method, has been used for classification and prediction. Unlike our previous work which compared Artificial Neural Networks (ANNs) with LR, this study uses a SVM-based predictive model to identify patients with high risk of vomiting during PCEA and comparing results with those derived from the LR-based model. From January to March 2007, data from 195 patients undergoing PCEA following orthopedic surgery were applied to develop two predictive models. 75% of the data were randomly selected for training, while the remainder was used for testing to validate predictive performance. The area under curve (AUC) was measured using the Receiver Operating Characteristic curve (ROC). The area under ROC curves of LR and SVM models were 0.734 and 0.929, respectively. A computer-based predictive model can be used to identify those who are at high risk for vomiting after PCEA, allowing for patient-specific therapeutic intervention or the use of alternative analgesic methods.
Photovoltaic power generation for air-conditioning system based on predictive control
Kim, S.; Choi, J.; Park, G.; Yoo Jiyoon
1995-12-31
In this paper an auxiliary power supply scheme using photovoltaic power generation for air-conditioning system and its novel control strategy are proposed. The proposed auxiliary power supply system employs a boost converter, a bidirectional power converter and photovoltaic arrays. The boost converter controlled by a predictive control strategy provides maximum power track (MPT) state on the photovoltaic (PV) arrays as well as power generation facility function on the ac utility grid. Furthermore the bidirectional power converter controls the power flow balance between the loads and two different power sources according to the condition of the load power and the supplied power from photovoltaic arrays. It is shown that the maximum power tracking of the PV arrays, the unit power factor of ac utility grid and the descent input dc voltage regulation of the air-conditioning system are achieved by the proposed predictive control strategy. The proposed switching strategy for the boost converter and the bidirectional power converter are based on the predictive control with ac line current and output voltage of the PV arrays. The bidirectional power converter is suitably modulation controlled to rectify the ac source during the power shortage under the poor power generation of PV arrays or over load conditions of air conditioner. During the opposite state, the bidirectional power converter is gated to function as a regeneration inverter. Controller design procedure for the proposed approach to achieve near sinusoidal input currents under the inverter mode and the rectifier mode is detailed. Simulation results on a laboratory prototype system are discussed. Experimental results from the laboratory prototype system will be presented in the near future.
Advanced Models and Controls for Prediction and Extension of Battery Lifetime (Presentation)
Smith, K.; Wood, E.; Santhanagopalan, S.; Kim, G.; Pesaran, A.
2014-02-01
Predictive models of capacity and power fade must consider a multiplicity of degradation modes experienced by Li-ion batteries in the automotive environment. Lacking accurate models and tests, lifetime uncertainty must presently be absorbed by overdesign and excess warranty costs. To reduce these costs and extend life, degradation models are under development that predict lifetime more accurately and with less test data. The lifetime models provide engineering feedback for cell, pack and system designs and are being incorporated into real-time control strategies.
NASA Astrophysics Data System (ADS)
Mirzaeva, G.; Goodwin, G. C.
2015-07-01
Inverters play a central role in modern society including renewable energy integration and motor drives. Due to the inherent switched nature of the inverter waveforms harmonic distortion is an issue. Additionally, the switching patterns are perturbed by unavoidable switching delays. Amongst those, nonlinear and load-dependent switching delays (known as inverter 'dead-time delays') are the most difficult to compensate. In this paper, we propose a new approach to delay compensation and harmonic suppression in inverter voltage. The proposed approach is based on variable prediction horizon nonlinear model predictive control.
2016-01-01
Objective: Low self-control has been linked with smoking, yet it remains unclear whether childhood self-control underlies the emergence of lifetime smoking patterns. We examined the contribution of childhood self-control to early smoking initiation and smoking across adulthood. Methods: 21,132 participants were drawn from 2 nationally representative cohort studies; the 1970 British Cohort Study (BCS) and the 1958 National Child Development Study (NCDS). Child self-control was teacher-rated at age 10 in the BCS and at ages 7 and 11 in the NCDS. Participants reported their smoking status and number of cigarettes smoked per day at 5 time-points in the BCS (ages 26–42) and 6 time-points in the NCDS (ages 23–55). Both studies controlled for socioeconomic background, cognitive ability, psychological distress, gender, and parental smoking; the NCDS also controlled for an extended set of background characteristics. Results: Early self-control made a substantial graded contribution to (not) smoking throughout life. In adjusted regression models, a 1-SD increase in self-control predicted a 6.9 percentage point lower probability of smoking in the BCS, and this was replicated in the NCDS (5.2 point reduced risk). Adolescent smoking explained over half of the association between self-control and adult smoking. Childhood self-control was positively related to smoking cessation and negatively related to smoking initiation, relapse to smoking, and the number of cigarettes smoked in adulthood. Conclusions: This study provides strong evidence that low childhood self-control predicts an increased risk of smoking throughout adulthood and points to adolescent smoking as a key pathway through which this may occur. PMID:27607137
Crane, Natania A; Jenkins, Lisanne M; Bhaumik, Runa; Dion, Catherine; Gowins, Jennifer R; Mickey, Brian J; Zubieta, Jon-Kar; Langenecker, Scott A
2017-02-01
Predicting treatment response for major depressive disorder can provide a tremendous benefit for our overstretched health care system by reducing number of treatments and time to remission, thereby decreasing morbidity. The present study used neural and performance predictors during a cognitive control task to predict treatment response (% change in Hamilton Depression Rating Scale pre- to post-treatment). Forty-nine individuals diagnosed with major depressive disorder were enrolled with intent to treat in the open-label study; 36 completed treatment, had useable data, and were included in most data analyses. Participants included in the data analysis sample received treatment with escitalopram (n = 22) or duloxetine (n = 14) for 10 weeks. Functional MRI and performance during a Parametric Go/No-go test were used to predict per cent reduction in Hamilton Depression Rating Scale scores after treatment. Haemodynamic response function-based contrasts and task-related independent components analysis (subset of sample: n = 29) were predictors. Independent components analysis component beta weights and haemodynamic response function modelling activation during Commission errors in the rostral and dorsal anterior cingulate, mid-cingulate, dorsomedial prefrontal cortex, and lateral orbital frontal cortex predicted treatment response. In addition, more commission errors on the task predicted better treatment response. Together in a regression model, independent component analysis, haemodynamic response function-modelled, and performance measures predicted treatment response with 90% accuracy (compared to 74% accuracy with clinical features alone), with 84% accuracy in 5-fold, leave-one-out cross-validation. Convergence between performance markers and functional magnetic resonance imaging, including novel independent component analysis techniques, achieved high accuracy in prediction of treatment response for major depressive disorder. The strong link to a task paradigm
Unity power factor converter based on a fuzzy controller and predictive input current.
Bouafassa, Amar; Rahmani, Lazhar; Kessal, Abdelhalim; Babes, Badreddine
2014-11-01
This paper proposes analysis and control of a single-phase power factor corrector (PFC). The proposed control is capable of achieving a unity power factor for each DC link voltage or load fluctuation. The method under study is composed of two intelligent approaches, a fuzzy logic controller to ensure an output voltage at a suitable value and predictive current control. The fuzzy controller is used with minimum rules to attain a low cost. The method is verified and discussed through simulation on the MATLAB/Simulink platform. It presents high dynamic performance under various parameter changes. Moreover, in order to examine and evaluate the method in real-time, a test bench is built using dSPACE 1104. The implantation of the proposed method is very easy and flexible and allows for operation under parameter variations. Additionally, the obtained results are very significant.
Model predictive control of a wet limestone flue gas desulfurization pilot plant
Perales, A.L.V.; Ollero, P.; Ortiz, F.J.G.; Gomez-Barea, A.
2009-06-15
A model predictive control (MPC) strategy based on a dynamic matrix (DMC) is designed and applied to a wet limestone flue gas desulfurization (WLFGD) pilot plant to evaluate what enhancement in control performance can be achieved with respect to a conventional decentralized feedback control strategy. The results reveal that MPC can significantly improve both reference tracking and disturbance rejection. For disturbance rejection, the main control objective in WLFGD plants, selection of tuning parameters and sample time, is of paramount importance due to the fast effect of the main disturbance (inlet SO{sub 2} load to the absorber) on the most important controlled variable (outlet flue gas SO{sub 2} concentration). The proposed MPC strategy can be easily applied to full-scale WLFGD plants.
Rueckert, Elmar; Čamernik, Jernej; Peters, Jan; Babič, Jan
2016-01-01
Human motor skill learning is driven by the necessity to adapt to new situations. While supportive contacts are essential for many tasks, little is known about their impact on motor learning. To study the effect of contacts an innovative full-body experimental paradigm was established. The task of the subjects was to reach for a distant target while postural stability could only be maintained by establishing an additional supportive hand contact. To examine adaptation, non-trivial postural perturbations of the subjects’ support base were systematically introduced. A novel probabilistic trajectory model approach was employed to analyze the correlation between the motions of both arms and the trunk. We found that subjects adapted to the perturbations by establishing target dependent hand contacts. Moreover, we found that the trunk motion adapted significantly faster than the motion of the arms. However, the most striking finding was that observations of the initial phase of the left arm or trunk motion (100–400 ms) were sufficient to faithfully predict the complete movement of the right arm. Overall, our results suggest that the goal-directed arm movements determine the supportive arm motions and that the motion of heavy body parts adapts faster than the light arms. PMID:27328750
Modeling and Control Systems Design by Model Predictive Control for Air-path System of Diesel Engine
NASA Astrophysics Data System (ADS)
Iwadare, Mitsuhiro; Ueno, Masaki; Hattori, Yasuharu; Adachi, Shuichi
Research has been conducted on a variety of combustion technologies in order to reduce diesel engine emissions. These technologies should precisely control the state of in-cylinder gas (EGR mass flow, air mass flow, and so on). However, because the controlled object is a multi-input, multi-output (MIMO) system and a coupled system, the use of control systems based on the conventional methods that employ PID controllers represents a challenge. Model predictive control (MPC) is well known as an MIMO algorithm. An intake control system that could be applied to the intake system of a diesel engine was constructed by supplementing MPC with a feedback function using a disturbance observer and compensator for the nonlinear characteristic of the actuators. Performance tests using an actual vehicle verified that, when applied to a two-input (throttle valve and EGR valve), two-output (air mass flow and intake chamber pressure) system, the proposed MPC is able to rapidly control each output independently to the target value.
NASA Astrophysics Data System (ADS)
Sanandaji, Borhan M.; Vincent, Tyrone L.; Colclasure, Andrew M.; Kee, Robert J.
This paper describes a systematic method for developing model-based controllers for solid-oxide fuel cell (SOFC) systems. To enhance the system efficiency and to avoid possible damages, the system must be controlled within specific operating conditions, while satisfying a load requirement. Model predictive control (MPC) is a natural choice for control implementation. However, to implement MPC, a low-order model is needed that captures the dominant dynamic behavior over the operating range. A linear parameter varying (LPV) model structure is developed and applied to obtain a control-oriented dynamic model of the SOFC stack. This approach effectively reduces a detailed physical model to a form that is compatible with MPC. The LPV structure includes nonlinear scheduling functions that blend the dynamics of locally linear models to represent nonlinear dynamic behavior over large operating ranges. Alternative scheduling variables are evaluated, with cell current being shown to be an appropriate choice. Using the reduced-order model, an MPC controller is designed that can respond to the load requirement over a wide range of operation changes while maintaining input-output variables within specified constraints. To validate the approach, the LPV-based MPC controller is applied to the high-order physical model.
Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory
Gregor P. Henze; Moncef Krarti
2003-12-17
Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building's massive structure or the use of active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this project investigates the merits of harnessing both storage media concurrently in the context of predictive optimal control. This topical report describes the demonstration of the model-based predictive optimal control for active and passive building thermal storage inventory in a test facility in real-time using time-of-use differentiated electricity prices without demand charges. The laboratory testing findings presented in this topical report cover the second of three project phases. The novel supervisory controller successfully executed a three-step procedure consisting of (1) short-term weather prediction, (2) optimization of control strategy over the next planning horizon using a calibrated building model, and (3) post-processing of the optimal strategy to yield a control command for the current time step that can be executed in the test facility. The primary and secondary building mechanical systems were effectively orchestrated by the model-based predictive optimal controller in real-time while observing comfort and operational constraints. The findings reveal that when the optimal controller is given imperfect weather fore-casts and when the building model used for planning control strategies does not match the actual building perfectly, measured utility costs savings relative to conventional building operation can be substantial. This requires that the facility under control lends itself to passive storage utilization and the building model
Evidence for the flexible sensorimotor strategies predicted by optimal feedback control.
Liu, Dan; Todorov, Emanuel
2007-08-29
Everyday movements pursue diverse and often conflicting mixtures of task goals, requiring sensorimotor strategies customized for the task at hand. Such customization is mostly ignored by traditional theories emphasizing movement geometry and servo control. In contrast, the relationship between the task and the strategy most suitable for accomplishing it lies at the core of our optimal feedback control theory of coordination. Here, we show that the predicted sensitivity to task goals affords natural explanations to a number of novel psychophysical findings. Our point of departure is the little-known fact that corrections for target perturbations introduced late in a reaching movement are incomplete. We show that this is not simply attributable to lack of time, in contradiction with alternative models and, somewhat paradoxically, in agreement with our model. Analysis of optimal feedback gains reveals that the effect is partly attributable to a previously unknown trade-off between stability and accuracy. This yields a testable prediction: if stability requirements are decreased, then accuracy should increase. We confirm the prediction experimentally in three-dimensional obstacle avoidance and interception tasks in which subjects hit a robotic target with programmable impedance. In additional agreement with the theory, we find that subjects do not rely on rigid control strategies but instead exploit every opportunity for increased performance. The modeling methodology needed to capture this extra flexibility is more general than the linear-quadratic methods we used previously. The results suggest that the remarkable flexibility of motor behavior arises from sensorimotor control laws optimized for composite cost functions.
A Numerical Process Control Method for Circular-Tube Hydroforming Prediction
Johnson, Kenneth I.; Nguyen, Ba Nghiep; Davies, Richard W.; Grant, Glenn J.; Khaleel, Mohammad A.
2004-03-01
This paper describes the development of a solution control method that tracks the stresses, strains and mechanical behavior of a tube during hydroforming to estimate the proper axial feed (end-feed) and internal pressure loads through time. The analysis uses the deformation theory of plasticity and Hill?s criterion to describe the plastic flow. Before yielding, the pressure and end-feed increments are estimated based on the initial tube geometry, elastic properties and yield stress. After yielding, the pressure increment is calculated based on the tube geometry at the previous solution increment and the current hoop stress increment. The end-feed increment is computed from the increment of the axial plastic strain. Limiting conditions such as column buckling (of long tubes), local axi-symmetric wrinkling of shorter tubes, and bursting due to localized wall thinning are considered. The process control method has been implemented in the Marc finite element code. Hydroforming simulations using this process control method were conducted to predict the load histories for controlled expansion of 6061-T4 aluminum tubes within a conical die shape and under free hydroforming conditions. The predicted loading paths were transferred to the hydroforming equipment to form the conical and free-formed tube shapes. The model predictions and experimental results are compared for deformed shape, strains and the extent of forming at rupture.
Model-based planning and real-time predictive control for laser-induced thermal therapy
Feng, Yusheng; Fuentes, David
2014-01-01
In this article, the major idea and mathematical aspects of model-based planning and real-time predictive control for laser-induced thermal therapy (LITT) are presented. In particular, a computational framework and its major components developed by authors in recent years are reviewed. The framework provides the backbone for not only treatment planning but also real-time surgical monitoring and control with a focus on MR thermometry enabled predictive control and applications to image-guided LITT, or MRgLITT. Although this computational framework is designed for LITT in treating prostate cancer, it is further applicable to other thermal therapies in focal lesions induced by radio-frequency (RF), microwave and high-intensity-focused ultrasound (HIFU). Moreover, the model-based dynamic closed-loop predictive control algorithms in the framework, facilitated by the coupling of mathematical modelling and computer simulation with real-time imaging feedback, has great potential to enable a novel methodology in thermal medicine. Such technology could dramatically increase treatment efficacy and reduce morbidity. PMID:22098360
Hood, Anna; Grange, Dorothy K; Christ, Shawn E; Steiner, Robert; White, Desirée A
2014-04-01
A number of studies have revealed significant relationships between cognitive performance and average phenylalanine (Phe) levels in children with phenylketonuria (PKU), but only a few studies have been conducted to examine the relationships between cognitive performance and variability (fluctuations) in Phe levels. In the current study, we examined a variety of indices of Phe control to determine which index best predicted IQ and executive abilities in 47 school-age children with early- and continuously-treated PKU. Indices of Phe control were mean Phe, the index of dietary control, change in Phe with age, and several indices of variability in Phe (standard deviation, standard error of estimate, and percentage of spikes). These indices were computed over the lifetime and during 3 developmental epochs (<5, 5.0-9.9, and ≥10 years of age). Results indicated that variability in Phe was generally a stronger predictor of cognitive performance than other indices of Phe control. In addition, executive performance was better predicted by variability in Phe during older than younger developmental epochs. These results indicate that variability in Phe should be carefully controlled to maximize cognitive outcomes and that Phe control should not be liberalized as children with PKU age.
Liu, Yiqi; Ganigué, Ramon; Sharma, Keshab; Yuan, Zhiguo
2016-07-01
Chemicals such as Mg(OH)2 and iron salts are widely dosed to sewage for mitigating sulfide-induced corrosion and odour problems in sewer networks. The chemical dosing rate is usually not automatically controlled but profiled based on experience of operators, often resulting in over- or under-dosing. Even though on-line control algorithms for chemical dosing in single pipes have been developed recently, network-wide control algorithms are currently not available. The key challenge is that a sewer network is typically wide-spread comprising many interconnected sewer pipes and pumping stations, making network-wide sulfide mitigation with a relatively limited number of dosing points challenging. In this paper, we propose and demonstrate an Event-driven Model Predictive Control (EMPC) methodology, which controls the flows of sewage streams containing the dosed chemical to ensure desirable distribution of the dosed chemical throughout the pipe sections of interests. First of all, a network-state model is proposed to predict the chemical concentration in a network. An EMPC algorithm is then designed to coordinate sewage pumping station operations to ensure desirable chemical distribution in the network. The performance of the proposed control methodology is demonstrated by applying the designed algorithm to a real sewer network simulated with the well-established SeweX model using real sewage flow and characteristics data. The EMPC strategy significantly improved the sulfide mitigation performance with the same chemical consumption, compared to the current practice.
Using micro saint to predict performance in a nuclear power plant control room
Lawless, M.T.; Laughery, K.R.; Persenky, J.J.
1995-09-01
The United States Nuclear Regulatory Commission (NRC) requires a technical basis for regulatory actions. In the area of human factors, one possible technical basis is human performance modeling technology including task network modeling. This study assessed the feasibility and validity of task network modeling to predict the performance of control room crews. Task network models were built that matched the experimental conditions of a study on computerized procedures that was conducted at North Carolina State University. The data from the {open_quotes}paper procedures{close_quotes} conditions were used to calibrate the task network models. Then, the models were manipulated to reflect expected changes when computerized procedures were used. These models` predictions were then compared to the experimental data from the {open_quotes}computerized conditions{close_quotes} of the North Carolina State University study. Analyses indicated that the models predicted some subsets of the data well, but not all. Implications for the use of task network modeling are discussed.
Zorza, Juan P; Marino, Julián; de Lemus, Soledad; Acosta Mesas, Alberto
2013-01-01
This study explored the predictive power of effortful control (EC) on empathy, academic performance, and social competence in adolescents. We obtained self-report measures of EC and dispositional empathy in 359 students (197 girls and 162 boys) aged between 12 and 14 years. Each student provided information about the prosocial behavior of the rest of his/her classmates and completed a sociogram. At the end of the school year, we calculated the mean grade of each student and the teacher responsible for each class completed a questionnaire on the academic skills of his/her students. The study confirmed the existence of a structural equation model (SEM) in which EC directly predicted academic performance and social competence. Additionally, empathic concern partially mediated the effect of EC on social competence. Finally, social competence significantly predicted academic performance. The article discusses the practical applications of the model proposed.
Richardson, Miles; Hunt, Thomas E; Richardson, Cassandra
2014-12-01
This paper presents a methodology to control construction task complexity and examined the relationships between construction performance and spatial and mathematical abilities in children. The study included three groups of children (N = 96); ages 7-8, 10-11, and 13-14 years. Each group constructed seven pre-specified objects. The study replicated and extended previous findings that indicated that the extent of component symmetry and variety, and the number of components for each object and available for selection, significantly predicted construction task difficulty. Results showed that this methodology is a valid and reliable technique for assessing and predicting construction play task difficulty. Furthermore, construction play performance predicted mathematical attainment independently of spatial ability.
Komulainen, Emilia; Zdrojewska, Justyna; Freemantle, Erika; Mohammad, Hasan; Kulesskaya, Natalia; Deshpande, Prasannakumar; Marchisella, Francesca; Mysore, Raghavendra; Hollos, Patrik; Michelsen, Kimmo A.; Mågard, Mats; Rauvala, Heikki; James, Peter; Coffey, Eleanor T.
2014-01-01
Genetic anomalies on the JNK pathway confer susceptibility to autism spectrum disorders, schizophrenia, and intellectual disability. The mechanism whereby a gain or loss of function in JNK signaling predisposes to these prevalent dendrite disorders, with associated motor dysfunction, remains unclear. Here we find that JNK1 regulates the dendritic field of L2/3 and L5 pyramidal neurons of the mouse motor cortex (M1), the main excitatory pathway controlling voluntary movement. In Jnk1-/- mice, basal dendrite branching of L5 pyramidal neurons is increased in M1, as is cell soma size, whereas in L2/3, dendritic arborization is decreased. We show that JNK1 phosphorylates rat HMW-MAP2 on T1619, T1622, and T1625 (Uniprot P15146) corresponding to mouse T1617, T1620, T1623, to create a binding motif, that is critical for MAP2 interaction with and stabilization of microtubules, and dendrite growth control. Targeted expression in M1 of GFP-HMW-MAP2 that is pseudo-phosphorylated on T1619, T1622, and T1625 increases dendrite complexity in L2/3 indicating that JNK1 phosphorylation of HMW-MAP2 regulates the dendritic field. Consistent with the morphological changes observed in L2/3 and L5, Jnk1-/- mice exhibit deficits in limb placement and motor coordination, while stride length is reduced in older animals. In summary, JNK1 phosphorylates HMW-MAP2 to increase its stabilization of microtubules while at the same time controlling dendritic fields in the main excitatory pathway of M1. Moreover, JNK1 contributes to normal functioning of fine motor coordination. We report for the first time, a quantitative Sholl analysis of dendrite architecture, and of motor behavior in Jnk1-/- mice. Our results illustrate the molecular and behavioral consequences of interrupted JNK1 signaling and provide new ground for mechanistic understanding of those prevalent neuropyschiatric disorders where genetic disruption of the JNK pathway is central. PMID:25309320
Constraining monodromy inflation
Peiris, Hiranya V.; Easther, Richard; Flauger, Raphael E-mail: r.easther@auckland.ac.nz
2013-09-01
We use cosmic microwave background (CMB) data from the 9-year WMAP release to derive constraints on monodromy inflation, which is characterized by a linear inflaton potential with a periodic modulation. We identify two possible periodic modulations that significantly improve the fit, lowering χ{sup 2} by approximately 10 and 20. However, standard Bayesian model selection criteria assign roughly equal odds to the modulated potential and the unmodulated case. A modulated inflationary potential can generate substantial primordial non-Gaussianity with a specific and characteristic form. For the best-fit parameters to the WMAP angular power spectrum, the corresponding non-Gaussianity might be detectable in upcoming CMB data, allowing nontrivial consistency checks on the predictions of a modulated inflationary potential.
NASA Astrophysics Data System (ADS)
Farmer, Samuel; Silver-Thorn, Barbara; Voglewede, Philip; Beardsley, Scott A.
2014-10-01
Objective. Powered robotic prostheses create a need for natural-feeling user interfaces and robust control schemes. Here, we examined the ability of a nonlinear autoregressive model to continuously map the kinematics of a transtibial prosthesis and electromyographic (EMG) activity recorded within socket to the future estimates of the prosthetic ankle angle in three transtibial amputees. Approach. Model performance was examined across subjects during level treadmill ambulation as a function of the size of the EMG sampling window and the temporal ‘prediction’ interval between the EMG/kinematic input and the model’s estimate of future ankle angle to characterize the trade-off between model error, sampling window and prediction interval. Main results. Across subjects, deviations in the estimated ankle angle from the actual movement were robust to variations in the EMG sampling window and increased systematically with prediction interval. For prediction intervals up to 150 ms, the average error in the model estimate of ankle angle across the gait cycle was less than 6°. EMG contributions to the model prediction varied across subjects but were consistently localized to the transitions to/from single to double limb support and captured variations from the typical ankle kinematics during level walking. Significance. The use of an autoregressive modeling approach to continuously predict joint kinematics using natural residual muscle activity provides opportunities for direct (transparent) control of a prosthetic joint by the user. The model’s predictive capability could prove particularly useful for overcoming delays in signal processing and actuation of the prosthesis, providing a more biomimetic ankle response.
Szabo, Judit K; Davy, Pamela J; Hooper, Michael J; Astheimer, Lee B
2009-12-01
Locusts and grasshoppers cause considerable economic damage to agriculture worldwide. The Australian Plague Locust Commission uses multiple pesticides to control locusts in eastern Australia. Avian exposure to agricultural pesticides is of conservation concern, especially in the case of rare and threatened species. The aim of this study was to evaluate the probability of pesticide exposure of native avian species during operational locust control based on knowledge of species occurrence in areas and times of application. Using presence-absence data provided by the Birds Australia Atlas for 1998 to 2002, we developed a series of generalized linear models to predict avian occurrences on a monthly basis in 0.5 degrees grid cells for 280 species over 2 million km2 in eastern Australia. We constructed species-specific models relating occupancy patterns to survey date and location, rainfall, and derived habitat preference. Model complexity depended on the number of observations available. Model output was the probability of occurrence for each species at times and locations of past locust control operations within the 5-year study period. Given the high spatiotemporal variability of locust control events, the variability in predicted bird species presence was high, with 108 of the total 280 species being included at least once in the top 20 predicted species for individual space-time events. The models were evaluated using field surveys collected between 2000 and 2005, at sites with and without locust outbreaks. Model strength varied among species. Some species were under- or over-predicted as times and locations of interest typically did not correspond to those in the prediction data set and certain species were likely attracted to locusts as a food source. Field surveys demonstrated the utility of the spatially explicit species lists derived from the models but also identified the presence of a number of previously unanticipated species. These results also emphasize
Tian, Tian; Salis, Howard M.
2015-01-01
Natural and engineered genetic systems require the coordinated expression of proteins. In bacteria, translational coupling provides a genetically encoded mechanism to control expression level ratios within multi-cistronic operons. We have developed a sequence-to-function biophysical model of translational coupling to predict expression level ratios in natural operons and to design synthetic operons with desired expression level ratios. To quantitatively measure ribosome re-initiation rates, we designed and characterized 22 bi-cistronic operon variants with systematically modified intergenic distances and upstream translation rates. We then derived a thermodynamic free energy model to calculate de novo initiation rates as a result of ribosome-assisted unfolding of intergenic RNA structures. The complete biophysical model has only five free parameters, but was able to accurately predict downstream translation rates for 120 synthetic bi-cistronic and tri-cistronic operons with rationally designed intergenic regions and systematically increased upstream translation rates. The biophysical model also accurately predicted the translation rates of the nine protein atp operon, compared to ribosome profiling measurements. Altogether, the biophysical model quantitatively predicts how translational coupling controls protein expression levels in synthetic and natural bacterial operons, providing a deeper understanding of an important post-transcriptional regulatory mechanism and offering the ability to rationally engineer operons with desired behaviors. PMID:26117546
Control theory prediction of resolved Cheyne-Stokes respiration in heart failure.
Sands, Scott A; Edwards, Bradley A; Kee, Kirk; Stuart-Andrews, Christopher; Skuza, Elizabeth M; Roebuck, Teanau; Turton, Anthony; Hamilton, Garun S; Naughton, Matthew T; Berger, Philip J
2016-11-01
Cheyne-Stokes respiration (CSR) foretells deleterious outcomes in patients with heart failure. Currently, the size of therapeutic intervention is not guided by the patient's underlying pathophysiology. In theory, the intervention needed to resolve CSR, as a control system instability (loop gain >1), can be predicted knowing the baseline loop gain and how much it falls with therapy.In 12 patients with heart failure, we administered an inspiratory carbon dioxide fraction of 1-3% during CSR (n=95 interventions) as a means to reduce loop gain. We estimated the loop gain on therapy (LGtherapy), using the baseline loop gain (using hyperpnoea length/cycle length) and its expected reduction (18% per 1% inspired carbon dioxide), and tested the specific hypothesis that LGtherapy predicts CSR persistence (LGtherapy >1) versus resolution (LGtherapy <1).As predicted, when LGtherapy >1.0, CSR continued during therapy in 23 out of 25 (92%) trials. A borderline loop gain zone (0.8
Observer-based predictive controller design with network-enhanced time-delay compensation
NASA Astrophysics Data System (ADS)
Florin Caruntu, Constantin
2015-02-01
State feedback control is very attractive due to the precise computation of the gain matrix, but the implementation of a state feedback controller is possible only when all state variables are directly measurable. This condition is almost impossible to accomplish due to the excess number of required sensors or unavailability of states for measurement in most of the practical situations. Hence, the need for an estimator or observer is obvious to estimate all the state variables by observing the input and the output of the controlled system. As such, the goal of this paper is to provide a control design methodology based on a Luenberger observer design that can assure the closed-loop performances of a vehicle drivetrain with backlash, while compensating the network-enhanced time-varying delays. This goal is achieved in a sequential manner: firstly, a piecewise linear model of two inertias drivetrain, which takes into consideration the backlash nonlinearity and the network-enhanced time-varying delay effects is derived; then, a Luenberger observer which estimates the state variables is synthesized and the robust full state-feedback predictive controller based on flexible control Lyapunov functions is designed to explicitly take into account the bounds of the disturbances caused by time-varying delays and to guarantee also the input-to-state stability of the system in a non-conservative way. The full state-feedback predictive control strategy based on the Luenberger observer design was experimentally tested on a vehicle drivetrain emulator controlled through controller area network, with the aim of minimizing the backlash effects while compensating the network-enhanced delays.
NASA Astrophysics Data System (ADS)
Tofighi, Elham; Mahdizadeh, Amin
2016-09-01
This paper addresses the problem of automatic tuning of weighting coefficients for the nonlinear model predictive control (NMPC) of wind turbines. The choice of weighting coefficients in NMPC is critical due to their explicit impact on efficiency of the wind turbine control. Classically, these weights are selected based on intuitive understanding of the system dynamics and control objectives. The empirical methods, however, may not yield optimal solutions especially when the number of parameters to be tuned and the nonlinearity of the system increase. In this paper, the problem of determining weighting coefficients for the cost function of the NMPC controller is formulated as a two-level optimization process in which the upper- level PSO-based optimization computes the weighting coefficients for the lower-level NMPC controller which generates control signals for the wind turbine. The proposed method is implemented to tune the weighting coefficients of a NMPC controller which drives the NREL 5-MW wind turbine. The results are compared with similar simulations for a manually tuned NMPC controller. Comparison verify the improved performance of the controller for weights computed with the PSO-based technique.
Prediction of Traffic Complexity and Controller Workload in Mixed Equipage NextGen Environments
NASA Technical Reports Server (NTRS)
Lee, Paul U.; Prevot, Thomas
2012-01-01
Controller workload is a key factor in limiting en route air traffic capacity. Past efforts to quantify and predict workload have resulted in identifying objective metrics that correlate well with subjective workload ratings during current air traffic control operations. Although these metrics provide a reasonable statistical fit to existing data, they do not provide a good mechanism for estimating controller workload for future air traffic concepts and environments that make different assumptions about automation, enabling technologies, and controller tasks. One such future environment is characterized by en route airspace with a mixture of aircraft equipped with and without Data Communications (Data Comm). In this environment, aircraft with Data Comm will impact controller workload less than aircraft requiring voice communication, altering the close correlation between aircraft count and controller workload that exists in current air traffic operations. This paper outlines a new trajectory-based complexity (TBX) calculation that was presented to controllers during a human-in-the-loop simulation. The results showed that TBX accurately estimated the workload in a mixed Data Comm equipage environment and the resulting complexity values were understood and readily interpreted by the controllers. The complexity was represented as a "modified aircraft account" that weighted different complexity factors and summed them in such a way that the controllers could effectively treat them as aircraft count. The factors were also relatively easy to tune without an extensive data set. The results showed that the TBX approach is well suited for presenting traffic complexity in future air traffic environments.
Fast prediction of transient stability margin in systems with SVC control and HVDC link
Tso, S.K.; Cheung, S.P.
1995-12-31
Recent developments in transient stability margin (TSM) prediction using the energy-based direct method have included excitation controllers, power system stabilizers (PSSs) and/or static VAr compensators (SVCs). These devices can be represented in their detailed dynamic models to desired degrees of complexity while the proposed extended equal-area criterion can still be effectively applied. This paper describes further development of this technique to incorporate an HVDC transmission into the test network for TSM prediction. The method is examined with a practical 17-machine power network representing the South China/Hong Kong system. An SVC control scheme is also installed in a weak bus of the test network for transient stability improvement. The results obtained show that there is no sacrifice in accuracy, speed or reliability of the TSM method with SVC and HVDC realistically incorporated into the study.
Design and analysis of a model predictive controller for active queue management.
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.
NASA Astrophysics Data System (ADS)
Woldekidan, Korbaga
This dissertation aims at developing a novel and systematic approach to apply Model Predictive Control (MPC) to improve energy efficiency and indoor environmental quality in office buildings. Model predictive control is one of the advanced optimal control approaches that use models to predict the behavior of the process beyond the current time to optimize the system operation at the present time. In building system, MPC helps to exploit buildings' thermal storage capacity and to use the information on future disturbances like weather and internal heat gains to estimate optimal control inputs ahead of time. In this research the major challenges of applying MPC to building systems are addressed. A systematic framework has been developed for ease of implementation. New methods are proposed to develop simple and yet reasonably accurate models that can minimize the MPC development effort as well as computational time. The developed MPC is used to control a detailed building model represented by whole building performance simulation tool, EnergyPlus. A co-simulation strategy is used to communicate the MPC control developed in Matlab platform with the case building model in EnergyPlus. The co-simulation tool used (MLE+) also has the ability to talk to actual building management systems that support the BACnet communication protocol which makes it easy to implement the developed MPC control in actual buildings. A building that features an integrated lighting and window control and HVAC system with a dedicated outdoor air system and ceiling radiant panels was used as a case building. Though this study is specifically focused on the case building, the framework developed can be applied to any building type. The performance of the developed MPC was compared against a baseline control strategy using Proportional Integral and Derivative (PID) control. Various conventional and advanced thermal comfort as well as ventilation strategies were considered for the comparison. These
Dretsch, Michael N.; Wood, Kimberly H.; Daniel, Thomas A.; Katz, Jeffrey S.; Deshpande, Gopikrishna; Goodman, Adam M.; Wheelock, Muriah D.; Wood, Kayli B.; Denney Jr., Thomas S.; Traynham, Stephanie; Knight, David C.
2016-01-01
Background: Prior work examining emotional dysregulation observed in posttraumatic stress disorder (PTSD) has primarily been limited to fear-learning processes specific to anticipation, habituation, and extinction of threat. In contrast, the response to threat itself has not been systematically evaluated. Objective: To explore potential disruption in fear conditioning neurocircuitry in service members with PTSD, specifically in response to predictable versus unpredictable threats. Method: In the current study, active-duty U.S. Army soldiers with (PTSD group; n = 38) and without PTSD (deployment-exposed controls; DEC; n = 40), participated in a fear-conditioning study in which threat predictability was manipulated by presenting an aversive unconditioned stimulus (UCS) that was either preceded by a conditioned stimulus (i.e., predictable) or UCS alone (i.e., unpredictable). Threat expectation, skin conductance response (SCR), and functional magnetic resonance imaging (fMRI) signal to predictable and unpredictable threats (i.e., UCS) were assessed. Results: Both groups showed greater threat expectancy and diminished threat-elicited SCRs to predictable compared to unpredictable threat. Significant group differences were observed within the amygdala, hippocampus, insula, and superior and middle temporal gyri. Contrary to our predictions, the PTSD group showed a diminished threat-related response within each of these brain regions during predictable compared to unpredictable threat, whereas the DEC group showed increased activation. Conclusion: Although, the PTSD group showed greater threat-related diminution, hypersensitivity to unpredictable threat cannot be ruled out. Furthermore, pre-trauma, trait-like factors may have contributed to group differences in activation of the neurocircuitry underpinning fear conditioning. PMID:27867434
Time-Preference Tests Fail to Predict Behavior Related to Self-control
Arfer, Kodi B.; Luhmann, Christian C.
2017-01-01
According to theory, choices relating to patience and self-control in domains as varied as drug use and retirement saving are driven by generalized preferences about delayed rewards. Past research has shown that measurements of these time preferences are associated with these choices. Research has also attempted to examine how well such measurements can predict choices, but only with inappropriate analytical methods. Moreover, it is not clear which of the many kinds of time-preference tests that have been proposed are most useful for prediction, and a theoretically important aspect of time preferences, nonstationarity, has been neglected in measurement. In Study 1, we examined three approaches to measuring time preferences with 181 users of Mechanical Turk. Retest reliability, for both immediate and 1-month intervals, was decent, as was convergent validity between tests, and association was similar to previous results, but predictive accuracy for 10 criterion variables (e.g., tobacco use) was approximately nil. In Study 2, we examined one other approach to measuring time preferences, and 40 criterion variables, using 7,127 participants in the National Longitudinal Survey of Youth 1979. Time preferences were significantly related to criterion variables, but predictive accuracy was again poor. Our findings imply serious problems for using time-preference tests to predict real-world decisions. The results of Study 1 further suggest there is little value in measuring nonstationarity separately from patience. PMID:28232810
Prediction of forces and moments for flight vehicle control effectors: Workplan
NASA Technical Reports Server (NTRS)
Maughmer, Mark D.
1989-01-01
Two research activities directed at hypersonic vehicle configurations are currently underway. The first involves the validation of a number of classical local surface inclination methods commonly employed in preliminary design studies of hypersonic flight vehicles. Unlike studies aimed at validating such methods for predicting overall vehicle aerodynamics, this effort emphasizes validating the prediction of forces and moments for flight control studies. Specifically, several vehicle configurations for which experimental or flight-test data are available are being examined. By comparing the theoretical predictions with these data, the strengths and weaknesses of the local surface inclination methods can be ascertained and possible improvements suggested. The second research effort, of significance to control during take-off and landing of most proposed hypersonic vehicle configurations, is aimed at determining the change due to ground effect in control effectiveness of highly swept delta planforms. Central to this research is the development of a vortex-lattice computer program which incorporates an unforced trailing vortex sheet and an image ground plane. With this program, the change in pitching moment of the basic vehicle due to ground proximity, and whether or not there is sufficient control power available to trim, can be determined. In addition to the current work, two different research directions are suggested for future study. The first is aimed at developing an interactive computer program to assist the flight controls engineer in determining the forces and moments generated by different types of control effectors that might be used on hypersonic vehicles. The first phase of this work would deal in the subsonic portion of the flight envelope, while later efforts would explore the supersonic/hypersonic flight regimes. The second proposed research direction would explore methods for determining the aerodynamic trim drag of a generic hypersonic flight vehicle
Advanced Control Filtering and Prediction for Phased Arrays in Directed Energy Systems
2014-07-31
SIMULINK model for prediction and feedback control of a phase ramp. Mirror represented by integrator with sample time tsim. The model shown has a...and simulating the closed-loop system in SIMULINK . Approved for public release; distribution unlimited 3 4.0 RESULTS AND DISCUSSION 4.1...although this measurement probably is not necessary. 4.2 Simulation Model There are three differences between the current SIMULINK model and the
Self-statements, locus of control, and depression in predicting self-esteem.
Philpot, V D; Holliman, W B; Madonna, S
1995-06-01
The contributions of frequency of positive and negative self-statements and their ratio, locus of control, and depression in prediction of self-esteem were examined. Volunteers were 145 college students (100 women and 45 men) who were administered the Coopersmith Self-esteem Inventory-Adult Form, Automatic Thought Questionnaire-Revised, the Beck Depression Inventory, and the Rotter Internal-External Locus of Control Scale. Intercorrelations suggested significant relationships among variables. The magnitude of the relationship was strongest between the frequency of negative self-statements and self-esteem. These results are consistent with and lend further support to prior studies of Kendall, et al. and Schwartz and Michaelson.
Buceta, David; Tojo, Concha; Vukmirovic, Miomir B.; Deepak, F. Leonard; Lopez-Quintela, M. Arturo
2015-06-02
In this study, we present a theoretical model to predict the atomic structure of Au/Pt nanoparticles synthesized in microemulsions. Excellent concordance with the experimental results shows that the structure of the nanoparticles can be controlled at sub-nanometer resolution simply by changing the reactants concentration. The results of this study not only offer a better understanding of the complex mechanisms governing reactions in microemulsions, but open up a simple new way to synthesize bimetallic nanoparticles with ad-hoc controlled nanostructures.
NASA Astrophysics Data System (ADS)
Souza, André L. G.; Ishihara, João Y.; Ferreira, Henrique C.; Borges, Renato A.; Borges, Geovany A.
2016-12-01
The present work proposes a new approach for an antenna pointing system for satellite tracking. Such a system uses the received signal to estimate the beam pointing deviation and then adjusts the antenna pointing. The present work has two contributions. First, the estimation is performed by a Kalman filter based conical scan technique. This technique uses the Kalman filter avoiding the batch estimator and applies a mathematical manipulation avoiding the linearization approximations. Secondly, a control technique based on the model predictive control together with an explicit state feedback solution are obtained in order to reduce the computational burden. Numerical examples illustrate the results.
2002-06-01
control may be used to augment an existing inner loop or may be used as a stand-alone controller. The design focuses primarily on the architecture without a stability augmentation system .... augmentation system with model predictive control used as an outer loop. The second architecture replaces the inner and outer loops with a single model...tracking is achieved through two model predictive control architectures, which are discussed. The first architecture has an inner loop stability
Correlation of Predicted and Observed Optical Properties of Multilayer Thermal Control Coatings
NASA Technical Reports Server (NTRS)
Jaworske, Donald A.
1998-01-01
Thermal control coatings on spacecraft will be increasingly important, as spacecraft grow smaller and more compact. New thermal control coatings will be needed to meet the demanding requirements of next generation spacecraft. Computer programs are now available to design optical coatings and one such program was used to design several thermal control coatings consisting of alternating layers of WO3 and SiO2. The coatings were subsequently manufactured with electron beam evaporation and characterized with both optical and thermal techniques. Optical data were collected in both the visible region of the spectrum and the infrared. Predictions of solar absorptance and infrared emittance were successfully correlated to the observed thermal control properties. Functional performance of the coatings was verified in a bench top thermal vacuum chamber.
Predicted performance benefits of an adaptive digital engine control system of an F-15 airplane
NASA Technical Reports Server (NTRS)
Burcham, F. W., Jr.; Myers, L. P.; Ray, R. J.
1985-01-01
The highly integrated digital electronic control (HIDEC) program will demonstrate and evaluate the improvements in performance and mission effectiveness that result from integrating engine-airframe control systems. Currently this is accomplished on the NASA Ames Research Center's F-15 airplane. The two control modes used to implement the systems are an integrated flightpath management mode and in integrated adaptive engine control system (ADECS) mode. The ADECS mode is a highly integrated mode in which the airplane flight conditions, the resulting inlet distortion, and the available engine stall margin are continually computed. The excess stall margin is traded for thrust. The predicted increase in engine performance due to the ADECS mode is presented in this report.
Predicted performance benefits of an adaptive digital engine control system on an F-15 airplane
NASA Technical Reports Server (NTRS)
Burcham, F. W., Jr.; Myers, L. P.; Ray, R. J.
1985-01-01
The highly integrated digital electronic control (HIDEC) program will demonstrate and evaluate the improvements in performance and mission effectiveness that result from integrating engine-airframe control systems. Currently this is accomplished on the NASA Ames Research Center's F-15 airplane. The two control modes used to implement the systems are an integrated flightpath management mode and an integrated adaptive engine control system (ADECS) mode. The ADECS mode is a highly integrated mode in which the airplane flight conditions, the resulting inlet distortion, and the available engine stall margin are continually computed. The excess stall margin is traded for thrust. The predicted increase in engine performance due to the ADECS mode is presented in this report.
Design of a generalized predictive controller for a biological wastewater treatment plant.
Sadeghassadi, M; Macnab, C J B; Westwick, D
2016-01-01
This paper presents a generalized predictive control (GPC) technique to regulate the activated sludge process found in a bioreactor used in wastewater treatment. The control strategy can track dissolved oxygen setpoint changes quickly, adapting to the system uncertainties and disturbances. Tests occur on an Activated Sludge Model No. 1 benchmark of an activated sludge process. A T filter added to the GPC framework results in an effective control strategy in the presence of coloured measurement noise. This work also suggests how a constraint on the measured variable can be added as a penalty term to the GPC framework which leads to improved control of the dissolved oxygen concentration in the presence of dynamic input disturbance.
Fertuck, Eric A.; Keilp, John; Song, Inkyung; Morris, Melissa C.; Wilson, Scott T.; Brodsky, Beth S.; Stanley, Barbara
2011-01-01
Background Non-completion of a prescribed course of treatment occurs in 20–60% of individuals diagnosed with borderline personality disorder (BPD). While symptom severity, personality traits and environmental factors have been implicated as predictors of treatment non-completion (TNC), there have been no studies of neuropsychological predictors in this population. Methods From a randomized controlled trial, a subsample of 31, unmedicated outpatients diagnosed with BPD with recent self-injurious behavior was assessed on 5 neuropsychological domains. Patients were also assessed for general IQ, demographic and other salient clinical variables. Patients were randomized to one of four treatment conditions, which lasted up to 1 year. Number of weeks in treatment (WIT) up to 1 year was utilized as the index of TNC. Results Thirty-three percent of the subsample (n = 12) did not complete 1 year of treatment. However, more WIT were predicted by better baseline executive control (Trails B; p < 0.01) and visual memory performance (Benton visual retention; p < 0.001); other neuropsychological domains did not predict WIT. Conclusion In the treatment of outpatients with BPD, better executive control and visual memory performance predict more WIT. Assessing and addressing these neurocognitive factors in treatment may reduce TNC in this high-risk population. PMID:22116411
Nandola, Naresh N.; Rivera, Daniel E.
2011-01-01
This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087
Brain activity in predictive sensorimotor control for landings: an EEG pilot study.
Baumeister, J; von Detten, S; van Niekerk, S-M; Schubert, M; Ageberg, E; Louw, Q A
2013-12-01
Landing from a jump is related to predictive sensorimotor control. Frontal, central and parietal brain areas are known to play a role in this process based on online sensory feedback. This can be measured by EEG. However, there is only limited knowledge about brain activity during predictive preparation for drop landings (DL). The purpose is to demonstrate changes in brain activity in preparation for DL in different conditions. After resting, 10 athletes performed a series of DLs and were asked to concentrate on the landing preparation for 10 s before an auditory signal required them to drop land from a 30 cm platform. This task was executed before and after a standardized fatigue protocol. EEG spectral power was calculated during DL preparation. Frontal Theta power was increased during preparation compared to rest. Parietal Alpha-2 power demonstrated higher values in preparation after fatigue condition while lower limb kinematics remained unchanged. Cortical activity in frontal and parietal brain areas is sensitive for predictive sensorimotor control of drop landings. Frontal Theta power demonstrates an increase and is related to higher attentional control. In a fatigued condition the parietal Alpha-2 power increase might be related to a deactivation in the somatosensory brain areas.
Qiu, Haixia; Kim, Michele M; Penjweini, Rozhin; Finlay, Jarod C; Busch, Theresa M; Wang, Tianhao; Guo, Wensheng; Cengel, Keith A; Simone, Charles B; Glatstein, Eli; Zhu, Timothy C
2017-01-13
This preclinical study examines light fluence, photodynamic therapy (PDT) dose and "apparent reacted singlet oxygen," [(1) O2 ]rx , to predict local control rate (LCR) for Photofrin-mediated PDT of radiation-induced fibrosarcoma (RIF) tumors. Mice bearing RIF tumors were treated with in-air fluences (50-250 J cm(-2) ) and in-air fluence rates (50-150 mW cm(-2) ) at Photofrin dosages of 5 and 15 mg kg(-1) and a drug-light interval of 24 h using a 630-nm, 1-cm-diameter collimated laser. A macroscopic model was used to calculate [(1) O2 ]rx and PDT dose based on in vivo explicit dosimetry of the drug concentration, light fluence and tissue optical properties. PDT dose and [(1) O2 ]rx were defined as a temporal integral of drug concentration and fluence rate, and singlet oxygen concentration consumed divided by the singlet oxygen lifetime, respectively. LCR was stratified for different dose metrics for 74 mice (66 + 8 control). Complete tumor control at 14 days was observed for [(1) O2 ]rx ≥ 1.1 mm or PDT dose ≥1200 μm J cm(-2) but cannot be predicted with fluence alone. LCR increases with increasing [(1) O2 ]rx and PDT dose but is not well correlated with fluence. Comparing dosimetric quantities, [(1) O2 ]rx outperformed both PDT dose and fluence in predicting tumor response and correlating with LCR.
Cell Orientation by a Microgrooved Substrate Can Be Predicted by Automatic Control Theory
Kemkemer, Ralf; Jungbauer, Simon; Kaufmann, Dieter; Gruler, Hans
2006-01-01
Cells have the ability to measure and respond to extracellular signals like chemical molecules and topographical surface features by changing their orientation. Here, we examined the orientation of cultured human melanocytes exposed to grooved topographies. To predict the cells' orientation response, we describe the cell behavior with an automatic controller model. The predicted dependence of the cell response to height and spatial frequency of the grooves is obtained by considering the symmetry of the system (cell + substrate). One basic result is that the automatic controller responds to the square of the product of groove height and spatial frequency or to the aspect ratio for symmetric grooves. This theoretical prediction was verified by the experiments, in which melanocytes were exposed to microfabricated poly(dimethylsiloxane) substrates having parallel rectangular grooves of heights (h) between 25 and 200 nm and spatial frequencies (L) between 100 and 500 mm−1. In addition, the model of the cellular automatic controller is extended to include the case of different guiding signals acting simultaneously. PMID:16581835
Jacques Loeb, B. F. Skinner, and the legacy of prediction and control.
Hackenberg, T D
1995-01-01
The biologist Jacques Loeb is an important figure in the history of behavior analysis. Between 1890 and 1915, Loeb championed an approach to experimental biology that would later exert substantial influence on the work of B. F. Skinner and behavior analysis. This paper examines some of these sources of influence, with a particular emphasis on Loeb's firm commitment to prediction and control as fundamental goals of an experimental life science, and how these goals were extended and broadened by Skinner. Both Loeb and Skinner adopted a pragmatic approach to science that put practical control of their subject matter above formal theory testing, both based their research programs on analyses of reproducible units involving the intact organism, and both strongly endorsed technological applications of basic laboratory science. For Loeb, but especially for Skinner, control came to mean something more than mere experimental or technological control for its own sake; it became synonomous with scientific understanding. This view follows from (a) the successful working model of science Loeb and Skinner inherited from Ernst Mach, in which science is viewed as human social activity, and effective practical action is taken as the basis of scientific knowledge, and (b) Skinner's analysis of scientific activity, situated in the world of direct experience and related to practices arranged by scientific verbal communities. From this perspective, prediction and control are human acts that arise from and are maintained by social circumstances in which such acts meet with effective consequences.
Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.
Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei
2016-02-01
A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.
Generalized Predictive Control of Dynamic Systems with Rigid-Body Modes
NASA Technical Reports Server (NTRS)
Kvaternik, Raymond G.
2013-01-01
Numerical simulations to assess the effectiveness of Generalized Predictive Control (GPC) for active control of dynamic systems having rigid-body modes are presented. GPC is a linear, time-invariant, multi-input/multi-output predictive control method that uses an ARX model to characterize the system and to design the controller. Although the method can accommodate both embedded (implicit) and explicit feedforward paths for incorporation of disturbance effects, only the case of embedded feedforward in which the disturbances are assumed to be unknown is considered here. Results from numerical simulations using mathematical models of both a free-free three-degree-of-freedom mass-spring-dashpot system and the XV-15 tiltrotor research aircraft are presented. In regulation mode operation, which calls for zero system response in the presence of disturbances, the simulations showed reductions of nearly 100%. In tracking mode operations, where the system is commanded to follow a specified path, the GPC controllers produced the desired responses, even in the presence of disturbances.
Low speed hybrid generalized predictive control of a gasoline-propelled car.
Romero, M; de Madrid, A P; Mañoso, C; Milanés, V
2015-07-01
Low-speed driving in traffic jams causes significant pollution and wasted time for commuters. Additionally, from the passengers׳ standpoint, this is an uncomfortable, stressful and tedious scene that is suitable to be automated. The highly nonlinear dynamics of car engines at low-speed turn its automation in a complex problem that still remains as unsolved. Considering the hybrid nature of the vehicle longitudinal control at low-speed, constantly switching between throttle and brake pedal actions, hybrid control is a good candidate to solve this problem. This work presents the analytical formulation of a hybrid predictive controller for automated low-speed driving. It takes advantage of valuable characteristics supplied by predictive control strategies both for compensating un-modeled dynamics and for keeping passengers security and comfort analytically by means of the treatment of constraints. The proposed controller was implemented in a gas-propelled vehicle to experimentally validate the adopted solution. To this end, different scenarios were analyzed varying road layouts and vehicle speeds within a private test track. The production vehicle is a commercial Citroën C3 Pluriel which has been modified to automatically act over its throttle and brake pedals.
Testing predictions from the male control theory of men's partner violence.
Bates, Elizabeth A; Graham-Kevan, Nicola; Archer, John
2014-01-01
The aim of this study was to test predictions from the male control theory of intimate partner violence (IPV) and Johnson's [Johnson, M. P. (1995). Journal of Marriage and the Family, 57, 282-294] typology. A student sample (N = 1,104) reported on their use of physical aggression and controlling behavior, to partners and to same-sex non-intimates. Contrary to the male control theory, women were found to be more physically aggressive to their partners than men were, and the reverse pattern was found for aggression to same-sex non-intimates. Furthermore, there were no substantial sex differences in controlling behavior, which significantly predicted physical aggression in both sexes. IPV was found to be associated with physical aggression to same-sex non-intimates, thereby demonstrating a link with aggression outside the family. Using Johnson's typology, women were more likely than men to be classed as "intimate terrorists," which was counter to earlier findings. Overall, these results do not support the male control theory of IPV. Instead, they fit the view that IPV does not have a special etiology, and is better studied within the context of other forms of aggression.
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.
Digging Soil Experiments for Micro Hydraulic Excavators based on Model Predictive Tracking Control
NASA Astrophysics Data System (ADS)
Tomatsu, Takumi; Nonaka, Kenichiro; Sekiguchi, Kazuma; Suzuki, Katsumasa
2016-09-01
Recently, the increase of burden to operators and lack of skilled operators are the issue in the work of the hydraulic excavator. These problems are expected to be improved by autonomous control. In this paper, we present experimental results of hydraulic excavators using model predictive control (MPC) which incorporates servo mechanism. MPC optimizes digging operations by the optimal control input which is calculated by predicting the future states and satisfying the constraints. However, it is difficult for MPC to cope with the reaction force from soil when a hydraulic excavator performs excavation. Servo mechanism suppresses the influence of the constant disturbance using the error integration. However, the bucket tip deviates from a specified shape by the sudden change of the disturbance. We can expect that the tracking performance is improved by combining MPC and servo mechanism. Path-tracking controls of the bucket tip are performed using the optimal control input. We apply the proposed method to the Komatsu- made micro hydraulic excavator PC01 by experiments. We show the effectiveness of the proposed method through the experiment of digging soil by comparing servo mechanism and pure MPC with the proposed method.
Model Predictive Control of the Current Profile and the Internal Energy of DIII-D Plasmas
NASA Astrophysics Data System (ADS)
Lauret, M.; Wehner, W.; Schuster, E.
2015-11-01
For efficient and stable operation of tokamak plasmas it is important that the current density profile and the internal energy are jointly controlled by using the available heating and current-drive (H&CD) sources. The proposed approach is a version of nonlinear model predictive control in which the input set is restricted in size by the possible combinations of the H&CD on/off states. The controller uses real-time predictions over a receding-time horizon of both the current density profile (nonlinear partial differential equation) and the internal energy (nonlinear ordinary differential equation) evolutions. At every time instant the effect of every possible combination of H&CD sources on the current profile and internal energy is evaluated over the chosen time horizon. The combination that leads to the best result, which is assessed by a user-defined cost function, is then applied up until the next time instant. Simulations results based on a control-oriented transport code illustrate the effectiveness of the proposed control method. Supported by the US DOE under DE-FC02-04ER54698 & DE-SC0010661.
NASA Astrophysics Data System (ADS)
Rhoads, Lloyd A.
This thesis builds upon recent studies focusing on modeling, operation, and control of high temperature gas cooled reactors. A computer model was developed, based on mass, energy, and momentum balances of control volumes throughout the plant. Several simulations of the plant behavior were conducted and their results were compared with those from the literature. Proportional control was combined with optimal control to form a time varying, adjustable gain predictive controller which adjusts the proportional gains during transients. The controller was designed to utilize control rod motions and bypass control valves to maintain desired plant conditions. An optimization scheme was introduced to efficiently solve the optimization problem formulated as part of the predictive controller operation. Several additional transients were run to examine the full plant controller performance. Multiple predictive controllers were designed and their performance was compared with a proportional controller throughout each transient. The predictive controller results confirmed the importance of proper selection of the optimal controller parameters, in particular the controller time step size and the horizon time. The well-designed proportional controllers clearly demonstrated improvements in plant performance during short time scale transients, namely a loss of secondary heat transfer transient and a step change in desired power transient. Results from long time scale transients demonstrated the capabilities of the proposed bypass control system to control electrical power production without the need for storage vessels.
An English language interface for constrained domains
NASA Technical Reports Server (NTRS)
Page, Brenda J.
1989-01-01
The Multi-Satellite Operations Control Center (MSOCC) Jargon Interpreter (MJI) demonstrates an English language interface for a constrained domain. A constrained domain is defined as one with a small and well delineated set of actions and objects. The set of actions chosen for the MJI is from the domain of MSOCC Applications Executive (MAE) Systems Test and Operations Language (STOL) directives and contains directives for signing a cathode ray tube (CRT) on or off, calling up or clearing a display page, starting or stopping a procedure, and controlling history recording. The set of objects chosen consists of CRTs, display pages, STOL procedures, and history files. Translation from English sentences to STOL directives is done in two phases. In the first phase, an augmented transition net (ATN) parser and dictionary are used for determining grammatically correct parsings of input sentences. In the second phase, grammatically typed sentences are submitted to a forward-chaining rule-based system for interpretation and translation into equivalent MAE STOL directives. Tests of the MJI show that it is able to translate individual clearly stated sentences into the subset of directives selected for the prototype. This approach to an English language interface may be used for similarly constrained situations by modifying the MJI's dictionary and rules to reflect the change of domain.
Predictive functional control for active queue management in congested TCP/IP networks.
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.
Adjusting to Job Demands: The Role of Work Self-Determination and Job Control in Predicting Burnout
ERIC Educational Resources Information Center
Fernet, Claude; Guay, Frederic; Senecal, Caroline
2004-01-01
This study examined the dynamic interplay among job demands, job control, and work self-determination in order to predict burnout dimensions. A three-way interaction effect was found between job demands, job control and work self-determination in predicting each dimension of burnout (emotional exhaustion, depersonalization, and personal…
NASA Astrophysics Data System (ADS)
MyoLin, Nay; Rutten, Martine; van de Giesen, Nick
2016-04-01
Flooding is a common natural disaster in the world. Construction of reservoirs, sluice gates, dikes, embankments and sea walls are implemented to minimize loss of life and property in a flood event. Rather than completely relying on large structural measures, non-structural measures such as real time control of a reservoir system can also improve flood prevention and water supply in a river basin. In this paper, we present the optimal operation of a multi-reservoir system by using Model Predictive Control (MPC) and particular attention is focused on flood mitigation of the Sittaung River Basin, Myanmar. The main challenges are non-linearity in the dynamic behavior of the water system and exponential growth of computational complexity with the state and control dimension. To deal with an issue related to non-linearity, we applied simplified internal model based on linearization scheme with a large grid length. For solving curse of dimensionality, we utilize the reduced model in which the states of the system are reduced by considering outflows from uncontrolled catchments as disturbances in the water system. We also address the computational time for real time control by using large time step scheme. Simulation results indicate that this model is able to use for real time control of a reservoir system addressing trade-offs between the multiple objectives.
Smith, Kandler; Shi, Ying; Santhanagopalan, Shriram
2015-07-29
Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under different levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.
Model Predictive Control considering Reachable Range of Wheels for Leg / Wheel Mobile Robots
NASA Astrophysics Data System (ADS)
Suzuki, Naito; Nonaka, Kenichiro; Sekiguchi, Kazuma
2016-09-01
Obstacle avoidance is one of the important tasks for mobile robots. In this paper, we study obstacle avoidance control for mobile robots equipped with four legs comprised of three DoF SCARA leg/wheel mechanism, which enables the robot to change its shape adapting to environments. Our previous method achieves obstacle avoidance by model predictive control (MPC) considering obstacle size and lateral wheel positions. However, this method does not ensure existence of joint angles which achieves reference wheel positions calculated by MPC. In this study, we propose a model predictive control considering reachable mobile ranges of wheels positions by combining multiple linear constraints, where each reachable mobile range is approximated as a convex trapezoid. Thus, we achieve to formulate a MPC as a quadratic problem with linear constraints for nonlinear problem of longitudinal and lateral wheel position control. By optimization of MPC, the reference wheel positions are calculated, while each joint angle is determined by inverse kinematics. Considering reachable mobile ranges explicitly, the optimal joint angles are calculated, which enables wheels to reach the reference wheel positions. We verify its advantages by comparing the proposed method with the previous method through numerical simulations.
Personality traits and individual differences predict threat-induced changes in postural control.
Zaback, Martin; Cleworth, Taylor W; Carpenter, Mark G; Adkin, Allan L
2015-04-01
This study explored whether specific personality traits and individual differences could predict changes in postural control when presented with a height-induced postural threat. Eighty-two healthy young adults completed questionnaires to assess trait anxiety, trait movement reinvestment (conscious motor processing, movement self-consciousness), physical risk-taking, and previous experience with height-related activities. Tests of static (quiet standing) and anticipatory (rise to toes) postural control were completed under low and high postural threat conditions. Personality traits and individual differences significantly predicted height-induced changes in static, but not anticipatory postural control. Individuals less prone to taking physical risks were more likely to lean further away from the platform edge and sway at higher frequencies and smaller amplitudes. Individuals more prone to conscious motor processing were more likely to lean further away from the platform edge and sway at larger amplitudes. Individuals more self-conscious about their movement appearance were more likely to sway at smaller amplitudes. Evidence is also provided that relationships between physical risk-taking and changes in static postural control are mediated through changes in fear of falling and physiological arousal. Results from this study may have indirect implications for balance assessment and treatment; however, further work exploring these factors in patient populations is necessary.
NASA Astrophysics Data System (ADS)
Li, Peng; Zhu, Zheng H.; Meguid, S. A.
2016-07-01
This paper studies the pulse-width pulse-frequency modulation based trajectory planning for orbital rendezvous and proximity maneuvering near a non-cooperative spacecraft in an elliptical orbit. The problem is formulated by converting the continuous control input, output from the state dependent model predictive control, into a sequence of pulses of constant magnitude by controlling firing frequency and duration of constant-magnitude thrusters. The state dependent model predictive control is derived by minimizing the control error of states and control roughness of control input for a safe, smooth and fuel efficient approaching trajectory. The resulting nonlinear programming problem is converted into a series of quadratic programming problem and solved by numerical iteration using the receding horizon strategy. The numerical results show that the proposed state dependent model predictive control with the pulse-width pulse-frequency modulation is able to effectively generate optimized trajectories using equivalent control pulses for the proximity maneuvering with less energy consumption.
NASA Astrophysics Data System (ADS)
Jassmann, U.; Dickler, S.; Zierath, J.; Hakenberg, M.; Abel, D.
2016-09-01
This contribution presents a Model Predictive Controller (MPC) with moveblocking strategy for combined power leveling and load alleviation in wind turbine operation with a focus on extreme loads. The controller is designed for a 3 MW wind turbine developed by W2E Wind to Energy GmbH and compared to a baseline controller, using a classic control scheme, which currently operates the wind turbine. All simulations are carried out with a detailed multibody simulation turbine model implemented in alaska/Wind. The performance of the two different controllers is compared using a 50-year Extreme Operation Gust event, since it is one of the main design drivers for the wind turbine considered in this work. The implemented MPC is able to level electrical output power and reduce mechanical loads at the same time. Without de-rating the achieved control results, a move-blocking strategy is utilized and allowed to reduce the computational burden of the MPC by more than 50% compared to a baseline MPC implementation. This even allows to run the MPC on a state of the art Programmable Logic Controller.
GOBF-ARMA based model predictive control for an ideal reactive distillation column.
Seban, Lalu; Kirubakaran, V; Roy, B K; Radhakrishnan, T K
2015-11-01
This paper discusses the control of an ideal reactive distillation column (RDC) using model predictive control (MPC) based on a combination of deterministic generalized orthonormal basis filter (GOBF) and stochastic autoregressive moving average (ARMA) models. Reactive distillation (RD) integrates reaction and distillation in a single process resulting in process and energy integration promoting green chemistry principles. Improved selectivity of products, increased conversion, better utilization and control of reaction heat, scope for difficult separations and the avoidance of azeotropes are some of the advantages that reactive distillation offers over conventional technique of distillation column after reactor. The introduction of an in situ separation in the reaction zone leads to complex interactions between vapor-liquid equilibrium, mass transfer rates, diffusion and chemical kinetics. RD with its high order and nonlinear dynamics, and multiple steady states is a good candidate for testing and verification of new control schemes. Here a combination of GOBF-ARMA models is used to catch and represent the dynamics of the RDC. This GOBF-ARMA model is then used to design an MPC scheme for the control of product purity of RDC under different operating constraints and conditions. The performance of proposed modeling and control using GOBF-ARMA based MPC is simulated and analyzed. The proposed controller is found to perform satisfactorily for reference tracking and disturbance rejection in RDC.
Cognitive Control Predicts Use of Model-Based Reinforcement-Learning
Otto, A. Ross; Skatova, Anya; Madlon-Kay, Seth; Daw, Nathaniel D.
2015-01-01
Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information—in the service of overcoming habitual, stimulus-driven responses—in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior. PMID:25170791
Winter, David G
2010-12-01
Several decades of research have established that implicit achievement motivation (n Achievement) is associated with success in business, particularly in entrepreneurial or sales roles. However, several political psychology studies have shown that achievement motivation is not associated with success in politics; rather, implicit power motivation often predicts political success. Having versus lacking control may be a key difference between business and politics. Case studies suggest that achievement-motivated U.S. presidents and other world leaders often become frustrated and thereby fail because of lack of control, whereas power-motivated presidents develop ways to work with this inherent feature of politics. A reevaluation of previous research suggests that, in fact, relationships between achievement motivation and business success only occur when control is high. The theme of control is also prominent in the development of achievement motivation. Cross-national data are also consistent with this analysis: In democratic industrialized countries, national levels of achievement motivation are associated with strong executive control. In countries with low opportunity for education (thus fewer opportunities to develop a sense of personal control), achievement motivation is associated with internal violence. Many of these manifestations of frustrated achievement motivation in politics resemble authoritarianism. This conclusion is tested by data from a longitudinal study of 113 male college students, showing that high initial achievement motivation combined with frustrated desires for control is related to increases in authoritarianism (F-scale scores) during the college years. Implications for the psychology of leadership and practical politics are discussed.
ERIC Educational Resources Information Center
Infurna, Frank J.; Ram, Nilam; Gerstorf, Denis
2013-01-01
Perceived control plays an important role for health across adulthood and old age. However, little is known about the factors that account for such associations and whether changes in control (or control trajectory) uniquely predict major health outcomes over and above mean levels of control. Using data from the nationwide Americans' Changing…
Folding of Small Proteins Using Constrained Molecular Dynamics
Balaraman, Gouthaman S.; Park, In-Hee; Jain, Abhinandan; Vaidehi, Nagarajan
2011-01-01
The focus of this paper is to examine whether conformational search using constrained molecular dynamics (MD) method is more enhanced and enriched towards “native-like” structures compared to all-atom MD for the protein folding as a model problem. Constrained MD methods provide an alternate MD tool for protein structure prediction and structure refinement. It is computationally expensive to perform all-atom simulations of protein folding because the processes occur on a timescale of microseconds. Compared to the all-atom MD simulation, constrained MD methods have the advantage that stable dynamics can be achieved for larger time steps and the number of degrees of freedom is an order of magnitude smaller, leading to a decrease in computational cost. We have developed a generalized constrained MD method that allows the user to “freeze and thaw” torsional degrees of freedom as fit for the problem studied. We have used this method to perform all-torsion constrained MD in implicit solvent coupled with the replica exchange method to study folding of small proteins with various secondary structural motifs such as, α-helix (polyalanine, WALP16), β-turn (1E0Q), and a mixed motif protein (Trp-cage). We demonstrate that constrained MD replica exchange method exhibits a wider conformational search than all-atom MD with increased enrichment of near native structures. “Hierarchical” constrained MD simulations, where the partially formed helical regions in the initial stretch of the all-torsion folding simulation trajectory of Trp-cage were frozen, showed a better sampling of near native structures than all-torsion constrained MD simulations. This is in agreement with the zipping-and-assembly folding model put forth by Dill and coworkers for folding proteins. The use of hierarchical “freeze and thaw” clustering schemes in constrained MD simulation can be used to sample conformations that contribute significantly to folding of proteins. PMID:21591767
Wu, Julie M.; Takahashi, Diana L; Ingram, Donald K.; Mattison, Julie A.; Roth, George; Ottinger, Mary Ann; Zelinski, Mary B.
2010-01-01
Controlled ovarian stimulation (COS) is an alternative to natural breeding in nonhuman primates; however, these protocols are costly with no guarantee of success. Toward the objective of predicting COS outcome in rhesus monkeys, the current study evaluated three clinically used ovarian reserve tests (ORTs): day 3 (d3) follicle-stimulating hormone (FSH) with d3 inhibin B (INHB), the clomiphene citrate challenge test (CCCT), and the exogenous FSH Ovarian Reserve Test (EFORT). A COS was also performed and response was classified as either successful (COS+) or unsuccessful (COS−) and retrospectively compared to ORT predictions. FSH and INHB were assessed for best hormonal index in conjunction with the aforementioned tests. INHB was consistently more accurate than FSH in all ORTs used. Overall, a modified version of the CCCT using INHB values yielded the best percentage of correct predictions. This is the first report of ORT evaluation in rhesus monkeys and may provide a useful diagnostic test prior to costly follicle stimulations, as well as predicting the onset of menopause. PMID:20336797
Prediction of forces and moments for hypersonic flight vehicle control effectors
NASA Technical Reports Server (NTRS)
Maughmer, Mark D.; Long, Lyle N.; Pagano, Peter J.
1991-01-01
Developing methods of predicting flight control forces and moments for hypersonic vehicles, included a preliminary assessment of subsonic/supersonic panel methods and hypersonic local flow inclination methods for such predictions. While these findings clearly indicate the usefulness of such methods for conceptual design activities, deficiencies exist in some areas. Thus, a second phase of research was proposed in which a better understanding is sought for the reasons of the successes and failures of the methods considered, particularly for the cases at hypersonic Mach numbers. To obtain this additional understanding, a more careful study of the results obtained relative to the methods used was undertaken. In addition, where appropriate and necessary, a more complete modeling of the flow was performed using well proven methods of computational fluid dynamics. As a result, assessments will be made which are more quantitative than those of phase 1 regarding the uncertainty involved in the prediction of the aerodynamic derivatives. In addition, with improved understanding, it is anticipated that improvements resulting in better accuracy will be made to the simple force and moment prediction.
Lowe, Michael R.; Arigo, Danielle; Butryn, Meghan L.; Gilbert, Jennifer R; Sarwer, David; Stice, Eric
2016-01-01
Objective The subjective feeling of loss of control (LOC) over eating is common among eating disordered individuals and has predicted weight gain in past research. Restrained eating and negative affect are risk factors for binge eating (which involves LOC), but intense feelings of pleasure derived from palatable foods might also predict the emergence or intensification of LOC eating. The Power of Food Scale (PFS; Lowe et al., 2009) assesses preoccupation with the pleasure derived from palatable food. Method The current sample (n = 294) comprised female college freshmen at risk for weight gain. LOC was assessed using an abbreviated version of the Eating Disorders Examination interview. LOC was assessed at baseline, 6 weeks and 6, 12 and 24 months follow-ups. Results Among those exhibiting LOC eating at baseline, (and controlling for baseline depression, restrained eating and body image dissatisfaction), those scoring higher on the PFS at baseline showed a smaller reduction in LOC frequency over time relative to those scoring lower. Using the same covariates, the PFS predicted the first emergence of LOC over two years among those showing no LOC at baseline. Conclusions These results suggest that powerful hedonic attraction to palatable foods may represent a risk factor for the maintenance of LOC in those initially experiencing it and the emergence of LOC eating in those who are not. An enhanced ability to identify individuals at increased risk of developing or maintaining LOC eating could be useful in prevention programs. PMID:26690638
Prediction of circulation control performance characteristics for Super STOL and STOL applications
NASA Astrophysics Data System (ADS)
Naqvi, Messam Abbas
due to the lack of a simple prediction capability. This research effort was focused on the creation of a rapid prediction capability of Circulation Control Aerodynamic Characteristics which could help designers with rapid performance estimates for design space exploration. A morphological matrix was created with the available set of options which could be chosen to create this prediction capability starting with purely analytical physics based modeling to high fidelity CFD codes. Based on the available constraints, and desired accuracy meta-models have been created around the two dimensional circulation control performance results computed using Navier Stokes Equations (Computational Fluid Dynamics). DSS2, a two dimensional RANS code written by Professor Lakshmi Sankar was utilized for circulation control airfoil characteristics. The CFD code was first applied to the NCCR 1510-7607N airfoil to validate the model with available experimental results. It was then applied to compute the results of a fractional factorial design of experiments array. Metamodels were formulated using the neural networks to the results obtained from the Design of Experiments. Additional validation runs were performed to validate the model predictions. Metamodels are not only capable of rapid performance prediction, but also help generate the relation trends of response matrices with control variables and capture the complex interactions between control variables. Quantitative as well as qualitative assessments of results were performed by computation of aerodynamic forces & moments and flow field visualizations. Wing characteristics in three dimensions were obtained by integration over the whole wing using Prandtl's Wing Theory. The baseline Super STOL configuration [3] was then analyzed with the application of circulation control technology. The desired values of lift and drag to achieve the target values of Takeoff & Landing performance were compared with the optimal configurations obtained
HAS-BLED Predicts Warfarin Control in Australian Patients treated for Deep Vein Thrombosis.
Mueller, Kylie; Bernaitis, Nijole; Badrick, Tony; Anoopkumar-Dukie, Shailendra
2017-03-01
The HAS-BLED model is widely utilized to assess patients' bleed risk prior to anticoagulant therapy including warfarin. Some of the variables assessed in the model are also known to influence warfarin control, commonly measured by time in therapeutic range (TTR). The aim of the study was to determine whether the HAS-BLED risk tool is a good predictor of bleed risk and warfarin control in deep vein thrombosis (DVT) patients. Retrospective data were collected for DVT warfarin care patients at Sullivan Nicolaides Pathology. Data included age, medical history and concurrent drug therapy to calculate HAS-BLED scores. INR results were used to calculate TTR with the Rosendaal method and mean TTR used for analysis and comparison. The eligible 533 patients had a mean TTR of 78.3%. Categorization according to HAS-BLED score resulted in 150 patients classified as low-risk, 331 as moderate-risk and 52 as high-risk with a haemorrhagic incidence per patient of 0.08, 0.53 and 0.54, respectively. Patients in the low-, moderate- and high-risk HAS-BLED categories had a mean TTR of 81%, 79% and 65%, respectively, with significant differences (p < 0.001) found in TTR between the low- and high-risk and moderate- and high-risk categories. In an Australian DVT population, the HAS-BLED score accurately predicts decreasing warfarin control with increasing risk category, and patients with scores ≥3 achieve poor control as indicated by a TTR <70%. In addition to predicting bleed risk, the HAS-BLED tool may also predict the potential benefit of warfarin treatment and hence influence choice of anticoagulant therapy.
A Prediction Method of TV Camera Image for Space Manual-control Rendezvous and Docking
NASA Astrophysics Data System (ADS)
Zhen, Huang; Qing, Yang; Wenrui, Wu
Space manual-control rendezvous and docking (RVD) is a key technology for accomplishing the RVD mission in manned space engineering, especially when automatic control system is out of work. The pilot on chase spacecraft manipulates the hand-stick by the image of target spacecraft captured by TV camera. From the TV image, the relative position and attitude of chase and target spacecrafts can be shown. Therefore, the size, the position, the brightness and the shadow of the target on TV camera are key to guarantee the success of manual-control RVD. A method of predicting the on-orbit TV camera image at different relative positions and light conditions during the process of RVD is discussed. Firstly, the basic principle of capturing the image of cross drone on target spacecraft by TV camera is analyzed theoretically, based which the strategy of manual-control RVD is discussed in detail. Secondly, the relationship between the displayed size or position and the real relative distance of chase and target spacecrafts is presented, the brightness and reflection by the target spacecraft at different light conditions are decribed, the shadow on cross drone caused by the chase or target spacecraft is analyzed. Thirdly, a prediction method of on-orbit TV camera images at certain orbit and light condition is provided, and the characteristics of TV camera image during the RVD is analyzed. Finally, the size, the position, the brightness and the shadow of target spacecraft on TV camera image at typical orbit is simulated. The result, by comparing the simulated images with the real images captured by the TV camera on Shenzhou manned spaceship , shows that the prediction method is reasonable
Wide-area Power System Oscillation Damping using Model Predictive Control Technique
NASA Astrophysics Data System (ADS)
Mohamed, Tarek Hassan; Abdel-Rahim, Abdel-Moamen Mohammed; Hassan, Ahmed Abd-Eltawwab; Hiyama, Takashi
This paper presents a new approach to deal with the problem of robust tuning of power system stabilizer (PSS) and automatic voltage regulator (AVR) in multi-machine power systems. The proposed method is based on a model predictive control (MPC) technique, for improvement stability of the wide-area power system with multiple generators and distribution systems including dispersed generations. The proposed method provides better damping of power system oscillations under small and large disturbances even with the inclusion of local PSSs. The effectiveness of the proposed approach is demonstrated through a two areas, four machines power system. A performance comparison between the proposed controller and some of other controllers is carried out confirming the superiority of the proposed technique. It has also been observed that the proposed algorithm can be successfully applied to larger multiarea power systems and do not suffer with computational difficulties. The proposed algorithm carried out using MATLAB/SIMULINK software package.
Integrated CFD and Controls Analysis Interface for High Accuracy Liquid Propellant Slosh Predictions
NASA Technical Reports Server (NTRS)
Marsell, Brandon; Griffin, David; Schallhorn, Paul; Roth, Jacob
2012-01-01
Coupling computational fluid dynamics (CFD) with a controls analysis tool elegantly allows for high accuracy predictions of the interaction between sloshing liquid propellants and the control system of a launch vehicle. Instead of relying on mechanical analogs which are n0t va lid during all stages of flight, this method allows for a direct link between the vehicle dynamic environments calculated by the solver in the controls analysis tool to the fluid now equations solved by the CFD code. This paper describes such a coupling methodology, presents the results of a series of test cases, and compares said results against equivalent results from extensively validated tools. The coupling methodology, described herein, has proven to be highly accurate in a variety of different cases.
NASA Technical Reports Server (NTRS)
Marsell, Brandon; Griffin, David; Schallhorn, Dr. Paul; Roth, Jacob
2012-01-01
Coupling computational fluid dynamics (CFD) with a controls analysis tool elegantly allows for high accuracy predictions of the interaction between sloshing liquid propellants and th e control system of a launch vehicle. Instead of relying on mechanical analogs which are not valid during aU stages of flight, this method allows for a direct link between the vehicle dynamic environments calculated by the solver in the controls analysis tool to the fluid flow equations solved by the CFD code. This paper describes such a coupling methodology, presents the results of a series of test cases, and compares said results against equivalent results from extensively validated tools. The coupling methodology, described herein, has proven to be highly accurate in a variety of different cases.
Model Predictive Control-based Optimal Coordination of Distributed Energy Resources
Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming; Elizondo, Marcelo A.
2013-01-07
Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive control (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.
Model Predictive Control-based Optimal Coordination of Distributed Energy Resources
Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming; Elizondo, Marcelo A.
2013-04-03
Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive control (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.
A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles
NASA Astrophysics Data System (ADS)
Gao, Yiqi; Gray, Andrew; Tseng, H. Eric; Borrelli, Francesco
2014-06-01
This paper proposes a robust control framework for lane-keeping and obstacle avoidance of semiautonomous ground vehicles. It presents a systematic way of enforcing robustness during the MPC design stage. A robust nonlinear model predictive controller (RNMPC) is used to help the driver navigating the vehicle in order to avoid obstacles and track the road centre line. A force-input nonlinear bicycle vehicle model is developed and used in the RNMPC control design. A robust invariant set is used in the RNMPC design to guarantee that state and input constraints are satisfied in the presence of disturbances and model error. Simulations and experiments on a vehicle show the effectiveness of the proposed framework.
Evaluation of Transport and Dispersion Models: A Controlled Comparison of HPAC and NARAC Predictions
Warner, S; Heagy, J F; Platt, N; Larson, D; Sugiyama, G; Nasstrom, J S; Foster, K T; Bradley, S; Bieberbach, G
2001-05-01
During fiscal year 2000, a series of studies in support of the Defense Threat Reduction Agency (DTRA) was begun. The goal of these studies is to improve the verification, validation, and accreditation (VV&A) of hazard prediction and assessment models and capabilities. These studies are part of a larger joint VV&A collaborative effort that DTRA and the Department of Energy (DOE), via the Lawrence Livermore National Laboratory (LLNL), are conducting. This joint effort includes comparisons of the LLNL and DTRA transport and dispersion (T&D) modeling systems, NARAC and HPAC, respectively. The purpose of this work is to compare, in a systematic way, HPAC and NARAC model predictions for a set of controlled hypothetical release scenarios. Only ''model-versus-model'' comparisons are addressed in this work. Model-to-field trial comparisons for HPAC and NARAC have been addressed in a recent companion study, in support of the same joint VV&A effort.
Viani, B E
2001-04-11
Representative, simplified geothermal rock-fluid systems are investigated with a modeling approach to estimate how rock water interactions affect coupled properties related to mechanical stability and permeability improvement through fracturing. First, geochemical modeling is used to determine the evolution of fluid chemistry at temperatures up to 300 C when fluids are in contact with representative rocks of continental origin. Then, a kinetic crack growth model for quartz is used to predict growth rate for subcritical cracks in acidic and basic environments. The predicted growth rate is highly sensitive to temperature and pH in the ranges tested. At present, the model is limited to situations in which quartz controls the mechanical process of interest, such as well bore stability in silica cemented rocks and the opening of quartz filled veins to enhance permeability.
Optimization of retinotopy constrained source estimation constrained by prior
Hagler, Donald J.
2015-01-01
Studying how the timing and amplitude of visual evoked responses (VERs) vary between visual areas is important for understanding visual processing but is complicated by difficulties in reliably estimating VERs in individual visual areas using non-invasive brain measurements. Retinotopy constrained source estimation (RCSE) addresses this challenge by using multiple, retinotopically-mapped stimulus locations to simultaneously constrain estimates of VERs in visual areas V1, V2, and V3, taking advantage of the spatial precision of fMRI retinotopy and the temporal resolution of magnetoencephalography (MEG) or electroencephalography (EEG). Nonlinear optimization of dipole locations, guided by a group-constrained RCSE solution as a prior, improved the robustness of RCSE. This approach facilitated the analysis of differences in timing and amplitude of VERs between V1, V2, and V3, elicited by stimuli with varying luminance contrast in a sample of eight adult humans. The V1 peak response was 37% larger than that of V2 and 74% larger than that of V3, and also ~10–20 msec earlier. Normalized contrast response functions were nearly identical for the three areas. Results without dipole optimization, or with other nonlinear methods not constrained by prior estimates were similar but suffered from greater between-subject variability. The increased reliability of estimates offered by this approach may be particularly valuable when using a smaller number of stimulus locations, enabling a greater variety of stimulus and task manipulations. PMID:23868690
Power and Performance Management in Nonlinear Virtualized Computing Systems via Predictive Control.
Wen, Chengjian; Mu, Yifen
2015-01-01
The problem of power and performance management captures growing research interest in both academic and industrial field. Virtulization, as an advanced technology to conserve energy, has become basic architecture for most data centers. Accordingly, more sophisticated and finer control are desired in virtualized computing systems, where multiple types of control actions exist as well as time delay effect, which make it complicated to formulate and solve the problem. Furthermore, because of improvement on chips and reduction of idle power, power consumption in modern machines shows significant nonlinearity, making linear power models(which is commonly adopted in previous work) no longer suitable. To deal with this, we build a discrete system state model, in which all control actions and time delay effect are included by state transition and performance and power can be defined on each state. Then, we design the predictive controller, via which the quadratic cost function integrating performance and power can be dynamically optimized. Experiment results show the effectiveness of the controller. By choosing a moderate weight, a good balance can be achieved between performance and power: 99.76% requirements can be dealt with and power consumption can be saved by 33% comparing to the case with open loop controller.
Power and Performance Management in Nonlinear Virtualized Computing Systems via Predictive Control
Wen, Chengjian; Mu, Yifen
2015-01-01
The problem of power and performance management captures growing research interest in both academic and industrial field. Virtulization, as an advanced technology to conserve energy, has become basic architecture for most data centers. Accordingly, more sophisticated and finer control are desired in virtualized computing systems, where multiple types of control actions exist as well as time delay effect, which make it complicated to formulate and solve the problem. Furthermore, because of improvement on chips and reduction of idle power, power consumption in modern machines shows significant nonlinearity, making linear power models(which is commonly adopted in previous work) no longer suitable. To deal with this, we build a discrete system state model, in which all control actions and time delay effect are included by state transition and performance and power can be defined on each state. Then, we design the predictive controller, via which the quadratic cost function integrating performance and power can be dynamically optimized. Experiment results show the effectiveness of the controller. By choosing a moderate weight, a good balance can be achieved between performance and power: 99.76% requirements can be dealt with and power consumption can be saved by 33% comparing to the case with open loop controller. PMID:26225769
Predictive transport simulations of real-time profile control in JET advanced tokamak plasmas
NASA Astrophysics Data System (ADS)
Tala, T.; Laborde, L.; Mazon, D.; Moreau, D.; Corrigan, G.; Crisanti, F.; Garbet, X.; Heading, D.; Joffrin, E.; Litaudon, X.; Parail, V.; Salmi, A.; EFDA-JET workprogramme, contributors to the
2005-09-01
Predictive, time-dependent transport simulations with a semi-empirical plasma model have been used in closed-loop simulations to control the q-profile and the strength and location of the internal transport barrier (ITB). Five transport equations (Te, Ti, q, ne, vΦ) are solved, and the power levels of lower hybrid current drive, NBI and ICRH are calculated in a feedback loop determined by the feedback controller matrix. The real-time control (RTC) technique and algorithms used in the transport simulations are identical to those implemented and used in JET experiments (Laborde L. et al 2005 Plasma Phys. Control. Fusion 47 155 and Moreau D. et al 2003 Nucl. Fusion 43 870). The closed-loop simulations with RTC demonstrate that varieties of q-profiles and pressure profiles in the ITB can be achieved and controlled simultaneously. The simulations also showed that with the same RTC technique as used in JET experiments, it is possible to sustain the q-profiles and pressure profiles close to their set-point profiles for longer than the current diffusion time. In addition, the importance of being able to handle the multiple time scales to control the location and strength of the ITB is pointed out. Several future improvements and perspectives of the RTC scheme are presented.
Physical modelling and adaptive predictive control of diffusion/LPCVD reactors
NASA Astrophysics Data System (ADS)
Dewaard, H.
1992-12-01
The aim of this study is to design a temperature controller for batch electric diffusion/low pressure chemical vapor deposition (LPCVD) furnaces, that complies with the increasingly more stringent requirements of VLSI processing. A mathematical model has been developed for batch electric diffusion/LPCVD reactors that are currently used in the semiconductor industry for the fabrication of micro-electronic devices. The model has been formulated in terms of partial integro-differential equations, which are derived from the basic energy conservation law of physics. The model takes into account the effects of radiation and conduction. Chapter 2 gives a detailed description of the furnace system and provides some insight into the processes that take place. In chapter 3, the model of the diffusion/LPPCVD furnace is derived. Chapter 4 deals with the design of a temperature control system for the diffusion/LPCVD reactor, that makes use of the model as developed in chapter 3. Chapter 5 gives the results of the control designs, both of simulation and of application on a real furnace. Results of the linear quadratic Gaussian controller, the (non-adaptive) reduced order controller, and the adaptive predictive controller are presented. Finally, in chapter 6, some conclusions are drawn and suggestions for further research are given.
Control Aspects of Highly Constrained Guidance Techniques
1978-02-01
optimized design, TFR = 2 ................................................ 151 78-1 STEER, fuel depletion steering, optimized design, integral feedback, TFR...2 .............................. 152 78-2 STEER, fuel depletion steering, optimized design, integral feedback, TFR = 2...153 !9 List of Figures (Cont.) Figure Page 79-1 STEER, fuel depletion steering, optimized design, integral feedback, AIRSX, TFR = 2
Optimal Control Strategies for Constrained Relative Orbits
2007-09-01
in the next chapter. 50 IV. The Optimal Trajectory The optimal trajectory will be the output of a nonlinear programming algo- rithm that searches for...surface and watch the iteration path of the nonlinear programming algorithm. Let ψ1 = π 4 The results of the optimization algorithm for each of the...T̃max 170 since kz is an integer kz = ⌈ T̃T T̃max ⌉ (134) where d e represents the ceiling function. The total ∆V expended performing these optimal
Improved model predictive control of resistive wall modes by error field estimator in EXTRAP T2R
NASA Astrophysics Data System (ADS)
Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.
2016-12-01
Many implementations of a model-based approach for toroidal plasma have shown better control performance compared to the conventional type of feedback controller. One prerequisite of model-based control is the availability of a control oriented model. This model can be obtained empirically through a systematic procedure called system identification. Such a model is used in this work to design a model predictive controller to stabilize multiple resistive wall modes in EXTRAP T2R reversed-field pinch. Model predictive control is an advanced control method that can optimize the future behaviour of a system. Furthermore, this paper will discuss an additional use of the empirical model which is to estimate the error field in EXTRAP T2R. Two potential methods are discussed that can estimate the error field. The error field estimator is then combined with the model predictive control and yields better radial magnetic field suppression.
Evolution was chemically constrained.
Williams, R J P; Fraústo Da Silva, J J R
2003-02-07
The objective of this paper is to present a systems view of the major features of biological evolution based upon changes in internal chemistry and uses of cellular space, both of which it will be stated were dependent on the changing chemical environment. The account concerns the major developments from prokaryotes to eukaryotes, to multi-cellular organisms, to animals with nervous systems and a brain, and finally to human beings and their uses of chemical elements in space outside themselves. It will be stated that the changes were in an inevitable progression, and were not just due to blind chance, so that "random searching" by a coded system to give species had a fixed overall route. The chemical sequence is from a reducing to an ever-increasingly oxidizing environment, while organisms retained reduced chemicals. The process was furthered recently by human beings who have also increased the range of reduced products trapped on Earth in novel forms. All the developments are brought about from the nature of the chemicals which organisms accumulate using the environment and its changes. The relationship to the manner in which particular species (gene sequences) were coincidentally changed, the molecular view of evolution, is left for additional examination. There is a further issue in that the changes of the chemistry of the environment developed largely at equilibrium due to the relatively fast reactions there of the available inorganic chemicals. Inside cells, some of these same chemicals also came to equilibrium within compounds. All such equilibria reduced the variance (degrees of freedom) of the total environmental/biological system and its possible development. However, the more sophisticated organic chemistry, almost totally inside cells until humans evolved, is kinetically controlled and limited by the demands of cellular reduction necessary to produce essential chemicals and by the availability of certain elements and energy. Hence the variability of
Constrained minimization of smooth functions using a genetic algorithm
NASA Technical Reports Server (NTRS)
Moerder, Daniel D.; Pamadi, Bandu N.
1994-01-01
The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.
NASA Astrophysics Data System (ADS)
Zehe, E.; Sivapalan, M.
2008-11-01
The aim of this paper is to provide evidence that the dynamics of hydrological systems and geo-ecosystems is often influenced by threshold behavior at a variety of space and time scales. Based on well known characteristics of elementary threshold phenomena we suggest criteria for detecting threshold behavior in hydrological systems. The most important one is intermittence of phenomena, i.e. the rapid switching of related state variables/fluxes from zero to finite values, or existence of behavior regimes where the same process/response appears qualitatively differently at the macroscopic level. From the literature we present several examples for intermittent hydrological phenomena, ranging from overland flow generation in different landscapes, including the effects of hydrophobicity, to soil water flow occurring in the matrix continuum or via preferential pathways, including the case of cracking soils, nonlinear subsurface stormflow response of hillslopes during severe rainfall events, and long-term catchment flooding responses. Since threshold phenomena are often associated with environmental hazards such as floods, soil erosion, and contamination of shallow groundwater resources, we discuss common difficulties that complicate predictions of whether or not they might even occur. Predicting the onset of threshold phenomena requires a thorough understanding of the underlying controls. Through examples we illustrate that threshold behavior in hydrological systems can manifest at (a) the process level, (b) the response level, and (c) the functional level, and explain that the complexity of the underlying controls and of the interacting phenomena that determine threshold behavior become increasingly complex at the higher levels. Finally we provide evidence from field observations and model predictions that show that within an "unstable range" of system states "close" to a threshold, it is difficult to predict whether or not the system will switch behavior, for instance
Models of basal ganglia and cerebellum for sensorimotor integration and predictive control
NASA Astrophysics Data System (ADS)
Jabri, Marwan A.; Huang, Jerry; Coenen, Olivier J. D.; Sejnowski, Terrence J.
2000-10-01
This paper presents a sensorimotor architecture integrating computational models of a cerebellum and a basal ganglia and operating on a microrobot. The computational models enable a microrobot to learn to track a moving object and anticipate future positions using a CCD camera. The architecture features pre-processing modules for coordinate transformation and instantaneous orientation extraction. Learning of motor control is implemented using predictive Hebbian reinforcement-learning algorithm in the basal ganglia model. Learning of sensory predictions makes use of a combination of long-term depression (LTD) and long-term potentiation (LTP) adaptation rules within the cerebellum model. The basal ganglia model uses the visual inputs to develop sensorimotor mapping for motor control, while the cerebellum module uses robot orientation and world- coordinate transformed inputs to predict the location of the moving object in a robot centered coordinate system. We propose several hypotheses about the functional role of cell populations in the cerebellum and argue that mossy fiber projections to the deep cerebellar nucleus (DCN) could play a coordinate transformation role and act as gain fields. We propose that such transformation could be learnt early in the brain development stages and could be guided by the activity of the climbing fibers. Proprioceptor mossy fibers projecting to the DCN and providing robot orientation with respect to a reference system could be involved in this case. Other mossy fibers carrying visual sensory input provide visual patterns to the granule cells. The combined activities of the granule and the Purkinje cells store spatial representations of the target patterns. The combinations of mossy and Purkinje projections to the DCN provide a prediction of the location of the moving target taking into consideration the robot orientation. Results of lesion simulations based on our model show degradations similar to those reported in cerebellar lesion