Science.gov

Sample records for predictive control l-mpc

  1. Stable predictive control horizons

    NASA Astrophysics Data System (ADS)

    Estrada, Raúl; Favela, Antonio; Raimondi, Angelo; Nevado, Antonio; Requena, Ricardo; Beltrán-Carbajal, Francisco

    2012-04-01

    The stability theory of predictive and adaptive predictive control for processes of linear and stable nature is based on the hypothesis of a physically realisable driving desired trajectory (DDT). The formal theoretical verification of this hypothesis is trivial for processes with a stable inverse, but it is not for processes with an unstable inverse. The extended strategy of predictive control was developed with the purpose of overcoming methodologically this stability problem and it has delivered excellent performance and stability in its industrial applications given a suitable choice of the prediction horizon. From a theoretical point of view, the existence of a prediction horizon capable of ensuring stability for processes with an unstable inverse was proven in the literature. However, no analytical solution has been found for the determination of the prediction horizon values which guarantee stability, in spite of the theoretical and practical interest of this matter. This article presents a new method able to determine the set of prediction horizon values which ensure stability under the extended predictive control strategy formulation and a particular performance criterion for the design of the DDT generically used in many industrial applications. The practical application of this method is illustrated by means of simulation examples.

  2. 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.

  3. 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.

  4. Inhibitory Control Predicts Grammatical Ability.

    PubMed

    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.

  5. Inhibitory Control Predicts Grammatical Ability

    PubMed Central

    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

  6. Predictive control and estimation - State space approach

    NASA Technical Reports Server (NTRS)

    Gawronski, W.

    1991-01-01

    A modified output prediction procedure and a new controller design based on the predictive control law are presented. A new predictive estimator enhances system performance. The predictive controller was designed and applied to the tracking control of the NASA/JPL 70-m antenna. Simulation results show significant improvement in tracking performance over the linear quadratic controller and estimator presently in use.

  7. Adaptive, predictive controller for optimal process control

    SciTech Connect

    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.

  8. Data-Based Predictive Control with Multirate Prediction Step

    NASA Technical Reports Server (NTRS)

    Barlow, Jonathan S.

    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. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.

  9. 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.

  10. Predictive Control of Speededness in Adaptive Testing

    ERIC Educational Resources Information Center

    van der Linden, Wim J.

    2009-01-01

    An adaptive testing method is presented that controls the speededness of a test using predictions of the test takers' response times on the candidate items in the pool. Two different types of predictions are investigated: posterior predictions given the actual response times on the items already administered and posterior predictions that use the…

  11. 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.

  12. Controlling and Predicting Unpredictable Behavior.

    PubMed

    de Souza Barba, Lourenço

    2015-05-01

    Behaving predictably can be advantageous in some situations, but unpredictability can also be advantageous in some competitive situations like sports, games, and war. Can, however, unpredictable behavior be conditioned? If a contingency of reinforcement based upon the predictability of behavior generates unpredictable responding, is it possible to conclude that predictability is itself a reinforceable dimension of behavior? In this paper, I address these questions by examining the concept and measures of predictability and the procedures generally used to increase unpredictable responding. I discuss the hypothesis that contingencies based on response frequency shape the generalized operant "to vary" and an alternative hypothesis that such contingencies generate unpredictable responding by balancing the strength of each alternative response over time. I discuss the findings that support the balance hypothesis as well as its limitations. I conclude that the two alternative hypotheses may be complementary in explaining unpredictable responding. PMID:27606162

  13. 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.

  14. Prediction, Control and the Challenge to Complexity

    ERIC Educational Resources Information Center

    Radford, Mike

    2008-01-01

    The dominant discourse in research, management and teaching is one that may loosely be characterised as that of prediction and control. The objective of research is to identify causal correlations within policy, management, teaching strategies and educational outcomes that are sufficiently robust as to be able to predict outcomes and make…

  15. A Course in... Model Predictive Control.

    ERIC Educational Resources Information Center

    Arkun, Yaman; And Others

    1988-01-01

    Describes a graduate engineering course which specializes in model predictive control. Lists course outline and scope. Discusses some specific topics and teaching methods. Suggests final projects for the students. (MVL)

  16. 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

  17. 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.

  18. Neural predictive control for active buffet alleviation

    NASA Astrophysics Data System (ADS)

    Pado, Lawrence E.; Lichtenwalner, Peter F.; Liguore, Salvatore L.; Drouin, Donald

    1998-06-01

    The adaptive neural control of aeroelastic response (ANCAR) and the affordable loads and dynamics independent research and development (IRAD) programs at the Boeing Company jointly examined using neural network based active control technology for alleviating undesirable vibration and aeroelastic response in a scale model aircraft vertical tail. The potential benefits of adaptive control includes reducing aeroelastic response associated with buffet and atmospheric turbulence, increasing flutter margins, and reducing response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and thus loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Wind tunnel tests were undertaken on a rigid 15% scale aircraft in Boeing's mini-speed wind tunnel, which is used for testing at very low air speeds up to 80 mph. The model included a dynamically scaled flexible fail consisting of an aluminum spar with balsa wood cross sections with a hydraulically powered rudder. Neural predictive control was used to actuate the vertical tail rudder in response to strain gauge feedback to alleviate buffeting effects. First mode RMS strain reduction of 50% was achieved. The neural predictive control system was developed and implemented by the Boeing Company to provide an intelligent, adaptive control architecture for smart structures applications with automated synthesis, self-optimization, real-time adaptation, nonlinear control, and fault tolerance capabilities. It is designed to solve complex control problems though a process of automated synthesis, eliminating costly control design and surpassing it in many instances by accounting for real world non-linearities.

  19. 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

  20. 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.

  1. Optimization approaches to nonlinear model predictive control

    SciTech Connect

    Biegler, L.T. . Dept. of Chemical Engineering); Rawlings, J.B. . Dept. of Chemical Engineering)

    1991-01-01

    With the development of sophisticated methods for nonlinear programming and powerful computer hardware, it now becomes useful and efficient to formulate and solve nonlinear process control problems through on-line optimization methods. This paper explores and reviews control techniques based on repeated solution of nonlinear programming (NLP) problems. Here several advantages present themselves. These include minimization of readily quantifiable objectives, coordinated and accurate handling of process nonlinearities and interactions, and systematic ways of dealing with process constraints. We motivate this NLP-based approach with small nonlinear examples and present a basic algorithm for optimization-based process control. As can be seen this approach is a straightforward extension of popular model-predictive controllers (MPCs) that are used for linear systems. The statement of the basic algorithm raises a number of questions regarding stability and robustness of the method, efficiency of the control calculations, incorporation of feedback into the controller and reliable ways of handling process constraints. Each of these will be treated through analysis and/or modification of the basic algorithm. To highlight and support this discussion, several examples are presented and key results are examined and further developed. 74 refs., 11 figs.

  2. Optimal Control of Distributed Energy Resources using Model Predictive Control

    SciTech Connect

    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.

  3. Cascade generalized predictive control strategy for boiler drum level.

    PubMed

    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.

  4. 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

  5. Predictive controller and estimator for NASA Deep Space Network antennas

    NASA Technical Reports Server (NTRS)

    Gawronski, W.

    1992-01-01

    A new design procedure is presented for a predictive controller that significantly improves antenna tracking performance. The predictive controller uses future values of the stored output command to generate the control signal. For antennas tracking stars or spacecraft, these values are known in advance, hence the predictive control scheme is easily implemented in this case. The predictive controller is designed for tracking control of the the NASA/JPL 70-m antenna. On-axis tracking is considered, where the output is taken on the encoder, or tachometer. Simulation results show a significant improvement in performance over the LQ controller.

  6. 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.

  7. 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.

  8. Precise flight-path control using a predictive algorithm

    NASA Technical Reports Server (NTRS)

    Hess, R. A.; Jung, Y. C.

    1991-01-01

    Generalized predictive control describes an algorithm for the control of dynamic systems in which a control input is generated that minimizes a quadratic cost function consisting of a weighted sum of errors between desired and predicted future system output and future predicted control increments. The output predictions are obtained from an internal model of the plant dynamics. A design technique is discussed for applying the single-input/single-output generalized predictive control algorithm to a problem of longitudinal/vertical terrain-following flight of a rotorcraft. By using the generalized predictive control technique to provide inputs to a classically designed stability and control augmentation system, it is demonstrated that a robust flight-path control system can be created that exhibits excellent tracking performance.

  9. Voltage control in pulsed system by predict-ahead control

    DOEpatents

    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.

  10. Voltage control in pulsed system by predict-ahead control

    DOEpatents

    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.

  11. Comparison of Predictive Control Methods for High Consumption Industrial Furnace

    PubMed Central

    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. PMID:24319354

  12. Comparison of predictive control methods for high consumption industrial furnace.

    PubMed

    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.

  13. Predictability Influences Stopping and Response Control

    ERIC Educational Resources Information Center

    Morein-Zamir, Sharon; Chua, Romeo; Franks, Ian; Nagelkerke, Paul; Kingstone, Alan

    2007-01-01

    Using a continuous tracking task, the authors examined whether stopping is resistant to expectancies as well as whether it is a representative measure of response control. Participants controlled the speed of a moving marker by continuously adjusting their response force. Participants stopped their ongoing tracking in response to auditory signals…

  14. 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.

  15. Prospects for earthquake prediction and control

    USGS Publications Warehouse

    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.

  16. 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.

  17. 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.

  18. 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.

  19. Algorithm for predictive control implementation on fiber optic transmission lines

    NASA Astrophysics Data System (ADS)

    Andreev, Vladimir A.; Burdin, Vladimir A.; Voronkov, Andrey A.

    2014-04-01

    This paper presents the algorithm for predictive control implementation on fiber-optic transmission lines. In order to improve the maintenance of fiber optic communication lines, the algorithm prediction uptime optic communication cables have been worked out. It considers the results of scheduled preventive maintenance and database of various works on the track cable line during maintenance.

  20. Neural network learning of optimal Kalman prediction and control.

    PubMed

    Linsker, Ralph

    2008-11-01

    Although there are many neural network (NN) algorithms for prediction and for control, and although methods for optimal estimation (including filtering and prediction) and for optimal control in linear systems were provided by Kalman in 1960 (with nonlinear extensions since then), there has been, to my knowledge, no NN algorithm that learns either Kalman prediction or Kalman control (apart from the special case of stationary control). Here we show how optimal Kalman prediction and control (KPC), as well as system identification, can be learned and executed by a recurrent neural network composed of linear-response nodes, using as input only a stream of noisy measurement data. The requirements of KPC appear to impose significant constraints on the allowed NN circuitry and signal flows. The NN architecture implied by these constraints bears certain resemblances to the local-circuit architecture of mammalian cerebral cortex. We discuss these resemblances, as well as caveats that limit our current ability to draw inferences for biological function. It has been suggested that the local cortical circuit (LCC) architecture may perform core functions (as yet unknown) that underlie sensory, motor, and other cortical processing. It is reasonable to conjecture that such functions may include prediction, the estimation or inference of missing or noisy sensory data, and the goal-driven generation of control signals. The resemblances found between the KPC NN architecture and that of the LCC are consistent with this conjecture.

  1. Towards feasible and effective predictive wavefront control for adaptive optics

    SciTech Connect

    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.

  2. 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.

  3. Multi-objective optimization for model predictive control.

    PubMed

    Wojsznis, Willy; Mehta, Ashish; Wojsznis, Peter; Thiele, Dirk; Blevins, Terry

    2007-06-01

    This paper presents a technique of multi-objective optimization for Model Predictive Control (MPC) where the optimization has three levels of the objective function, in order of priority: handling constraints, maximizing economics, and maintaining control. The greatest weights are assigned dynamically to control or constraint variables that are predicted to be out of their limits. The weights assigned for economics have to out-weigh those assigned for control objectives. Control variables (CV) can be controlled at fixed targets or within one- or two-sided ranges around the targets. Manipulated Variables (MV) can have assigned targets too, which may be predefined values or current actual values. This MV functionality is extremely useful when economic objectives are not defined for some or all the MVs. To achieve this complex operation, handle process outputs predicted to go out of limits, and have a guaranteed solution for any condition, the technique makes use of the priority structure, penalties on slack variables, and redefinition of the constraint and control model. An engineering implementation of this approach is shown in the MPC embedded in an industrial control system. The optimization and control of a distillation column, the standard Shell heavy oil fractionator (HOF) problem, is adequately achieved with this MPC. PMID:17382946

  4. 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.

  5. 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.

  6. Optimized continuous pharmaceutical manufacturing via model-predictive control.

    PubMed

    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. PMID:27317987

  7. Optimized continuous pharmaceutical manufacturing via model-predictive control.

    PubMed

    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.

  8. Model predictive torque control with an extended prediction horizon for electrical drive systems

    NASA Astrophysics Data System (ADS)

    Wang, Fengxiang; Zhang, Zhenbin; Kennel, Ralph; Rodríguez, José

    2015-07-01

    This paper presents a model predictive torque control method for electrical drive systems. A two-step prediction horizon is achieved by considering the reduction of the torque ripples. The electromagnetic torque and the stator flux error between predicted values and the references, and an over-current protection are considered in the cost function design. The best voltage vector is selected by minimising the value of the cost function, which aims to achieve a low torque ripple in two intervals. The study is carried out experimentally. The results show that the proposed method achieves good performance in both steady and transient states.

  9. 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.

  10. Self-Control Assessments and Implications for Predicting Adolescent Offending.

    PubMed

    Fine, Adam; Steinberg, Laurence; Frick, Paul J; Cauffman, Elizabeth

    2016-04-01

    Although low self-control is consistently related to adolescent offending, it is unknown whether self-report measures or laboratory behavior tasks yield better predictive utility, or if a combination yields incremental predictive power. This is particularly important because developmental theory indicates that self-control is related to adolescent offending and, consequently, risk assessments rely on self-control measures. The present study (a) examines relationships between self-reported self-control on the Weinberger Adjustment Inventory with Go/No-Go response inhibition, and (b) compares the predictive utility of both assessment strategies for short- and long-term adolescent reoffending. It uses longitudinal data from the Crossroads Study of male, first-time adolescent offenders ages 13-17 (N = 930; 46 % Hispanic/Latino, 37 % Black/African-American, 15 % non-Hispanic White, 2 % other race). The results of the study indicate that the measures are largely unrelated, and that the self-report measure is a better indicator of both short- and long-term reoffending. The laboratory task measure does not add value to what is already predicted by the self-report measure. Implications for assessing self-control during adolescence and consequences of assessment strategy are discussed. PMID:26792266

  11. 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.

  12. 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.

  13. Stabilisation of difference equations with noisy prediction-based control

    NASA Astrophysics Data System (ADS)

    Braverman, E.; Kelly, C.; Rodkina, A.

    2016-07-01

    We consider the influence of stochastic perturbations on stability of a unique positive equilibrium of a difference equation subject to prediction-based control. These perturbations may be multiplicative We begin by relaxing the control parameter in the deterministic equation, and deriving a range of values for the parameter over which all solutions eventually enter an invariant interval. Then, by allowing the variation to be stochastic, we derive sufficient conditions (less restrictive than known ones for the unperturbed equation) under which the positive equilibrium will be globally a.s. asymptotically stable: i.e. the presence of noise improves the known effectiveness of prediction-based control. Finally, we show that systemic noise has a "blurring" effect on the positive equilibrium, which can be made arbitrarily small by controlling the noise intensity. Numerical examples illustrate our results.

  14. 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.

  15. 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.

  16. Automaticity and cognitive control in the learned predictiveness effect.

    PubMed

    Shone, Lauren T; Harris, Irina M; Livesey, Evan J

    2015-01-01

    In novel contexts, learning is biased toward cues previously experienced as predictive compared with cues previously experienced as nonpredictive. This is known as learned predictiveness. A recent finding has shown that instructions issued about the causal status of cues influences the expression of learned predictiveness, suggesting that controlled, volitional processes play a role in this effect. Three experiments are reported further investigating the effects of instructional manipulations on learned predictiveness. Experiment 1 confirms the influence of inferential processes, extending previous work to suggest that instructions affect associative memory as well as causal reasoning. Experiments 2 and 3 used a procedure designed to tease apart inferential and automatic contributions to the bias by presenting instructed causes that were previously predictive and previously nonpredictive. The results demonstrate that the prior predictiveness of cues influences subsequent learning over and above the effect of explicit instruction. However, it appears that the relationship between explicit instruction and predictive history is interactive rather than additive. Potential explanations for this interactivity are discussed.

  17. Predicting asthma control: the role of psychological triggers.

    PubMed

    Ritz, Thomas; Bobb, Carol; Griffiths, Chris

    2014-01-01

    Asthma triggers have been linked to adverse health outcomes in asthma, but little is known about their association with asthma control. Because trigger avoidance is an integral part of successful asthma management, psychological triggers in particular may be associated with suboptimal asthma control, given the difficulty of controlling them. We examined cross-sectional and longitudinal associations of perceived asthma triggers with self-report of asthma control impairment, symptoms, and spirometric lung function (forced expiratory volume in the 1st second, [FEV1]) in 179 adult primary care asthma patients. Perceived asthma triggers explained up to 42.5% of the variance in asthma control and symptoms, but not in FEV1 alone. Allergic triggers explained up to 12.1% of the asthma control and symptom variance, three nonallergic trigger types, air pollution/irritants, physical activity, and infection, explained up to 26.2% over and above allergic triggers, and psychological triggers up to 9.5% over and above all other triggers. Psychological triggers alone explained up to 33.9% of the variance and were the only trigger class that was consistently significant in all final multiple regression models predicting control and symptoms. Psychological triggers also predicted lower asthma control 3-6 months later, although controlling for initial asthma control eliminated this association. In free reports of individually relevant triggers, only psychological triggers were associated with suboptimal asthma control. Trigger factors are important predictors of self-reported asthma control and symptoms but not actual lung function. Particular attention should be directed to psychological triggers as indicators of patients' perceptions of suboptimal asthma control.

  18. Adaptive model predictive process control using neural networks

    DOEpatents

    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.

  19. Adaptive model predictive process control using neural networks

    DOEpatents

    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.

  20. 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.

  1. Motivation to control prejudice predicts categorization of multiracials.

    PubMed

    Chen, Jacqueline M; Moons, Wesley G; Gaither, Sarah E; Hamilton, David L; Sherman, Jeffrey W

    2014-05-01

    Multiracial individuals often do not easily fit into existing racial categories. Perceivers may adopt a novel racial category to categorize multiracial targets, but their willingness to do so may depend on their motivations. We investigated whether perceivers' levels of internal motivation to control prejudice (IMS) and external motivation to control prejudice (EMS) predicted their likelihood of categorizing Black-White multiracial faces as Multiracial. Across four studies, IMS positively predicted perceivers' categorizations of multiracial faces as Multiracial. The association between IMS and Multiracial categorizations was strongest when faces were most racially ambiguous. Explicit prejudice, implicit prejudice, and interracial contact were ruled out as explanations for the relationship between IMS and Multiracial categorizations. EMS may be negatively associated with the use of the Multiracial category. Therefore, perceivers' motivations to control prejudice have important implications for racial categorization processes.

  2. 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.

  3. Global connectivity of prefrontal cortex predicts cognitive control and intelligence

    PubMed Central

    Cole, Michael W.; Yarkoni, Tal; Repovs, Grega; Anticevic, Alan; Braver, Todd S.

    2012-01-01

    Control of thought and behavior is fundamental to human intelligence. Evidence suggests a fronto-parietal brain network implements such cognitive control across diverse contexts. We identify a mechanism – global connectivity – by which components of this network might coordinate control of other networks. A lateral prefrontal cortex (LPFC) region’s activity was found to predict performance in a high control demand working memory task, and also to exhibit high global connectivity. Critically, global connectivity in this LPFC region, involving connections both within and outside the fronto-parietal network, showed a highly selective relationship with individual differences in fluid intelligence. These findings suggest LPFC is a global hub with a brain-wide influence that facilitates the ability to implement control processes central to human intelligence. PMID:22745498

  4. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants

    SciTech Connect

    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.

  5. 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.

  6. Predictive onboard flow control in packet switching satellites

    NASA Technical Reports Server (NTRS)

    Bobinsky, E. 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.

  7. 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

  8. 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.

  9. Punishment sensitivity predicts the impact of punishment on cognitive control.

    PubMed

    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.

  10. 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.

  11. Adaptive Pole Placement Controllers For Robotic Manipulators With Predictive Action

    NASA Astrophysics Data System (ADS)

    Kaynak, Okyay; Hoyer, Helmut

    1987-10-01

    This paper proposes two pole assignment control schemes for robotic manipulators, based on an anticipatory action. In one, the control objective is for the velocity tracking error to decay with a prespecified dynamics. In the other, a generalised cost function is minimized and the weighting factors in the cost function are determined to achieve desired closed loop pole locations for the tracking error. The prediction scheme used ensures a high degree of robustness against system-model mismatch as demonstrated by the simulation results presented.

  12. Prediction and control of chaotic processes using nonlinear adaptive networks

    SciTech Connect

    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.

  13. 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.

  14. Multiplexed model predictive control for active vehicle suspensions

    NASA Astrophysics Data System (ADS)

    Hu, Yinlong; Chen, Michael Z. Q.; Hou, Zhongsheng

    2015-02-01

    Multiplexed model predictive control (MMPC) is a recently proposed efficient model predictive control (MPC) algorithm, which can effectively reduce the computational burden of the online optimisation in MPC implementation by updating the control inputs in an asynchronous manner. This paper investigates the application of MMPC in active vehicle suspension design. An MMPC controller integrated with soft constraints and a Kalman filter is proposed based on a full-car model. Ride comfort, roadholding and suspension deflection are considered in this paper, where ride comfort and roadholding are formulated as a quadratic cost function in terms of sprung mass accelerations and tyre deflections, while suspension deflection performance is formulated as a hard constraint. The saturation of the actuator force is also considered and formulated as a hard constraint as well. Numerical simulation is performed with respect to different choices of weighting factors, vehicle speeds and control horizons. The results show that the overall performance of ride comfort and roadholding can be improved significantly by employing MMPC and the average time taken by MMPC to solve the individual quadratic programming problem is considerably smaller than that of the conventional MPC, which effectively demonstrate the effectiveness of the proposed method.

  15. A novel trajectory prediction control for proximate time-optimal digital control DC—DC converters

    NASA Astrophysics Data System (ADS)

    Qing, Wang; Ning, Chen; Shen, Xu; Weifeng, Sun; Longxing, Shi

    2014-09-01

    The purpose of this paper is to present a novel trajectory prediction method for proximate time-optimal digital control DC—DC converters. The control method provides pre-estimations of the duty ratio in the next several switching cycles, so as to compensate the computational time delay of the control loop and increase the control loop bandwidth, thereby improving the response speed. The experiment results show that the fastest transient response time of the digital DC—DC with the proposed prediction is about 8 μs when the load current changes from 0.6 to 0.1 A.

  16. An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands

    PubMed Central

    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

  17. Control of nonlinear processes by using linear model predictive control algorithms.

    PubMed

    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. PMID:18255068

  18. 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.

  19. 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.

  20. Limits of Executive Control: Sequential Effects in Predictable Environments.

    PubMed

    Verbruggen, Frederick; McAndrew, Amy; Weidemann, Gabrielle; Stevens, Tobias; McLaren, Ian P L

    2016-05-01

    Cognitive-control theories attribute action control to executive processes that modulate behavior on the basis of expectancy or task rules. In the current study, we examined corticospinal excitability and behavioral performance in a go/no-go task. Go and no-go trials were presented in runs of five, and go and no-go runs alternated predictably. At the beginning of each trial, subjects indicated whether they expected a go trial or a no-go trial. Analyses revealed that subjects immediately adjusted their expectancy ratings when a new run started. However, motor excitability was primarily associated with the properties of the previous trial, rather than the predicted properties of the current trial. We also observed a large latency cost at the beginning of a go run (i.e., reaction times were longer for the first trial in a go run than for the second trial). These findings indicate that actions in predictable environments are substantially influenced by previous events, even if this influence conflicts with conscious expectancies about upcoming events. PMID:27000177

  1. 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.

  2. 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.

  3. 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.

  4. Terminal spacecraft rendezvous and capture with LASSO model predictive control

    NASA Astrophysics Data System (ADS)

    Hartley, Edward N.; Gallieri, Marco; Maciejowski, Jan M.

    2013-11-01

    The recently investigated ℓasso model predictive control (MPC) is applied to the terminal phase of a spacecraft rendezvous and capture mission. The interaction between the cost function and the treatment of minimum impulse bit is also investigated. The propellant consumption with ℓasso MPC for the considered scenario is noticeably less than with a conventional quadratic cost and control actions are sparser in time. Propellant consumption and sparsity are competitive with those achieved using a zone-based ℓ1 cost function, whilst requiring fewer decision variables in the optimisation problem than the latter. The ℓasso MPC is demonstrated to meet tighter specifications on control precision and also avoids the risk of undesirable behaviours often associated with pure ℓ1 stage costs.

  5. Programmable logic controller implementation of an auto-tuned predictive control based on minimal plant information.

    PubMed

    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.

  6. 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.

  7. Predictive control and estimation algorithms for the NASA/JPL Deep Space Network antennas

    NASA Technical Reports Server (NTRS)

    Gawronski, W.

    1991-01-01

    A modified output prediction procedure, and a new controller design based on the predictive control law are presented. Also, the predictive estimator is developed to complement the controller, and to enhance the system performance. The predictive controller was designed and applied to the tracking control of the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) 70-m antenna. Simulation results show significant improvement in tracking performance over the linear quadratic controller and estimator presently in use.

  8. Model Predictive Control for the Operation of Building Cooling Systems

    SciTech Connect

    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.

  9. [The quality control based on the predictable performance].

    PubMed

    Zheng, D X

    2016-09-01

    The clinical performance can only be evaluated when it comes to the last step in the conventional way of prosthesis. However, it often causes the failure because of the unconformity between the expectation and final performance. Resulting from this kind of situation, quality control based on the predictable results has been suggested. It is a new idea based on the way of reverse thinking, and focuses on the need of patient and puts the final performance of the prosthesis to the first place. With the prosthodontically driven prodedure, dentists can complete the unification with the expectation and the final performance. PMID:27596338

  10. Low inhibitory control and restrictive feeding practices predict weight outcomes

    PubMed Central

    Anzman, Stephanie L.; Birch, Leann L.

    2009-01-01

    Objective A priority for research is to identify individuals early in development who are particularly susceptible to weight gain in the current, obesogenic environment. This longitudinal study investigated whether early individual differences in inhibitory control, an aspect of temperament, predicted weight outcomes and whether parents’ restrictive feeding practices moderated this relation. Study design Participants included 197 non-Hispanic White girls and their parents; families were assessed when girls were 5, 7, 9, 11, 13, and 15 years old. Measures included mothers’ reports of girls’ inhibitory control levels, girls’ reports of parental restriction in feeding, girls’ body mass indexes (BMIs), and parents’ BMIs, education, and income. Results Girls with lower inhibitory control at age 7 had higher concurrent BMIs, greater weight gain, higher BMIs at all subsequent time points, and were 1.95 times more likely to be overweight at age 15. Girls who perceived higher parental restriction exhibited the strongest inverse relation between inhibitory control and weight status. Conclusion Variability in inhibitory control could help identify individuals who are predisposed to obesity risk; the current findings also highlight the importance of parenting practices as potentially modifiable factors which exacerbate or attenuate this risk. PMID:19595373

  11. Tuning the Model Predictive Control of a Crude Distillation Unit.

    PubMed

    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.

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

    PubMed

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

    2016-07-01

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

  13. Stabilizing model predictive control for constrained nonlinear distributed delay systems.

    PubMed

    Mahboobi Esfanjani, R; Nikravesh, S K Y

    2011-04-01

    In this paper, a model predictive control scheme with guaranteed closed-loop asymptotic stability is proposed for a class of constrained nonlinear time-delay systems with discrete and distributed delays. A suitable terminal cost functional and also an appropriate terminal region are utilized to achieve asymptotic stability. To determine the terminal cost, a locally asymptotically stabilizing controller is designed and an appropriate Lyapunov-Krasoskii functional of the locally stabilized system is employed as the terminal cost. Furthermore, an invariant set for locally stabilized system which is established by using the Razumikhin Theorem is used as the terminal region. Simple conditions are derived to obtain terminal cost and terminal region in terms of Bilinear Matrix Inequalities. The method is illustrated by a numerical example.

  14. Predictive process control for sub-0.2-um lithography

    NASA Astrophysics Data System (ADS)

    Zavecz, Terrence E.; Blanquies, Rene M.

    2000-06-01

    An advanced control system providing modeling and predictive data simulation for pass-fail criteria of overlay production control has been used in 0.18 micrometer Design Rule production facilities for over a year. During this period overlay was measured on both product wafers and during periodic process qualification tests. The resulting raw data is modeled using exposure tool specific and layer-focused models. Modeled results, measured process statistics and tool signatures are combined in a real-time simulation to calculate the true overlay distribution over the entire wafer and lot. All results and raw data are automatically gathered and stored in a database for on-going analysis. In this manner, tool, product technology and process performance data are gathered for every overlay process-step. The data provides valuable insights into not only tool stability but also the process- step characteristic errors that contribute to the overlay spectrum of distortions. Data gathered in this manner is very stable and can be used to predict a feed-forward correction for all correctable coefficients. The technique must take into consideration algorithm modeled coefficient variations resulting from: (1) Reticle pattern-to-alignment mark design errors. (2) Process film variations. (3) Tool-to-tool static matching. (4) Tool-to-tool dynamic matching errors which are match-residual, process or time induced. This extensive database has resulted in a method of conducting Predictive Process Control (PPC) for overlay lithography within an advanced semiconductor line. Using PPC the wafer production facility experiences: (1) Improved Yield: Lots are always exposed with optimum setup. Optimized setups reduce rework levels and therefore wafer handling. (2) Capacity Improvement: Elimination of rework tacitly improves capacity in the facility. WIP is also simplified because lots do not have to wait for a dedicated exposure tool to become available. (3) Dynamic MatchingTM: Matching of

  15. Evidence for predictive control in lifting series of virtual objects.

    PubMed

    Mawase, Firas; Karniel, Amir

    2010-06-01

    The human motor control system gracefully behaves in a dynamic and time varying environment. Here, we explored the predictive capabilities of the motor system in a simple motor task of lifting a series of virtual objects. When a subject lifts an object, she/he uses an expectation of the weight of the object to generate a motor command. All models of motor learning employ learning algorithms that essentially expect the future to be similar to the previously experienced environment. In this study, we asked subjects to lift a series of increasing weights and determined whether they extrapolated from past experience and predicted the next weight in the series even though that weight had never been experienced. The grip force at the beginning of the lifting task is a clean indication of the motor expectation. In contrast to the motor learning literature asserting adaptation by means of expecting a weighted average based on past experience, our results suggest that the motor system is able to predict the subsequent weight that follows a series of increasing weights.

  16. 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.

  17. Striatal prediction errors support dynamic control of declarative memory decisions

    PubMed Central

    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

  18. 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

  19. Study on noise prediction model and control schemes for substation.

    PubMed

    Chen, Chuanmin; 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

  20. Study on noise prediction model and control schemes for substation.

    PubMed

    Chen, Chuanmin; 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.

  1. Control when it counts: Change in executive control under stress predicts depression symptoms.

    PubMed

    Quinn, Meghan E; Joormann, Jutta

    2015-08-01

    Individual differences in the ability to regulate affect following stressful life events have been associated with an increased risk for experiencing depression symptoms. Research further suggests that emotion regulation may depend on executive control which, in turn, has been shown to decline following stress exposure. Whether individual differences in stress-induced change in executive control predict depression symptoms, however, remains unknown. The current study examined whether trait executive control as well as stress-induced change in executive control predicts depression symptoms during a stressful time of life. The current study recruited 43 individuals during their first year of college. Participants completed an executive control task before and after a laboratory stress induction. Participants reported baseline depression symptoms during the laboratory session and follow-up depression symptoms during the final weeks of the semester. Results demonstrate that stress-induced change in executive control predicted an increase in depression symptoms at the end of the semester. The findings suggest that individual differences in the degree of decline in executive control following stress exposure may be a key factor in explaining why some individuals are vulnerable to depression during a stressful time of life. PMID:26098731

  2. Control when it counts: Change in executive control under stress predicts depression symptoms.

    PubMed

    Quinn, Meghan E; Joormann, Jutta

    2015-08-01

    Individual differences in the ability to regulate affect following stressful life events have been associated with an increased risk for experiencing depression symptoms. Research further suggests that emotion regulation may depend on executive control which, in turn, has been shown to decline following stress exposure. Whether individual differences in stress-induced change in executive control predict depression symptoms, however, remains unknown. The current study examined whether trait executive control as well as stress-induced change in executive control predicts depression symptoms during a stressful time of life. The current study recruited 43 individuals during their first year of college. Participants completed an executive control task before and after a laboratory stress induction. Participants reported baseline depression symptoms during the laboratory session and follow-up depression symptoms during the final weeks of the semester. Results demonstrate that stress-induced change in executive control predicted an increase in depression symptoms at the end of the semester. The findings suggest that individual differences in the degree of decline in executive control following stress exposure may be a key factor in explaining why some individuals are vulnerable to depression during a stressful time of life.

  3. 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.

  4. 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.

  5. 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.

  6. Predictive wavefront control for Adaptive Optics with arbitrary control loop delays

    SciTech Connect

    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.

  7. Observer-based tracking controller design for networked predictive control systems with uncertain Markov delays

    NASA Astrophysics Data System (ADS)

    Zhang, Hui; Shi, Yang; Wang, Junmin

    2013-10-01

    This paper is concerned with a tracking controller design problem for discrete-time networked predictive control systems. The control law used here is a combined state-feedback control and integral control. Since not all the states are available in practice, a local Luenberger observer is utilised to estimate the state vector. The measured output and estimated state vector are packed together and transmitted to the tracking controller via a communication channel with a limited capacity. Meanwhile, the control signal is also transmitted through a communication network.Network-induced delays on both links are considered for the signal transmission and modelled by Markov chains. Moreover, it is assumed that the elements in Markov transition matrices are subject to uncertainties. In order to fully compensate for network-induced delays, the controller generates a sequence of control signals which are dependent on each possible delay in the feedforward channel. By taking the augmentation twice, we obtain delay-free stochastic closed-loop systems and the controlled output is chosen as the tracking error. Sufficient conditions are provided for the energy-to-peak performance of the closed-loop systems. The feedback gains of the controller can be derived by solving a minimisation problem. Two examples are illustrated to demonstrate the effectiveness of the proposed design method.

  8. A Rate Feedback Predictive Control Scheme Based on Neural Network and Control Theory for Autonomic Communication

    NASA Astrophysics Data System (ADS)

    Xiong, Naixue; Vasilakos, Athanasios V.; Yang, Laurence T.; Long, Fei; Shu, Lei; Li, Yingshu

    The main difficulty arising in designing an efficient congestion control scheme lies in the large propagation delay in data transfer which usually leads to a mismatch between the network resources and the amount of admitted traffic. To attack this problem, this chapter describes a novel congestion control scheme that is based on a Back Propagation (BP) neural network technique.We consider a general computer communication model with multiple sources and one destination node. The dynamic buffer occupancy of the bottleneck node is predicted and controlled by using a BP neural network. The controlled best-effort traffic of the sources uses the bandwidth, which is left over by the guaranteed traffic. This control mechanism is shown to be able to avoid network congestion efficiently and to optimize the transfer performance both by the theoretic analyzing procedures and by the simulation studies.

  9. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    USGS Publications Warehouse

    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.

  10. Predictive powertrain control using powertrain history and GPS data

    SciTech Connect

    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.

  11. Autonomous formation flight of helicopters: Model predictive control approach

    NASA Astrophysics Data System (ADS)

    Chung, Hoam

    Formation flight is the primary movement technique for teams of helicopters. However, the potential for accidents is greatly increased when helicopter teams are required to fly in tight formations and under harsh conditions. This dissertation proposes that the automation of helicopter formations is a realistic solution capable of alleviating risks. Helicopter formation flight operations in battlefield situations are highly dynamic and dangerous, and, therefore, we maintain that both a high-level formation management system and a distributed coordinated control algorithm should be implemented to help ensure safe formations. The starting point for safe autonomous formation flights is to design a distributed control law attenuating external disturbances coming into a formation, so that each vehicle can safely maintain sufficient clearance between it and all other vehicles. While conventional methods are limited to homogeneous formations, our decentralized model predictive control (MPC) approach allows for heterogeneity in a formation. In order to avoid the conservative nature inherent in distributed MPC algorithms, we begin by designing a stable MPC for individual vehicles, and then introducing carefully designed inter-agent coupling terms in a performance index. Thus the proposed algorithm works in a decentralized manner, and can be applied to the problem of helicopter formations comprised of heterogenous vehicles. Individual vehicles in a team may be confronted by various emerging situations that will require the capability for in-flight reconfiguration. We propose the concept of a formation manager to manage separation, join, and synchronization of flight course changes. The formation manager accepts an operator's commands, information from neighboring vehicles, and its own vehicle states. Inside the formation manager, there are multiple modes and complex mode switchings represented as a finite state machine (FSM). Based on the current mode and collected

  12. EPR oxygen images predict tumor control by a 50 percent tumor control radiation dose

    PubMed Central

    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

  13. Predictable SCR co-benefits for mercury control

    SciTech Connect

    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.

  14. Exploratory Studies in Generalized Predictive Control for Active Aeroelastic Control of Tiltrotor Aircraft

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  16. The role of prediction and outcomes in adaptive cognitive control.

    PubMed

    Schiffer, Anne-Marike; Waszak, Florian; Yeung, Nick

    2015-01-01

    Humans adaptively perform actions to achieve their goals. This flexible behaviour requires two core abilities: the ability to anticipate the outcomes of candidate actions and the ability to select and implement actions in a goal-directed manner. The ability to predict outcomes has been extensively researched in reinforcement learning paradigms, but this work has often focused on simple actions that are not embedded in hierarchical and sequential structures that are characteristic of goal-directed human behaviour. On the other hand, the ability to select actions in accordance with high-level task goals, particularly in the presence of alternative responses and salient distractors, has been widely researched in cognitive control paradigms. Cognitive control research, however, has often paid less attention to the role of action outcomes. The present review attempts to bridge these accounts by proposing an outcome-guided mechanism for selection of extended actions. Our proposal builds on constructs from the hierarchical reinforcement learning literature, which emphasises the concept of reaching and evaluating informative states, i.e., states that constitute subgoals in complex actions. We develop an account of the neural mechanisms that allow outcome-guided action selection to be achieved in a network that relies on projections from cortical areas to the basal ganglia and back-projections from the basal ganglia to the cortex. These cortico-basal ganglia-thalamo-cortical 'loops' allow convergence - and thus integration - of information from non-adjacent cortical areas (for example between sensory and motor representations). This integration is essential in action sequences, for which achieving an anticipated sensory state signals the successful completion of an action. We further describe how projection pathways within the basal ganglia allow selection between representations, which may pertain to movements, actions, or extended action plans. The model lastly envisages

  17. Model predictive control for a class of systems with isolated nonlinearity.

    PubMed

    Tao, Jili; Zhu, Yong; Fan, Qinru

    2014-07-01

    The paper is concerned with an overall convergent nonlinear model predictive control design for a kind of nonlinear mechatronic drive systems. The proposed nonlinear model predictive control results in the improvement of regulatory capacity for reference tracking and load disturbance rejection. The design of the nonlinear model predictive controller consists of two steps: the first step is to design a linear model predictive controller based on the linear part of the system at each sample instant, then an overall convergent nonlinear part is added to the linear model predictive controller to combine a nonlinear controller using error driven. The structure of the proposed controller is similar to that of classical PI optimal regulator but it also bears a set-point feed forward control loop, thus tracking ability and disturbance rejection are improved. The proposed method is compared with the results from recent literature, where control performance under both model match and mismatch cases are enlightened. PMID:24755053

  18. Analysis, prediction and control of radio frequency interference with respect to DSN

    NASA Astrophysics Data System (ADS)

    Degroot, N. F.

    1982-06-01

    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.

  19. Prediction and control as determinants of behavioural uncertainty: effects on task performance and heart rate reactivity.

    PubMed

    Baker, S R; Stephenson, D

    2000-01-01

    Control or control-belief is often viewed as being directly instrumental in facilitating coping mechanisms in aversive situations, and yet the empirical evidence for the beneficial effects of control is inconclusive. In this study we investigated the role of predictability in determining the effects of perceived control during an aversive reaction time task. Fifty-six subjects were allocated to one of four groups; predictable-control, predictable-no control, unpredictable-control, unpredictable-no control. In the predictable conditions, subjects could temporally predict the occurrence of an aversive noise. In the perceived control conditions, duration of the aversive tone was contingent on subject's performance. All subjects were matched in terms of the nature of the task and in the number and time of receipt of both the warning signal and noise. Heart rate reactivity and two performance parameters were measured, reaction time and performance increase. Both predictability and control-belief led to a reduction in heart rate reactivity, although they appeared to function independently and at different points in the sequence of events. That is, predictability or perceived control was sufficient to mitigate the effects of an aversive situation. Neither perception of control or predictability led to better task performance. These results are discussed in terms of behavioural uncertainty explanations.

  20. 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.

  1. 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)

  2. Prediction of forces and moments for flight vehicle control effectors. Part 1: Validation of methods for predicting hypersonic vehicle controls forces and moments

    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.

  3. Mechanisms of Intentional Binding and Sensory Attenuation: The Role of Temporal Prediction, Temporal Control, Identity Prediction, and Motor Prediction

    ERIC Educational Resources Information Center

    Hughes, Gethin; Desantis, Andrea; Waszak, Florian

    2013-01-01

    Sensory processing of action effects has been shown to differ from that of externally triggered stimuli, with respect both to the perceived timing of their occurrence (intentional binding) and to their intensity (sensory attenuation). These phenomena are normally attributed to forward action models, such that when action prediction is consistent…

  4. Hurricane prediction and control: impact of large computers.

    PubMed

    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.

  5. Enhanced perceptions of control and predictability reduce motion-induced nausea and gastric dysrhythmia.

    PubMed

    Levine, Max E; Stern, Robert M; Koch, Kenneth L

    2014-08-01

    Nausea is a debilitating condition that is typically accompanied by gastric dysrhythmia. The enhancement of perceived control and predictability has generally been found to attenuate the physiological stress response. The aim of the present study was to test the effect of these psychosocial variables in the context of nausea, motion sickness, and gastric dysrhythmia. A 2x2, independent-groups, factorial design was employed in which perceived control and predictability were each provided at high or low levels to 80 participants before exposure to a rotating optokinetic drum. Ratings of nausea were obtained throughout a 6-min baseline period and a 16-min drum rotation period. Noninvasive recordings of the electrical activity of the stomach called electrogastrograms were also obtained throughout the study. Nausea scores were significantly lower among participants with high control than among those with low control, and were significantly lower among participants with high predictability than among those with low predictability. Estimates of gastric dysrhythmia obtained from the EGG during drum rotation were significantly lower among participants with high predictability than among those with low predictability. A significant interaction effect of control and predictability on gastric dysrhythmia was also observed, such that high control was only effective for arresting the development of gastric dysrhythmia when high predictability was also available. Stronger perceptions of control and predictability may temper the development of nausea and gastric dysrhythmia during exposure to provocative motion. Psychosocial interventions in a variety of nausea contexts may represent an alternative means of symptom control. PMID:24748483

  6. Enhanced perceptions of control and predictability reduce motion-induced nausea and gastric dysrhythmia.

    PubMed

    Levine, Max E; Stern, Robert M; Koch, Kenneth L

    2014-08-01

    Nausea is a debilitating condition that is typically accompanied by gastric dysrhythmia. The enhancement of perceived control and predictability has generally been found to attenuate the physiological stress response. The aim of the present study was to test the effect of these psychosocial variables in the context of nausea, motion sickness, and gastric dysrhythmia. A 2x2, independent-groups, factorial design was employed in which perceived control and predictability were each provided at high or low levels to 80 participants before exposure to a rotating optokinetic drum. Ratings of nausea were obtained throughout a 6-min baseline period and a 16-min drum rotation period. Noninvasive recordings of the electrical activity of the stomach called electrogastrograms were also obtained throughout the study. Nausea scores were significantly lower among participants with high control than among those with low control, and were significantly lower among participants with high predictability than among those with low predictability. Estimates of gastric dysrhythmia obtained from the EGG during drum rotation were significantly lower among participants with high predictability than among those with low predictability. A significant interaction effect of control and predictability on gastric dysrhythmia was also observed, such that high control was only effective for arresting the development of gastric dysrhythmia when high predictability was also available. Stronger perceptions of control and predictability may temper the development of nausea and gastric dysrhythmia during exposure to provocative motion. Psychosocial interventions in a variety of nausea contexts may represent an alternative means of symptom control.

  7. 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.

  8. Prediction by neural network methods compared for energy control problems

    SciTech Connect

    Surkan, A.J.; Skurikhin, A.N.

    1996-10-01

    Daily recordings of energy consumption are sequenced to construct a time series that is used to test the accuracy of a variety of neural networks in making one- and two-day demand predictions. Five neural network methods were applied to the same data for a problem of predicting daily energy usage. These included four which were gradient-based, namely, back propagation (BACKPROP), quick-propagation (QUICKPROP), cascade correlation (CASCOR), memory neuron network (MNN), and a correlation-based method called ALOPEX. The observed time series is the only information source used for making predictions. Other supplementary parallel series of independent data can produce improvements. It was demonstrated that these neural network learning algorithms can train networks to predict consistently, one-day-ahead, over one year of set-aside test patterns and reduce the average error to below 19%. Reported is a comparison of applicability of neural networks by programmed simulations of the widely used gradient-based algorithms along with newer algorithms MNN and ALOPEX. The diverse capabilities of these various networks give insights which are a useful basis for selecting further studies.

  9. An application of generalized predictive control to rotorcraft terrain-following flight

    NASA Technical Reports Server (NTRS)

    Hess, Ronald A.; Jung, Yoon C.

    1989-01-01

    Generalized predictive control (GPC) describes an algorithm for the control of dynamic systems in which a control input is generated which minimizes a quadratic cost function consisting of a weighted sum of errors between desired and predicted future system output and future predicted control increments. The output predictions are obtained from an internal model of the plant dynamics. The GPC algorithm is first applied to a simplified rotorcraft terrain-following problem, and GPC performance is compared to that of a conventional compensatory automatic system in terms of flight-path following, control activity, and control law implementation. Next, more realistic vehicle dynamics are utilized, and the GPC algorithm is applied to simultaneous terrain following and velocity control in the presence of atmospheric disturbances and errors in the internal model of the vehicle. The online computational and sensing requirements for implementing the GPC algorithm are minimal. Its use for manual control models appears promising.

  10. Self-tuning Generalized Predictive Control applied to terrain following flight

    NASA Technical Reports Server (NTRS)

    Hess, R. A.; Jung, Y. C.

    1989-01-01

    Generalized Predictive Control (GPC) describes an algorithm for the control of dynamic systems in which a control input is generated which minimizes a quadratic cost function consisting of a weighted sum of errors between desired and predicted future system output and future predicted control increments. The output predictions are obtained from an internal model of the plant dynamics. Self-tuning GPC refers to an implementation of the GPC algorithm in which the parameters of the internal model(s) are estimated on-line and the predictive control law tuned to the parameters so identified. The self-tuning GPC algorithm is applied to a problem of rotorcraft longitudinal/vertical terrain-following flight. The ability of the algorithm to tune to the initial vehicle parameters and to successfully adapt to a stability augmentation failure is demonstrated. Flight path performance is compared to a conventional, classically designed flight path control system.

  11. 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)…

  12. Predicting preschool effortful control from toddler temperament and parenting behavior

    PubMed Central

    Cipriano, Elizabeth A.; Stifter, Cynthia A.

    2010-01-01

    This longitudinal study assessed whether maternal behavior and emotional tone moderated the relationship between toddler temperament and preschooler's effortful control. Maternal behavior and emotional tone were observed during a parent-child competing demands task when children were 2 years of age. Child temperament was also assessed at 2 years of age, and three temperament groups were formed: inhibited, exuberant, and low reactive. At 4.5 years of age, children's effortful control was measured from parent-report and observational measures. Results indicated that parental behavior and emotional tone appear to be especially influential on exuberant children's effortful control development. Exuberant children whose mothers used commands and prohibitive statements with a positive emotional tone were more likely to be rated higher on parent-reported effortful control 2.5 years later. When mothers conveyed redirections and reasoning-explanations in a neutral tone, their exuberant children showed poorer effortful control at 4.5 years. PMID:23814350

  13. A predictive and feedback control algorithm maintains a constant glucose concentration in fed-batch fermentations.

    PubMed

    Kleman, G L; Chalmers, J J; Luli, G W; Strohl, W R

    1991-04-01

    A combined predictive and feedback control algorithm based on measurements of the concentration of glucose on-line has been developed to control fed-batch fermentations of Escherichia coli. The predictive control algorithm was based on the on-line calculation of glucose demand by the culture and plotting a linear regression to the next datum point to obtain a predicted glucose demand. This provided a predictive "coarse" control for the glucose-based nutrient feed. A direct feedback control using a proportional controller, based on glucose measurements every 2 min, fine-tuned the feed rate. These combined control schemes were used to maintain glucose concentrations in fed-batch fermentations as tight as 0.49 +/- 0.04 g/liter during growth of E. coli to high cell densities. PMID:2059049

  14. A predictive and feedback control algorithm maintains a constant glucose concentration in fed-batch fermentations.

    PubMed Central

    Kleman, G L; Chalmers, J J; Luli, G W; Strohl, W R

    1991-01-01

    A combined predictive and feedback control algorithm based on measurements of the concentration of glucose on-line has been developed to control fed-batch fermentations of Escherichia coli. The predictive control algorithm was based on the on-line calculation of glucose demand by the culture and plotting a linear regression to the next datum point to obtain a predicted glucose demand. This provided a predictive "coarse" control for the glucose-based nutrient feed. A direct feedback control using a proportional controller, based on glucose measurements every 2 min, fine-tuned the feed rate. These combined control schemes were used to maintain glucose concentrations in fed-batch fermentations as tight as 0.49 +/- 0.04 g/liter during growth of E. coli to high cell densities. PMID:2059049

  15. The Interplay of Maternal Sensitivity and Gentle Control When Predicting Children's Subsequent Academic Functioning: Evidence of Mediation by Effortful Control

    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…

  16. Predicting the operations alert levels for dengue surveillance and control.

    PubMed

    Sampath, Kameshwaran; Dayama, Pankaj

    2014-01-01

    Operations alert level is a discrete measure that quantifies the severity of epidemic outbreak with respect to operational measures. The alert levels are ordered based on the amount of response operations required. In this paper, we develop multi-class classification models based on ordinal multinomial logistic regression for predicting the alert levels for dengue at twenty weeks in advance. The regression uses the dynamic lag non-linear models to account for the non-linearity of the dengue incidence, along with its lagged values. The performance of the models is tested for the dengue case count data of Singapore.

  17. Transition prediction and control in subsonic flow over a hump

    NASA Technical Reports Server (NTRS)

    Masad, Jamal A.; Iyer, Venkit

    1993-01-01

    The influence of a surface roughness element in the form of a two-dimensional hump on the transition location in a two-dimensional subsonic flow with a free-stream Mach number up to 0.8 is evaluated. Linear stability theory, coupled with the N-factor transition criterion, is used in the evaluation. The mean flow over the hump is calculated by solving the interacting boundary-layer equations; the viscous-inviscid coupling is taken into consideration, and the flow is solved within the separation bubble. The effects of hump height, length, location, and shape; unit Reynolds number; free-stream Mach number, continuous suction level; location of a suction strip; continuous cooling level; and location of a heating strip on the transition location are evaluated. The N-factor criterion predictions agree well with the experimental correlation of Fage; in addition, the N-factor criterion is more general and powerful than experimental correlations. The theoretically predicted effects of the hump's parameters and flow conditions on transition location are consistent and in agreement with both wind-tunnel and flight observations.

  18. Investigation of energy management strategies for photovoltaic systems - A predictive control algorithm

    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.

  19. Meditation-induced states predict attentional control over time.

    PubMed

    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.

  20. Autonomy and control: augmenting the validity of the theory of planned behaviour in predicting exercise.

    PubMed

    Brickell, Tracey A; Chatzisarantis, Nikos L D; Pretty, Grace M

    2006-01-01

    This study examined the utility of the theory of planned behaviour (TPB) along with additional constructs in predicting exercise, and explored the motivational antecedents of exercise intentions. Participants included 162 Canadian University College students (61% females). Measures of TPB, autonomous and controlling intention, perceived autonomy support and core autonomous intention were completed during phase 1 of data collection. Two and three weeks later behaviour was assessed. Hierarchical regression analyses revealed that: (a) attitude and perceived behavioural control significantly predicted TPB intention and core autonomous intention; (b) subjective norm predicted controlling intention; and (c) perceived autonomy support predicted autonomous and core autonomous intention. TPB intention significantly predicted behaviour. TPB is a fairly useful model for predicting behaviour and important information can be gained when other measures of intention are explored.

  1. Happiness as a motivator: positive affect predicts primary control striving for career and educational goals.

    PubMed

    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. PMID:22569224

  2. Prediction-driven coordination of distributed MPC controllers for linear unconstrained dynamic systems

    NASA Astrophysics Data System (ADS)

    Marcos, Natalia I.; Fraser Forbes, J.; Guay, Martin

    2014-08-01

    In this paper, a coordinated-distributed model predictive control (CDMPC) scheme is proposed for discrete-time, linear, unconstrained dynamic systems. The proposed control scheme incorporates a coordinator that communicates with local CDMPC controllers. With the assistance of the coordinator, the local CDMPC controllers adjust their calculated control actions iteratively to achieve the optimal plant-wide operation. A 'prediction-driven' algorithm is used to coordinate the local CDMPC controllers. Convergence of the prediction-driven algorithm is shown along with a stability analysis of the closed-loop system under coordinated-distributed control. A simulation example is used to illustrate the effectiveness of the proposed coordinated-distributed control scheme.

  3. 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…

  4. Predicting Preschool Effortful Control from Toddler Temperament and Parenting Behavior

    ERIC Educational Resources Information Center

    Cipriano, Elizabeth A.; Stifter, Cynthia A.

    2010-01-01

    This longitudinal study assessed whether maternal behavior and emotional tone moderated the relationship between toddler temperament and preschooler's effortful control. Maternal behavior and emotional tone were observed during a parent-child competing demands task when children were 2 years of age. Child temperament was also assessed at 2 years…

  5. Adaptive and predictive control of a simulated robot arm.

    PubMed

    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).

  6. Controllability modulates the neural response to predictable but not unpredictable threat in humans.

    PubMed

    Wood, Kimberly H; Wheelock, Muriah D; Shumen, Joshua R; Bowen, Kenton H; Ver Hoef, Lawrence W; Knight, David C

    2015-10-01

    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 threats 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.

  7. New technologies in predicting, preventing and controlling emerging infectious diseases

    PubMed Central

    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

  8. Attributions of control and seropositivity among Latinos: examining the predictive utility of the locus of control construct.

    PubMed

    Burns, S M; Maniss, S; Young, L R L; Gaubatz, M

    2005-02-01

    This investigation explored the utility of the health locus of control construct in predicting the mental health quality of life (MHQOL) ratings of 72 Latinos living with HIV/AIDS. After controlling for patient CD4 count, viral load, time since diagnosis, Physical Health Quality of Life and acculturative status, Powerful Others Locus of Control beliefs accounted for a significant increment of the variance in Mental Health Quality of Life. In a similar model, Internal Locus of Control failed to predict MHQOL. Discussion and implications highlight how cultural considerations may broaden investigations of health among diverse, minority populations.

  9. 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.

  10. A Computerized Test of Self-Control Predicts Classroom Behavior

    PubMed Central

    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. PMID:16813037

  11. Predictive Multiple Model Switching Control with the Self-Organizing Map

    NASA Technical Reports Server (NTRS)

    Motter, Mark A.

    2000-01-01

    A predictive, multiple model control strategy is developed by extension of self-organizing map (SOM) local dynamic modeling of nonlinear autonomous systems to a control framework. Multiple SOMs collectively model the global response of a nonautonomous system to a finite set of representative prototype controls. Each SOM provides a codebook representation of the dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the global minimization of a similarity metric. The SOM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme that selects the best available model for the applied control. SOM based linear models are used to predict the response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal.

  12. Non linear predictive control of a LEGO mobile robot

    NASA Astrophysics Data System (ADS)

    Merabti, H.; Bouchemal, B.; Belarbi, K.; Boucherma, D.; Amouri, A.

    2014-10-01

    Metaheuristics are general purpose heuristics which have shown a great potential for the solution of difficult optimization problems. In this work, we apply the meta heuristic, namely particle swarm optimization, PSO, for the solution of the optimization problem arising in NLMPC. This algorithm is easy to code and may be considered as alternatives for the more classical solution procedures. The PSO- NLMPC is applied to control a mobile robot for the tracking trajectory and obstacles avoidance. Experimental results show the strength of this approach.

  13. Predictive optimal control of sewer networks using CORAL tool: application to Riera Blanca catchment in Barcelona.

    PubMed

    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. PMID:19700825

  14. 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…

  15. 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.

  16. Predicting methyl iodide emission, soil concentration, and pest control in a two-dimensional chamber system

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Due to ever increasing state and federal regulations, the future use of fumigants is predicted on negative environmental impacts while offering sufficient pest control efficacy. To foster the development of the best management practice (BMP), an integrated tool is needed to simultaneously predict fu...

  17. Predictive Analysis of Geochemical Controls in an Alpine Stream

    NASA Astrophysics Data System (ADS)

    Jochems, A. P.; Sherson, L. R.; Crossey, L. J.; Karlstrom, K. E.

    2010-12-01

    hydrographs during late winter, as well as on the falling limb of flow during summer. Cation and anion concentrations experience significant declines during periods of high flow, though loadings remain high. Solute concentrations were found to increase downstream regardless of season. Downstream increases take place abruptly where the river crosses fault systems that localize discharge of hot spring brines from the hydrothermal system. Analyses completed during the spring of 2010 indicate that arsenic greatly exceeds EPA drinking water standards at low flows (<30 cfs). TDS and sulfate concentrations in the Jemez also exceed these standards at similar discharge. Stable isotope analyses demonstrate contributions from geothermal systems, with isotopically enriched values of δ18O for thermal waters, and near-meteoric values for most river waters. A model predicting solute concentrations as a function of snowmelt demonstrates that the Jemez River is susceptible to significant degradation of water quality under scenarios of decreasing snowpack. Fluctuations in water chemistries of this system directly affect recreational use and water quality of the Jemez River and shallow aquifer recharge, and must be considered for any proposed domestic or municipal use in the future.

  18. Constrained off-line synthesis approach of model predictive control for networked control systems with network-induced delays.

    PubMed

    Tang, Xiaoming; Qu, Hongchun; Wang, Ping; Zhao, Meng

    2015-03-01

    This paper investigates the off-line synthesis approach of model predictive control (MPC) for a class of networked control systems (NCSs) with network-induced delays. A new augmented model which can be readily applied to time-varying control law, is proposed to describe the NCS where bounded deterministic network-induced delays may occur in both sensor to controller (S-A) and controller to actuator (C-A) links. Based on this augmented model, a sufficient condition of the closed-loop stability is derived by applying the Lyapunov method. The off-line synthesis approach of model predictive control is addressed using the stability results of the system, which explicitly considers the satisfaction of input and state constraints. Numerical example is given to illustrate the effectiveness of the proposed method.

  19. Scenario-based, closed-loop model predictive control with application to emergency vehicle scheduling

    NASA Astrophysics Data System (ADS)

    Goodwin, Graham. C.; Medioli, Adrian. M.

    2013-08-01

    Model predictive control has been a major success story in process control. More recently, the methodology has been used in other contexts, including automotive engine control, power electronics and telecommunications. Most applications focus on set-point tracking and use single-sequence optimisation. Here we consider an alternative class of problems motivated by the scheduling of emergency vehicles. Here disturbances are the dominant feature. We develop a novel closed-loop model predictive control strategy aimed at this class of problems. We motivate, and illustrate, the ideas via the problem of fluid deployment of ambulance resources.

  20. 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.

  1. The role of action prediction and inhibitory control for joint action coordination in toddlers.

    PubMed

    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. PMID:26150055

  2. Is It Really Self-Control? Examining the Predictive Power of the Delay of Gratification Task

    PubMed Central

    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

  3. The Impact of Trajectory Prediction Uncertainty on Air Traffic Controller Performance and Acceptability

    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.

  4. Is it really self-control? Examining the predictive power of the delay of gratification task.

    PubMed

    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.

  5. Study on model current predictive control method of PV grid- connected inverters systems with voltage sag

    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.

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

    PubMed

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

    2016-05-01

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

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

    PubMed

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

    2016-05-01

    In this paper, Model Predictive Control and Dead-beat predictive control strategies are proposed for the control of a PMSG based wind energy system. The proposed MPC considers the model of the converter-based system to forecast the possible future behavior of the controlled variables. It allows selecting the voltage vector to be applied that leads to a minimum error by minimizing a predefined cost function. The main features of the MPC are low current THD and robustness against parameters variations. The Dead-beat predictive control is based on the system model to compute the optimum voltage vector that ensures zero-steady state error. The optimum voltage vector is then applied through Space Vector Modulation (SVM) technique. The main advantages of the Dead-beat predictive control are low current THD and constant switching frequency. The proposed control techniques are presented and detailed for the control of back-to-back converter in a wind turbine system based on PMSG. Simulation results (under Matlab-Simulink software environment tool) and experimental results (under developed prototyping platform) are presented in order to show the performances of the considered control strategies. PMID:26725504

  8. Model predictive control system and method for integrated gasification combined cycle power generation

    SciTech Connect

    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.

  9. Factors predictive of adolescents' intentions to use birth control pills, condoms, and birth control pills in combination with condoms.

    PubMed

    Craig, D M; Wade, K E; Allison, K R; Irving, H M; Williams, J I; Hlibka, C M

    2000-01-01

    Using the Theory of Planned Behaviour (Ajzen, 1988) as a conceptual framework, 705 secondary school students were surveyed to identify their intentions to use birth control pills, condoms, and birth control pills in combination with condoms. Hierarchical multiple regression revealed that the theory explained between 23.5% and 45.8% of the variance in intentions. Variables external to the model such as past use, age, and ethnicity exhibited some independent effects. Attitudes were consistently predictive of intentions to use condoms, pills, and condoms in combination with pills for both male and female students. However, there were differences by gender in the degree to which subjective norms and perceived behavioural control predicted intentions. The findings suggest that programs should focus on: creation of positive attitudes regarding birth control pills and condoms; targeting important social influences, particularly regarding males' use of condoms; and developing strategies to increase students' control over the use of condoms.

  10. Factors predictive of adolescents' intentions to use birth control pills, condoms, and birth control pills in combination with condoms.

    PubMed

    Craig, D M; Wade, K E; Allison, K R; Irving, H M; Williams, J I; Hlibka, C M

    2000-01-01

    Using the Theory of Planned Behaviour (Ajzen, 1988) as a conceptual framework, 705 secondary school students were surveyed to identify their intentions to use birth control pills, condoms, and birth control pills in combination with condoms. Hierarchical multiple regression revealed that the theory explained between 23.5% and 45.8% of the variance in intentions. Variables external to the model such as past use, age, and ethnicity exhibited some independent effects. Attitudes were consistently predictive of intentions to use condoms, pills, and condoms in combination with pills for both male and female students. However, there were differences by gender in the degree to which subjective norms and perceived behavioural control predicted intentions. The findings suggest that programs should focus on: creation of positive attitudes regarding birth control pills and condoms; targeting important social influences, particularly regarding males' use of condoms; and developing strategies to increase students' control over the use of condoms. PMID:11089290

  11. Controllability modulates the neural response to predictable but not unpredictable threat in humans.

    PubMed

    Wood, Kimberly H; Wheelock, Muriah D; Shumen, Joshua R; Bowen, Kenton H; Ver Hoef, Lawrence W; Knight, David C

    2015-10-01

    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 threats 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

  12. Controllability Modulates the Neural Response to Predictable but not Unpredictable Threat in Humans

    PubMed Central

    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

  13. Multiple-step terminal control with parameter identification and prediction during flight vehicle descent

    NASA Astrophysics Data System (ADS)

    Sirazetdinov, T. K.; Kiselev, V. I.

    A method is presented for multiple-step terminal control at a section of the glide path of a flight vehcile in the dense layers of the atmosphere. In accordance with the method, the parameters are identified and predicted at each step of the control process. Results of a computer simulation of the motion of a flight vehicle are presented.

  14. Feasibility study on a perceived fatigue prediction dependent power control for an electrically assisted bicycle.

    PubMed

    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.

  15. Feasibility study on a perceived fatigue prediction dependent power control for an electrically assisted bicycle.

    PubMed

    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. PMID:24110131

  16. Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization.

    PubMed

    Li, Zhijun; Xia, Yuanqing; Su, Chun-Yi; Deng, Jun; Fu, Jun; He, Wei

    2015-08-01

    In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed by incorporating missile inside constraints. The combined model predictive approach could be transformed as a constrained quadratic programming (QP) problem, which may be solved using a linear variational inequality-based primal-dual neural network over a finite receding horizon. Online solutions to multiple parametric QP problems are used so that constrained optimal control decisions can be made in real time. Simulation studies are conducted to illustrate the effectiveness and performance of the proposed guidance control law for missile interception.

  17. 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

  18. Machine learning and predictive data analytics enabling metrology and process control in IC fabrication

    NASA Astrophysics Data System (ADS)

    Rana, Narender; Zhang, Yunlin; Wall, Donald; Dirahoui, Bachir; Bailey, Todd C.

    2015-03-01

    Integrate circuit (IC) technology is going through multiple changes in terms of patterning techniques (multiple patterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tight controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Multivariate data with complex nonlinear trends and correlations generally cannot be described well by mathematical or parametric models but can be relatively easily learned by computing machines and used to predict or extrapolate. This paper introduces the predictive metrology approach which has been applied to three different applications. Machine learning and predictive analytics have been leveraged to accurately predict dimensions of EUV resist patterns down to 18 nm half pitch leveraging resist shrinkage patterns. These patterns could not be directly and accurately measured due to metrology tool limitations. Machine learning has also been applied to predict the electrical performance early in the process pipeline for deep trench capacitance and metal line resistance. As the wafer goes through various processes its associated cost multiplies. It may take days to weeks to get the electrical performance readout. Predicting the electrical performance early on can be very valuable in enabling timely actionable decision such as rework, scrap, feedforward, feedback predicted information or information derived from prediction to improve or monitor processes. This paper provides a general overview of machine learning and advanced analytics application in the advanced semiconductor development and manufacturing.

  19. Predicting changes in hypertension control using electronic health records from a chronic disease management program

    PubMed Central

    Sun, Jimeng; McNaughton, Candace D; Zhang, Ping; Perer, Adam; Gkoulalas-Divanis, Aris; Denny, Joshua C; Kirby, Jacqueline; Lasko, Thomas; Saip, Alexander; Malin, Bradley A

    2014-01-01

    Objective Common chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control. Method In a cohort of 1294 patients with hypertension enrolled in a chronic disease management program at the Vanderbilt University Medical Center, patients are modeled as an array of features derived from the clinical domain over time, which are distilled into a core set using an information gain criteria regarding their predictive performance. A model for transition point prediction was then computed using a random forest classifier. Results The most predictive features for transitions in hypertension control status included hypertension assessment patterns, comorbid diagnoses, procedures and medication history. The final random forest model achieved a c-statistic of 0.836 (95% CI 0.830 to 0.842) and an accuracy of 0.773 (95% CI 0.766 to 0.780). Conclusions This study achieved accurate prediction of transition points of hypertension control status, an important first step in the long-term goal of developing personalized hypertension management plans. PMID:24045907

  20. Nonlinear Predictive Control of Wind Energy Conversion System Using Dfig with Aerodynamic Torque Observer

    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.

  1. 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.

  2. PLIO: a generic tool for real-time operational predictive optimal control of water networks.

    PubMed

    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).

  3. PLIO: a generic tool for real-time operational predictive optimal control of water networks.

    PubMed

    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). PMID:22097020

  4. A robust model predictive control algorithm for uncertain nonlinear systems that guarantees resolvability

    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.

  5. Small Body GN&C Research Report: A Robust Model Predictive Control Algorithm with Guaranteed Resolvability

    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.

  6. Implicit theories about willpower predict the activation of a rest goal following self-control exertion.

    PubMed

    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. PMID:26075793

  7. Model predictive control of bidirectional isolated DC-DC converter for energy conversion system

    NASA Astrophysics Data System (ADS)

    Akter, Parvez; Uddin, Muslem; Mekhilef, Saad; Tan, Nadia Mei Lin; Akagi, Hirofumi

    2015-08-01

    Model predictive control (MPC) is a powerful and emerging control algorithm in the field of power converters and energy conversion systems. This paper proposes a model predictive algorithm to control the power flow between the high-voltage and low-voltage DC buses of a bidirectional isolated full-bridge DC-DC converter. The predictive control algorithm utilises the discrete nature of the power converters and predicts the future nature of the system, which are compared with the references to calculate the cost function. The switching state that minimises the cost function is selected for firing the converter in the next sampling time period. The proposed MPC bidirectional DC-DC converter is simulated with MATLAB/Simulink and further verified with a 2.5 kW experimental configuration. Both the simulation and experimental results confirm that the proposed MPC algorithm of the DC-DC converter reduces reactive power by avoiding the phase shift between primary and secondary sides of the high-frequency transformer and allow power transfer with unity power factor. Finally, an efficiency comparison is performed between the MPC and dual-phase-shift-based pulse-width modulation controlled DC-DC converter which ensures the effectiveness of the MPC controller.

  8. Implicit theories about willpower predict the activation of a rest goal following self-control exertion.

    PubMed

    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.

  9. 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.

  10. Nonlinear modeling and predictive functional control of Hammerstein system with application to the turntable servo system

    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.

  11. 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.

  12. Model predictive control of non-linear systems over networks with data quantization and packet loss.

    PubMed

    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.

  13. Model predictive control of non-linear systems over networks with data quantization and packet loss.

    PubMed

    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. PMID:26341070

  14. 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.

  15. Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

    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.

  16. Multi input single output model predictive control of non-linear bio-polymerization process

    SciTech Connect

    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.

  17. Supervisory sampling and control: Sources of suboptimality in a prediction task

    NASA Technical Reports Server (NTRS)

    Sheridan, T. B.; Rouse, W. B.

    1972-01-01

    A process supervisor is defined as a person who decides when to sample the process input and what values of a control variable to specify in order to maximize (minimize) a given value function of input sampling period, control setting, and process state. Presented experimental data in such a process where the value function is a time-averaged sampling cost plus mean squared difference between input and control variable. The task was unpaced prediction of the output of a second order filter driven by white noise. Experimental results, when compared to the optical strategy, reveal several consistently suboptimal behaviors. One is a tendency not to choose a long prediction interval even though the optimal strategy dictates that one should. Some results are also interpreted in terms of those input parameters according to which each subjects' behavior would have been nearest optimal. Differences of those parameters from actual input parameters served to quantify how subjects' prediction behavior differed from optimal.

  18. 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.

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

    PubMed

    Ouari, Kamel; Rekioua, Toufik; Ouhrouche, Mohand

    2014-01-01

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

  20. Can the annual flood control volume at Three Gorges Dam be predicted to size a variable flood control pool?

    NASA Astrophysics Data System (ADS)

    DONG, Q.; Lall, U.

    2014-12-01

    We consider the empirical prediction of the peak flood volume on the Yangtze River at the Three Gorges Dam. The dam is operated for flood control, hydropower production and irrigation. The flood control space reserved in the reservoir each year during the monsoon season limits the ability to supply hydropower and irrigation services. Allocating a variable amount of flood control space based on a pre-season forecast of the peak event flood volume, or of the flood volume over a specific duration is consequently, more useful than a prediction of the annual maximum peak flow for this dam and for other flood control dams. The joint distribution of annual peak flow, the corresponding flood volume, and the event duration is investigated based on the copula theory. A statistical model is developed for the conditional prediction of this joint distribution using pre-season climate indicators. The potential for the guidance for water management in the Yangtze River basin and for insights to the design of the large flood control reservoirs in the future is illustrated.

  1. 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.

  2. Environmental controls on the phenology of moths: predicting plasticity and constraint under climate change.

    PubMed

    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.

  3. 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.

  4. Toddler Inhibitory Control, Bold Response to Novelty, and Positive Affect Predict Externalizing Symptoms in Kindergarten

    PubMed Central

    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

  5. Neural Network-Based Resistance Spot Welding Control and Quality Prediction

    SciTech Connect

    Allen, J.D., Jr.; Ivezic, N.D.; Zacharia, T.

    1999-07-10

    This paper describes the development and evaluation of neural network-based systems for industrial resistance spot welding process control and weld quality assessment. The developed systems utilize recurrent neural networks for process control and both recurrent networks and static networks for quality prediction. The first section describes a system capable of both welding process control and real-time weld quality assessment, The second describes the development and evaluation of a static neural network-based weld quality assessment system that relied on experimental design to limit the influence of environmental variability. Relevant data analysis methods are also discussed. The weld classifier resulting from the analysis successfldly balances predictive power and simplicity of interpretation. The results presented for both systems demonstrate clearly that neural networks can be employed to address two significant problems common to the resistance spot welding industry, control of the process itself, and non-destructive determination of resulting weld quality.

  6. Nonlinear recurrent neural network predictive control for energy distribution of a fuel cell powered robot.

    PubMed

    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.

  7. Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot

    PubMed Central

    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

  8. A Two-Time Scale Decentralized Model Predictive Controller Based on Input and Output Model

    PubMed Central

    Niu, Jian; Zhao, Jun; Xu, Zuhua; Qian, Jixin

    2009-01-01

    A decentralized model predictive controller applicable for some systems which exhibit different dynamic characteristics in different channels was presented in this paper. These systems can be regarded as combinations of a fast model and a slow model, the response speeds of which are in two-time scale. Because most practical models used for control are obtained in the form of transfer function matrix by plant tests, a singular perturbation method was firstly used to separate the original transfer function matrix into two models in two-time scale. Then a decentralized model predictive controller was designed based on the two models derived from the original system. And the stability of the control method was proved. Simulations showed that the method was effective. PMID:19834542

  9. Experimental quadrotor flight performance using computationally efficient and recursively feasible linear model predictive control

    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.

  10. A gradient of childhood self-control predicts health, wealth, and public safety

    PubMed Central

    Moffitt, Terrie E.; Arseneault, Louise; Belsky, Daniel; Dickson, Nigel; Hancox, Robert J.; Harrington, HonaLee; Houts, Renate; Poulton, Richie; Roberts, Brent W.; Ross, Stephen; Sears, Malcolm R.; Thomson, W. Murray; Caspi, Avshalom

    2011-01-01

    Policy-makers are considering large-scale programs aimed at self-control to improve citizens’ health and wealth and reduce crime. Experimental and economic studies suggest such programs could reap benefits. Yet, is self-control important for the health, wealth, and public safety of the population? Following a cohort of 1,000 children from birth to the age of 32 y, we show that childhood self-control predicts physical health, substance dependence, personal finances, and criminal offending outcomes, following a gradient of self-control. Effects of children's self-control could be disentangled from their intelligence and social class as well as from mistakes they made as adolescents. In another cohort of 500 sibling-pairs, the sibling with lower self-control had poorer outcomes, despite shared family background. Interventions addressing self-control might reduce a panoply of societal costs, save taxpayers money, and promote prosperity. PMID:21262822

  11. 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.

  12. Controlling Bimetallic Nanostructures by the Microemulsion Method with Subnanometer Resolution Using a Prediction Model.

    PubMed

    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.

  13. T-S fuzzy model predictive speed control of electrical vehicles.

    PubMed

    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. PMID:27167988

  14. Predictive and reinforcement learning for magneto-hydrodynamic control of hypersonic flows

    NASA Astrophysics Data System (ADS)

    Kulkarni, Nilesh Vijay

    Increasing needs for autonomy in future aerospace systems and immense progress in computing technology have motivated the development of on-line adaptive control techniques to account for modeling errors, changes in system dynamics, and faults occurring during system operation. After extensive treatment of the inner-loop adaptive control dealing mainly with stable adaptation towards desired transient behavior, adaptive optimal control has started receiving attention in literature. Motivated by the problem of optimal control of the magneto-hydrodynamic (MHD) generator at the inlet of the scramjet engine of a hypersonic flight vehicle, this thesis treats the general problem of efficiently combining off-line and on-line optimal control methods. The predictive control approach is chosen as the off-line method for designing optimal controllers using all the existing system knowledge. This controller is then adapted on-line using policy-iteration-based Q-learning, which is a stable model-free reinforcement learning approach. The combined approach is first illustrated in the optimal control of linear systems, which helps in the analysis as well as the validation of the method. A novel neural-networks-based parametric predictive control approach is then designed for the off-line optimal control of non-linear systems. The off-line approach is illustrated by applications to aircraft and spacecraft systems. This is followed by an extensive treatment of the off-line optimal control of the MHD generator using this neuro-control approach. On-line adaptation of the controller is implemented using several novel schemes derived from the policy-iteration-based Q-learning. The implementation results demonstrate the success of these on-line algorithms for adapting towards modeling errors in the off-line design.

  15. Adaptive Control of the Packet Transmission Period with Solar Energy Harvesting Prediction in Wireless Sensor Networks

    PubMed Central

    Kwon, Kideok; Yang, Jihoon; Yoo, Younghwan

    2015-01-01

    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%. PMID:25919372

  16. Adaptive control of the packet transmission period with solar energy harvesting prediction in wireless sensor networks.

    PubMed

    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%.

  17. 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.

  18. Neural-network predictive control for nonlinear dynamic systems with time-delay.

    PubMed

    Huang, Jin-Quan; Lewis, F L

    2003-01-01

    A new recurrent neural-network predictive feedback control structure for a class of uncertain nonlinear dynamic time-delay systems in canonical form is developed and analyzed. The dynamic system has constant input and feedback time delays due to a communications channel. The proposed control structure consists of a linearized subsystem local to the controlled plant and a remote predictive controller located at the master command station. In the local linearized subsystem, a recurrent neural network with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. No linearity in the unknown parameters is required. No preliminary off-line weight learning is needed. The remote controller is a modified Smith predictor that provides prediction and maintains the desired tracking performance; an extra robustifying term is needed to guarantee stability. Rigorous stability proofs are given using Lyapunov analysis. The result is an adaptive neural net compensation scheme for unknown nonlinear systems with time delays. A simulation example is provided to demonstrate the effectiveness of the proposed control strategy.

  19. Lateral prefrontal cortex activity during cognitive control of emotion predicts response to social stress in schizophrenia.

    PubMed

    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.

  20. 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.

  1. 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…

  2. 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…

  3. 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…

  4. The Ability of Psychological Flexibility and Job Control to Predict Learning, Job Performance, and Mental Health

    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…

  5. Predicting Young Children's Externalizing Problems: Interactions among Effortful Control, Parenting, and Child Gender

    ERIC Educational Resources Information Center

    Karreman, Annemiek; van Tuijl, Cathy; van Aken, Marcel A. G.; Dekovi, Maja

    2009-01-01

    This study investigated interactions between observed temperamental effortful control and observed parenting in the prediction of externalizing problems. Child gender effects on these relations were examined. The relations were examined concurrently when the child was 3 years old and longitudinally at 4.5 years. The sample included 89 two-parent…

  6. Predicting Learned Helplessness and Achievement: The Role of Locus on Control and Motivational Orientation.

    ERIC Educational Resources Information Center

    Early, Diane; Barrett, Marty

    This 2-year study examined the relative potency of locus of control (LOC) and motivational orientation (MO) as predictors of standardized achievement scores and learned helplessness. Also tested was the prediction that children with an extrinsic MO would be prone to adopt an external LOC over time. In the first year of the study, subjects were 158…

  7. Predicting methyl iodide emission, soil concentration, and pest control in a two-dimensional chamber system.

    PubMed

    Luo, Lifang; Yates, Scott R; Ashworth, Daniel J

    2011-01-01

    Due to ever-increasing state and federal regulations, the future use of fumigants is predicted on reducing negative environmental impacts while offering sufficient pestcontrol efficacy. To foster the development of a best management practice, an integrated tool is needed to simultaneously predict fumigant movement and pest control without having to conduct elaborate and costly experiments. The objective of this study was (i) to present a two-dimensional (2-D) mathematical model to describe both fumigant movement and pestcontrol and (ii) to evaluate the model by comparing the simulated and observed results. Both analytical and numerical methods were used to predict methyl iodide (MeI) transport and fate. To predict pest control efficacy, the concentration-time index (CT) was defined and a two-parameter logistic survival model was used. Dose-response curves were experimentally determined for MeI against three types of pests (barnyardgrass [Echinochloa crus-galli] seed, citrus nematode [Tylenchulus semipenetrans], and fungi [Fusarium oxysporum]). Methyl iodide transport and pest control measurements collected from a 2-D experiimental system (60 by 60 cm) were used to test the model. Methyl iodide volatilization rates and soil gas-phase concentrations over time were accurately simulated by the model. The mass balance analysis indicates that the fraction of MeI degrading in the soil was underestimated when determined by the appearance of iodide concentration. The experimental results showed that after 24 h of MeI fumigation in the 2-D soil chamber, fungal population was not suppressed; > 90% of citrus nematodes were killed; and barnyardgrass seeds within 20-cm distance from the center were affected. These experimental results were consistent with the predicted results. The model accurately estimated the MeI movement and control of various pests and is a powerful tool to evaluate pesticides in terms of their negative environmental impacts and pest control under various

  8. 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.

  9. Higher Self-Control Capacity Predicts Lower Anxiety-Impaired Cognition during Math Examinations.

    PubMed

    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.

  10. Higher Self-Control Capacity Predicts Lower Anxiety-Impaired Cognition during Math Examinations.

    PubMed

    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

  11. Higher Self-Control Capacity Predicts Lower Anxiety-Impaired Cognition during Math Examinations

    PubMed Central

    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

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

    NASA Astrophysics Data System (ADS)

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

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

  13. Prediction of Regulation Reserve Requirements in California ISO Control Area based on BAAL Standard

    SciTech Connect

    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 .

  14. Improving the feed-forward compensator in predictive control for setpoint tracking.

    PubMed

    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. PMID:24657005

  15. Modeling a multivariable reactor and on-line model predictive control.

    PubMed

    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.

  16. 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.

  17. Toward Proof of Concept of a One Health Approach to Disease Prediction and Control

    PubMed Central

    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

  18. Real-time prediction of unsteady aerodynamics: Application for aircraft control and manoeuvrability enhancement.

    PubMed

    Faller, W E; Schreck, S J

    1995-01-01

    The capability to control unsteady separated flow fields could dramatically enhance aircraft agility. To enable control, however, real-time prediction of these flow fields over a broad parameter range must be realized. The present work describes real-time predictions of three-dimensional unsteady separated flow fields and aerodynamic coefficients using neural networks. Unsteady surface-pressure readings were obtained from an airfoil pitched at a constant rate through the static stall angle. All data sets were comprised of 15 simultaneously acquired pressure records and one pitch angle record. Five such records and the associated pitch angle histories were used to train the neural network using a time-series algorithm. Post-training, the input to the network was the pitch angle (alpha), the angular velocity (dalpha/dt), and the initial 15 recorded surface pressures at time (t (0)). Subsequently, the time (t+Deltat) network predictions, for each of the surface pressures, were fed back as the input to the network throughout the pitch history. The results indicated that the neural network accurately predicted the unsteady separated flow fields as well as the aerodynamic coefficients to within 5% of the experimental data. Consistent results were obtained both for the training set as well as for generalization to both other constant pitch rates and to sinusoidal pitch motions. The results clearly indicated that the neural-network model could predict the unsteady surface-pressure distributions and aerodynamic coefficients based solely on angle of attack information. The capability for real-time prediction of both unsteady separated flow fields and aerodynamic coefficients across a wide range of parameters in turn provides a critical step towards the development of control systems targeted at exploiting unsteady aerodynamics for aircraft manoeuvrability enhancement.

  19. Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR.

    PubMed

    Wu, Hsin-Yun; Gong, Cihun-Siyong Alex; Lin, Shih-Pin; Chang, Kuang-Yi; Tsou, Mei-Yung; Ting, Chien-Kun

    2016-01-01

    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. PMID:27247165

  20. Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR

    PubMed Central

    Wu, Hsin-Yun; Gong, Cihun-Siyong Alex; Lin, Shih-Pin; Chang, Kuang-Yi; Tsou, Mei-Yung; Ting, Chien-Kun

    2016-01-01

    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. PMID:27247165

  1. Control and prediction of the course of brewery fermentations by gravimetric analysis.

    PubMed

    Kosín, P; Savel, J; Broz, A; Sigler, K

    2008-01-01

    A simple, fast and cheap test suitable for predicting the course of brewery fermentations based on mass analysis is described and its efficiency is evaluated. Compared to commonly used yeast vitality tests, this analysis takes into account wort composition and other factors that influence fermentation performance. It can be used to predict the shape of the fermentation curve in brewery fermentations and in research and development projects concerning yeast vitality, fermentation conditions and wort composition. It can also be a useful tool for homebrewers to control their fermentations.

  2. Advanced Models and Controls for Prediction and Extension of Battery Lifetime (Presentation)

    SciTech Connect

    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.

  3. Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control.

    PubMed

    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

  4. Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control

    PubMed Central

    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

  5. Unity power factor converter based on a fuzzy controller and predictive input current.

    PubMed

    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.

  6. Model predictive control of a wet limestone flue gas desulfurization pilot plant

    SciTech Connect

    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.

  7. 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.

  8. Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory

    SciTech Connect

    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

  9. A novel predictive control algorithm and robust stability criteria for integrating processes.

    PubMed

    Zhang, Bin; Yang, Weimin; Zong, Hongyuan; Wu, Zhiyong; Zhang, Weidong

    2011-07-01

    This paper introduces a novel predictive controller for single-input/single-output (SISO) integrating systems, which can be directly applied without pre-stabilizing the process. The control algorithm is designed on the basis of the tested step response model. To produce a bounded system response along the finite predictive horizon, the effect of the integrating mode must be zeroed while unmeasured disturbances exist. Here, a novel predictive feedback error compensation method is proposed to eliminate the permanent offset between the setpoint and the process output while the integrating system is affected by load disturbance. Also, a rotator factor is introduced in the performance index, which is contributed to the improvement robustness of the closed-loop system. Then on the basis of Jury's dominant coefficient criterion, a robust stability condition of the resulted closed loop system is given. There are only two parameters which need to be tuned for the controller, and each has a clear physical meaning, which is convenient for implementation of the control algorithm. Lastly, simulations are given to illustrate that the proposed algorithm can provide excellent closed loop performance compared with some reported methods. PMID:21353217

  10. Variability in phenylalanine control predicts IQ and executive abilities in children with phenylketonuria.

    PubMed

    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. PMID:24568837

  11. Sense of Control Predicts Depressive and Anxious Symptoms Across the Transition to Parenthood

    PubMed Central

    Keeton, Courtney Pierce; Perry-Jenkins, Maureen; Sayer, Aline G.

    2010-01-01

    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. PMID:18410208

  12. Event-driven model predictive control of sewage pumping stations for sulfide mitigation in sewer networks.

    PubMed

    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.

  13. Event-driven model predictive control of sewage pumping stations for sulfide mitigation in sewer networks.

    PubMed

    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. PMID:27124127

  14. Lithium-ion battery cell-level control using constrained model predictive control and equivalent circuit models

    SciTech Connect

    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.

  15. HIV-1 DNA predicts disease progression and post-treatment virological control.

    PubMed

    Williams, James P; Hurst, Jacob; Stöhr, Wolfgang; Robinson, Nicola; Brown, Helen; Fisher, Martin; Kinloch, Sabine; Cooper, David; Schechter, Mauro; Tambussi, Giuseppe; Fidler, Sarah; Carrington, Mary; Babiker, Abdel; Weber, Jonathan; Koelsch, Kersten K; Kelleher, Anthony D; Phillips, Rodney E; Frater, John

    2014-01-01

    In HIV-1 infection, a population of latently infected cells facilitates viral persistence despite antiretroviral therapy (ART). With the aim of identifying individuals in whom ART might induce a period of viraemic control on stopping therapy, we hypothesised that quantification of the pool of latently infected cells in primary HIV-1 infection (PHI) would predict clinical progression and viral replication following ART. We measured HIV-1 DNA in a highly characterised randomised population of individuals with PHI. We explored associations between HIV-1 DNA and immunological and virological markers of clinical progression, including viral rebound in those interrupting therapy. In multivariable analyses, HIV-1 DNA was more predictive of disease progression than plasma viral load and, at treatment interruption, predicted time to plasma virus rebound. HIV-1 DNA may help identify individuals who could safely interrupt ART in future HIV-1 eradication trials. PMID:25217531

  16. Academic performance and social competence of adolescents: predictions based on effortful control and empathy.

    PubMed

    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.

  17. HIV-1 DNA predicts disease progression and post-treatment virological control.

    PubMed

    Williams, James P; Hurst, Jacob; Stöhr, Wolfgang; Robinson, Nicola; Brown, Helen; Fisher, Martin; Kinloch, Sabine; Cooper, David; Schechter, Mauro; Tambussi, Giuseppe; Fidler, Sarah; Carrington, Mary; Babiker, Abdel; Weber, Jonathan; Koelsch, Kersten K; Kelleher, Anthony D; Phillips, Rodney E; Frater, John

    2014-09-12

    In HIV-1 infection, a population of latently infected cells facilitates viral persistence despite antiretroviral therapy (ART). With the aim of identifying individuals in whom ART might induce a period of viraemic control on stopping therapy, we hypothesised that quantification of the pool of latently infected cells in primary HIV-1 infection (PHI) would predict clinical progression and viral replication following ART. We measured HIV-1 DNA in a highly characterised randomised population of individuals with PHI. We explored associations between HIV-1 DNA and immunological and virological markers of clinical progression, including viral rebound in those interrupting therapy. In multivariable analyses, HIV-1 DNA was more predictive of disease progression than plasma viral load and, at treatment interruption, predicted time to plasma virus rebound. HIV-1 DNA may help identify individuals who could safely interrupt ART in future HIV-1 eradication trials.

  18. Predicted versus experienced workload and performance on a supervisory control task

    NASA Technical Reports Server (NTRS)

    Battiste, V.; Hart, S. G.

    1985-01-01

    The multitask simulation of a supervisory control system was examined in order to evaluate the ability of operators to predict the workload and performance impact of unfamiliar task features, using their basic knowledge and specific information provided before each scenario. Task difficulty and experienced workload were varied by manipulating the number of elements per task, the number of tasks, task schedule, and availability of task elements for performance. The results have indicated that an operator might correctly predict the workload of a realistically complex task if (1) he is familiar with the basic system, and (2) the design, functional requirements, and operational procedures of the proposed modifications are described clearly. He is less able to predict unfamiliar rate or schedule complexity manipulations for which timing is an important element.

  19. Using micro saint to predict performance in a nuclear power plant control room

    SciTech Connect

    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.

  20. Within-socket myoelectric prediction of continuous ankle kinematics for control of a powered transtibial prosthesis

    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.

  1. Dopamine Gene Profiling to Predict Impulse Control and Effects of Dopamine Agonist Ropinirole.

    PubMed

    MacDonald, Hayley J; Stinear, Cathy M; Ren, April; Coxon, James P; Kao, Justin; Macdonald, Lorraine; Snow, Barry; Cramer, Steven C; Byblow, Winston D

    2016-07-01

    Dopamine agonists can impair inhibitory control and cause impulse control disorders for those with Parkinson disease (PD), although mechanistically this is not well understood. In this study, we hypothesized that the extent of such drug effects on impulse control is related to specific dopamine gene polymorphisms. This double-blind, placebo-controlled study aimed to examine the effect of single doses of 0.5 and 1.0 mg of the dopamine agonist ropinirole on impulse control in healthy adults of typical age for PD onset. Impulse control was measured by stop signal RT on a response inhibition task and by an index of impulsive decision-making on the Balloon Analogue Risk Task. A dopamine genetic risk score quantified basal dopamine neurotransmission from the influence of five genes: catechol-O-methyltransferase, dopamine transporter, and those encoding receptors D1, D2, and D3. With placebo, impulse control was better for the high versus low genetic risk score groups. Ropinirole modulated impulse control in a manner dependent on genetic risk score. For the lower score group, both doses improved response inhibition (decreased stop signal RT) whereas the lower dose reduced impulsiveness in decision-making. Conversely, the higher score group showed a trend for worsened response inhibition on the lower dose whereas both doses increased impulsiveness in decision-making. The implications of the present findings are that genotyping can be used to predict impulse control and whether it will improve or worsen with the administration of dopamine agonists. PMID:26942320

  2. 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.

  3. Prospective versus predictive control in timing of hitting a falling ball.

    PubMed

    Katsumata, Hiromu; Russell, Daniel M

    2012-02-01

    Debate exists as to whether humans use prospective or predictive control to intercept an object falling under gravity (Baurès et al. in Vis Res 47:2982-2991, 2007; Zago et al. in Vis Res 48:1532-1538, 2008). Prospective control involves using continuous information to regulate action. τ, the ratio of the size of the gap to the rate of gap closure, has been proposed as the information used in guiding interceptive actions prospectively (Lee in Ecol Psychol 10:221-250, 1998). This form of control is expected to generate movement modulation, where variability decreases over the course of an action based upon more accurate timing information. In contrast, predictive control assumes that a pre-programmed movement is triggered at an appropriate criterion timing variable. For a falling object it is commonly argued that an internal model of gravitational acceleration is used to predict the motion of the object and determine movement initiation. This form of control predicts fixed duration movements initiated at consistent time-to-contact (TTC), either across conditions (constant criterion operational timing) or within conditions (variable criterion operational timing). The current study sought to test predictive and prospective control hypotheses by disrupting continuous visual information of a falling ball and examining consistency in movement initiation and duration, and evidence for movement modulation. Participants (n = 12) batted a ball dropped from three different heights (1, 1.3 and 1.5 m), under both full-vision and partial occlusion conditions. In the occlusion condition, only the initial ball drop and the final 200 ms of ball flight to the interception point could be observed. The initiation of the swing did not occur at a consistent TTC, τ, or any other timing variable across drop heights, in contrast with previous research. However, movement onset was not impacted by occluding the ball flight for 280-380 ms. This finding indicates that humans did not

  4. 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.

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

    PubMed

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

    2012-01-01

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

  6. Stock management in hospital pharmacy using chance-constrained model predictive control.

    PubMed

    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. PMID:26724992

  7. Stock management in hospital pharmacy using chance-constrained model predictive control.

    PubMed

    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.

  8. Indoor environmental quality (IEQ) and building energy optimization through model predictive control (MPC)

    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

  9. 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

  10. Comparisons of Prediction Models of Myofascial Pain Control after Dry Needling: A Prospective Study

    PubMed Central

    Huang, Yuan-Ting; Neoh, Choo-Aun; Lin, Shun-Yuan

    2013-01-01

    Background. This study purposed to validate the use of artificial neural network (ANN) models for predicting myofascial pain control after dry needling and to compare the predictive capability of ANNs with that of support vector machine (SVM) and multiple linear regression (MLR). Methods. Totally 400 patients who have received dry needling treatments completed the Brief Pain Inventory (BPI) at baseline and at 1 year postoperatively. Results. Compared to the MLR and SVM models, the ANN model generally had smaller mean square error (MSE) and mean absolute percentage error (MAPE) values in the training dataset and testing dataset. Most ANN models had MAPE values ranging from 3.4% to 4.6% and most had high prediction accuracy. The global sensitivity analysis also showed that pretreatment BPI score was the best parameter for predicting pain after dry needling. Conclusion. Compared with the MLR and SVM models, the ANN model in this study was more accurate in predicting patient-reported BPI scores and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data. PMID:23853659

  11. The prediction and control of behavior revisited: a review of the matching law.

    PubMed

    Plaud, J J

    1992-03-01

    Experimental research conducted over the past 3 decades in relation to behavioral allocation and choice, collectively known as matching law research, is analyzed in this paper. The importance of the matching law for areas ranging from experimental to clinical psychology and psychiatry is discussed in relation to empirical findings that bear upon the validity and utility of the matching law for both the prediction and control of human behavior as well as for psychological and scientific inquiry in general.

  12. Planning horizon for a predictive optimal controller for thermal energy storage systems

    SciTech Connect

    Krarti, M.; Henze, G.P.; Bell, D.

    1999-07-01

    This paper presents the results of a detailed simulation analysis to determine the planning horizon for a predictive optimal controller for thermal energy storage (TES) systems. The objective of the simulation analysis is to determine the sensitivity of the performance of a TES optimal controller and the planning horizon length to different design parameters, including: chiller capacity, cooling plant model, storage system capacity, and load profile. The analysis is performed using two commercial buildings: a 20-floor office building in Wisconsin, and a hotel in California.

  13. Controlling bimetallic nanostructures by the microemulsion method with subnanometer resolution using a prediction model

    DOE PAGES

    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.

  14. Robust model predictive control by iterative optimisation for polytopic uncertain systems

    NASA Astrophysics Data System (ADS)

    Wang, Chuanxu

    2012-09-01

    This article addresses robust model predictive control (MPC) for constrained systems with polytopic uncertainty description. Firstly, in the technique which parametrises the infinite horizon control moves into a single state feedback law and invokes the parameter-dependent Lyapunov method for achieving closed-loop stability, the slack matrices are iteratively solved at each sampling time. Secondly, in the technique which parametrises the infinite horizon control moves into a set of free perturbations followed by a single state feedback law, the feedback gains within the switch horizon are iteratively found at each sampling time, rather than just inherited from the previous sampling time. Numerical examples show that iterative MPC can not only improve the control performance, but also enlarge the region of attraction.

  15. Design of a generalized predictive controller for a biological wastewater treatment plant.

    PubMed

    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. PMID:27120654

  16. 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.

  17. 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.

  18. Jacques Loeb, B. F. Skinner, and the legacy of prediction and control.

    PubMed

    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.

  19. Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

    PubMed

    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.

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

    NASA Technical Reports Server (NTRS)

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

    2013-01-01

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

  1. 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.

  2. Low speed hybrid generalized predictive control of a gasoline-propelled car.

    PubMed

    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.

  3. Low speed hybrid generalized predictive control of a gasoline-propelled car.

    PubMed

    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. PMID:25634584

  4. 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.

  5. 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.

  6. Testing predictions from the male control theory of men's partner violence.

    PubMed

    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.

  7. Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

    PubMed

    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. PMID:26336152

  8. Brain activity in predictive sensorimotor control for landings: an EEG pilot study.

    PubMed

    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. PMID:23740338

  9. Higher Executive Control and Visual Memory Performance Predict Treatment Completion in Borderline Personality Disorder

    PubMed Central

    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

  10. Efficacy of predictive wavefront control for compensating aero-optical aberrations

    NASA Astrophysics Data System (ADS)

    Goorskey, David J.; Schmidt, Jason; Whiteley, Matthew R.

    2013-07-01

    Imaging and laser beam propagation from airborne platforms are degraded by dynamic aberrations due to air flow around the aircraft, aero-mechanical distortions and jitter, and free atmospheric turbulence. For certain applications, like dim-object imaging, free-space optical communications, and laser weapons, adaptive optics (AO) is necessary to compensate for the aberrations in real time. Aero-optical flow is a particularly interesting source of aberrations whose flowing structures can be exploited by adaptive and predictive AO controllers, thereby realizing significant performance gains. We analyze dynamic aero-optical wavefronts to determine the pointing angles at which predictive wavefront control is more effective than conventional, fixed-gain, linear-filter control. It was found that properties of the spatial decompositions and temporal statistics of the wavefronts are directly traceable to specific features in the air flow. Furthermore, the aero-optical wavefront aberrations at the side- and aft-looking angles were the most severe, but they also benefited the most from predictive AO.

  11. Brain activity in predictive sensorimotor control for landings: an EEG pilot study.

    PubMed

    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.

  12. Does Sense of Control Predict Depression Among Individuals After Psychiatric Hospital Discharge?

    PubMed

    Kim, Yoo Jung; Fusco, Rachel A

    2015-11-01

    Sense of control is known to be related to depression. Yet, few studies have examined the role of sense of control as related to depression for discharged psychiatric patients. In this study the longitudinal relationship between sense of control and depressive mood was examined using the MacArthur Violence Risk Assessment Study, a 6-wave, 1-year study of 948 ethnically diverse postdischarge psychiatric patients. Sense of control was decomposed into 2 components (i.e., a time-invariant as well as a time-varying component) and so as to examine which component of sense of control would more accurately explain this relationship. Results demonstrated that time-varying sense of control significantly predicted changes in depressive mood during the transition to community environment. Time-invariant sense of control, however, was not significantly related to changes in depressive mood. Findings of this study hold important implications for intervention practice with people before or after psychiatric discharge, including the need for incorporation of therapeutic and psychoeducational efforts that bolster sense of control.

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

    PubMed

    Bigdeli, N; Haeri, M

    2009-01-01

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

  14. Flatness Control Using Roll Coolant Based on Predicted Flatness Variation in Cold Rolling Mills

    NASA Astrophysics Data System (ADS)

    Dohmae, Yukihiro; Okamura, Yoshihide

    Flatness control for cold rolling mills is one of the important technologies for improving of product quality and productivity. In particular, poor flatness leads to strip tearing in the extreme case and, moreover, it significantly reduces productivity. Therefore, various flatness control system has been developed. The main actuators for flatness control are classified into two types; one is mechanical equipment such as roll bender, the other is roll coolant, which controls thermal expansion of roll. Flatness variation such as center buckle or edge wave is mainly controlled by mechanical actuator which has high response characteristics. On another front, flatness variation of local zone can be controlled by roll coolant although one's response is lower than the response of mechanical actuator. For accomplishing good flatness accuracy in cold rolling mills, it is important to improve the performance of coolant control moreover. In this paper, a new coolant control method based on flatness variation model is described. In proposed method, the state of coolant spray on or off is selected to minimize the flatness deviation by using predicted flatness variation. The effectiveness of developed system has been demonstrated by application in actual plant.

  15. Flood mitigation through optimal control of a network of multi-purpose reservoirs by using Model Predictive Control

    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.

  16. Threat Interference Biases Predict Socially Anxious Behavior: The Role of Inhibitory Control and Minute of Stressor.

    PubMed

    Gorlin, Eugenia I; Teachman, Bethany A

    2015-07-01

    The current study brings together two typically distinct lines of research. First, social anxiety is inconsistently associated with behavioral deficits in social performance, and the factors accounting for these deficits remain poorly understood. Second, research on selective processing of threat cues, termed cognitive biases, suggests these biases typically predict negative outcomes, but may sometimes be adaptive, depending on the context. Integrating these research areas, the current study examined whether conscious and/or unconscious threat interference biases (indexed by the unmasked and masked emotional Stroop) can explain unique variance, beyond self-reported anxiety measures, in behavioral avoidance and observer-rated anxious behavior during a public speaking task. Minute of speech and general inhibitory control (indexed by the color-word Stroop) were examined as within-subject and between-subject moderators, respectively. Highly socially anxious participants (N=135) completed the emotional and color-word Stroop blocks prior to completing a 4-minute videotaped speech task, which was later coded for anxious behaviors (e.g., speech dysfluency). Mixed-effects regression analyses revealed that general inhibitory control moderated the relationship between both conscious and unconscious threat interference bias and anxious behavior (though not avoidance), such that lower threat interference predicted higher levels of anxious behavior, but only among those with relatively weaker (versus stronger) inhibitory control. Minute of speech further moderated this relationship for unconscious (but not conscious) social-threat interference, such that lower social-threat interference predicted a steeper increase in anxious behaviors over the course of the speech (but only among those with weaker inhibitory control). Thus, both trait and state differences in inhibitory control resources may influence the behavioral impact of threat biases in social anxiety. PMID:26163713

  17. Changes in predictive motor control in drop-jumps based on uncertainties in task execution.

    PubMed

    Leukel, Christian; Taube, Wolfgang; Lorch, Michael; Gollhofer, Albert

    2012-02-01

    Drop-jumps are controlled by predictive and reactive motor strategies which differ with respect to the utilization of sensory feedback. With reaction, sensory feedback is integrated while performing the task. With prediction, sensory information may be used prior to movement onset. Certainty about upcoming events is important for prediction. The present study aimed at investigating how uncertainties in the task execution affect predictive motor control in drop-jumps. Ten healthy subjects (22±1 years, M±SD) participated. The subjects performed either (i) drop-jumps by knowing that they might had to switch to a landing movement upon an auditory cue, which was sometimes elicited prior to touch-down (uncertainty). In (ii), subjects performed drop-jumps by knowing that there would be no auditory cue and consequently no switch of the movement (certainty). The m. soleus EMG prior to touch-down was higher when subjects knew there would be no auditory cue compared to when subjects performed the same task but switching from drop-jump to landing was possible (uncertainty). The EMG was reversed in the late concentric phase, meaning that it was higher in the high uncertainty task. The results of the present study showed that the muscular activity was predictively adjusted according to uncertainties in task execution. It is argued that tendomuscular stiffness was the variable responsible for the adjustment of muscular activity. The required tendomuscular stiffness was higher in drop-jumps than in landings. Consequently, when it was not certain whether to jump or to land, muscular activity and therefore tendomuscular stiffness was reduced. PMID:21757248

  18. Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint

    SciTech Connect

    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.

  19. 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.

  20. Establishing Causality Using Longitudinal Hierarchical Linear Modeling: An Illustration Predicting Achievement From Self-Control

    PubMed Central

    Duckworth, Angela Lee; Tsukayama, Eli; May, Henry

    2010-01-01

    The predictive validity of personality for important life outcomes is well established, but conventional longitudinal analyses cannot rule out the possibility that unmeasured third-variable confounds fully account for the observed relationships. Longitudinal hierarchical linear models (HLM) with time-varying covariates allow each subject to serve as his or her own control, thus eliminating between-individual confounds. HLM also allows the directionality of the causal relationship to be tested by reversing time-lagged predictor and outcome variables. We illustrate these techniques through a series of models that demonstrate that within-individual changes in self-control over time predict subsequent changes in GPA but not vice-versa. The evidence supporting a causal role for self-control was not moderated by IQ, gender, ethnicity, or income. Further analyses rule out one time-varying confound: self-esteem. The analytic approach taken in this study provides the strongest evidence to date for the causal role of self-control in determining achievement. PMID:20976121

  1. Cognitive Control Predicts Use of Model-Based Reinforcement-Learning

    PubMed Central

    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

  2. GOBF-ARMA based model predictive control for an ideal reactive distillation column.

    PubMed

    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.

  3. Why achievement motivation predicts success in business but failure in politics: the importance of personal control.

    PubMed

    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.

  4. Model Predictive Wind Turbine Control with Move-Blocking Strategy for Load Alleviation and Power Leveling

    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.

  5. State dependent model predictive control for orbital rendezvous using pulse-width pulse-frequency modulated thrusters

    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.

  6. Level and Change in Perceived Control Predict 19-Year Mortality: Findings from the Americans' Changing Lives Study

    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…

  7. OVARIAN RESERVE TESTS AND THEIR UTILITY IN PREDICTING RESPONSE TO CONTROLLED OVARIAN STIMULATION IN RHESUS MONKEYS

    PubMed Central

    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

  8. Can Self-Prediction Overcome Barriers to Hepatitis B Vaccination? A Randomized Controlled Trial

    PubMed Central

    Cox, Anthony D.; Cox, Dena; Cyrier, Rosalie; Graham-Dotson, Yolanda; Zimet, Gregory D.

    2011-01-01

    Objective Hepatitis B virus (HBV) infection remains a serious public health problem, due in part to low vaccination rates among high-risk adults, many of whom decline vaccination because of barriers such as perceived inconvenience or discomfort. This study evaluates the efficacy of a self-prediction intervention to increase HBV vaccination rates among high-risk adults. Method Randomized controlled trial of 1175 adults recruited from three STD clinics in the United States over 28 months. Participants completed an audio-computer-assisted self-interview (A-CASI), which presented information about HBV infection and vaccination, and measured relevant beliefs, behaviors and demographics. Half of participants were assigned randomly to a "self-prediction" intervention, asking them to predict their future acceptance of HBV vaccination. The main outcome measure was subsequent vaccination behavior. Other measures included perceived barriers to HBV vaccination, measured prior to the intervention. Results There was a significant interaction between the intervention and vaccination barriers, indicating the effect of the intervention differed depending on perceived vaccination barriers. Among high-barriers patients, the intervention significantly increased vaccination acceptance. Among low-barrier patients, the intervention did not influence vaccination acceptance. Conclusions The self-prediction intervention significantly increased vaccination acceptance among "high-barriers" patients, who typically have very low vaccination rates. This brief intervention could be a useful tool in increasing vaccine uptake among high-barriers patients. PMID:21875205

  9. 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.

  10. Hedonic Hunger Prospectively Predicts Onset and Maintenance of Loss of Control Eating among College Women

    PubMed Central

    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

  11. 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

  12. Predictive control for voltage collapse avoidance using a modified discrete multi-valued PSO algorithm.

    PubMed

    Pourjafari, Ebrahim; Mojallali, Hamed

    2011-04-01

    Voltage stability is one of the most challenging concerns that power utilities are confronted with, and this paper proposes a voltage control scheme based on Model Predictive Control (MPC) to overcome this kind of instability. Voltage instability has a close relation with the adequacy of reactive power and the response of Under Load Tap Changers (ULTCs) to the voltage drop after the occurrence of a contingency. Therefore, the proposed method utilizes reactive power injection and tap changing to avoid voltage collapse. Considering discrete nature of the changes in the tap ratio and also in the reactive power injected by capacitor banks, the search area for the optimizer of MPC will be an integer area; consequently, a modified discrete multi-valued Particle Swarm Optimization (PSO) is considered to perform this optimization. Simulation results of applying the proposed control scheme to a 4-bus system confirm its capability to prevent voltage collapse. PMID:21251650

  13. High Accuracy Liquid Propellant Slosh Predictions Using an Integrated CFD and Controls Analysis Interface

    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.

  14. 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.

  15. Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

    SciTech Connect

    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.

  16. Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

    SciTech Connect

    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.

  17. 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.

  18. Model Predictions of Chemically Controlled Slow Crack Growth with Application to Mechanical Effects in Geothermal Environments

    SciTech Connect

    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.

  19. High-order distortion control using a computational prediction method for device overlay

    NASA Astrophysics Data System (ADS)

    Kang, Young-Seog; Affentauschegg, Cedric; Mulkens, Jan; Kim, Jang-Sun; Shin, Ju-Hee; Kim, Young-Ha; Nam, Young-Sun; Choi, Young-Sin; Ha, Hunhwan; Lee, Dong-Han; Lee, Jae-il; Rizvi, Umar; Geh, Bernd; van der Heijden, Rob; Baselmans, Jan; Kwon, Oh-Sung

    2016-04-01

    As a result of the continuously shrinking features of the integrated circuit, the overlay budget requirements have become very demanding. Historically, overlay has been performed using metrology targets for process control, and most overlay enhancements were achieved by hardware improvements. However, this is no longer sufficient, and we need to consider additional solutions for overlay improvements in process variation using computational methods. In this paper, we present the limitations of third-order intrafield distortion corrections based on standard overlay metrology and propose an improved method which includes a prediction of the device overlay and corrects the lens aberration fingerprint based on this prediction. For a DRAM use case, we present a computational approach that calculates the overlay of the device pattern using lens aberrations as an additional input, next to the target-based overlay measurement result. Supporting experimental data are presented that demonstrate a significant reduction of the intrafield overlay fingerprint.

  20. Evaluation of Transport and Dispersion Models: A Controlled Comparison of HPAC and NARAC Predictions

    SciTech Connect

    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.

  1. Pole-placement Predictive Functional Control for over-damped systems with real poles.

    PubMed

    Rossiter, J A; Haber, R; Zabet, K

    2016-03-01

    This paper gives new insight and design proposals for Predictive Functional Control (PFC) algorithms. Common practice and indeed a requirement of PFC is to select a coincidence horizon greater than one for high-order systems and for the link between the design parameters and the desired dynamic to be weak. Here the proposal is to use parallel first-order models to form an independent prediction model and show that with these it is possible both to use a coincidence horizon of one and moreover to obtain precisely the desired closed-loop dynamics. It is shown through analysis that the use of a coincidence horizon of one greatly simplifies coding, tuning, constraint handling and implementation. The paper derives the key results for high-order and non-minimum phase processes and also demonstrates the flexibility and potential industrial utility of the proposal.

  2. Pole-placement Predictive Functional Control for over-damped systems with real poles.

    PubMed

    Rossiter, J A; Haber, R; Zabet, K

    2016-03-01

    This paper gives new insight and design proposals for Predictive Functional Control (PFC) algorithms. Common practice and indeed a requirement of PFC is to select a coincidence horizon greater than one for high-order systems and for the link between the design parameters and the desired dynamic to be weak. Here the proposal is to use parallel first-order models to form an independent prediction model and show that with these it is possible both to use a coincidence horizon of one and moreover to obtain precisely the desired closed-loop dynamics. It is shown through analysis that the use of a coincidence horizon of one greatly simplifies coding, tuning, constraint handling and implementation. The paper derives the key results for high-order and non-minimum phase processes and also demonstrates the flexibility and potential industrial utility of the proposal. PMID:26723844

  3. Power and Performance Management in Nonlinear Virtualized Computing Systems via Predictive Control

    PubMed Central

    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

  4. 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.

  5. 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

  6. Nonparametric Hammerstein model based model predictive control for heart rate regulation.

    PubMed

    Su, Steven W; Huang, Shoudong; Wang, Lu; Celler, Branko G; Savkin, Andrey V; Guo, Ying; Cheng, Teddy

    2007-01-01

    This paper proposed a novel nonparametric model based model predictive control approach for the regulation of heart rate during treadmill exercise. As the model structure of human cardiovascular system is often hard to determine, nonparametric modelling is a more realistic manner to describe complex behaviours of cardiovascular system. This paper presents a new nonparametric Hammerstein model identification approach for heart rate response modelling. Based on the pseudo-random binary sequence experiment data, we decouple the identification of linear dynamic part and input nonlinearity of the Hammerstein system. Correlation analysis is applied to acquire step response of linear dynamic component. Support Vector Regression is adopted to obtain a nonparametric description of the inverse of input static nonlinearity that is utilized to form an approximate linear model of the Hammerstein system. Based on the established model, a model predictive controller under predefined speed and acceleration constraints is designed to achieve safer treadmill exercise. Simulation results show that the proposed control algorithm can achieve optimal heart rate tracking performance under predefined constraints.

  7. Perceived Sexual Control, Sex-Related Alcohol Expectancies and Behavior Predict Substance-Related Sexual Revictimization

    PubMed Central

    Walsh, Kate; Messman-Moore, Terri; Zerubavel, Noga; Chandley, Rachel B.; DeNardi, Kathleen A.; Walker, Dave P.

    2013-01-01

    Objectives Although numerous studies have documented linkages between childhood sexual abuse (CSA) and later sexual revictimization, mechanisms underlying revictimization, particularly assaults occurring in the context of substance use, are not well-understood. Consistent with Traumagenic Dynamics theory, the present study tested a path model positing that lowered perceptions of sexual control resulting from CSA may be associated with increased sex-related alcohol expectancies and heightened likelihood of risky sexual behavior, which in turn, may predict adult substance-related rape. Methods Participants were 546 female college students who completed anonymous surveys regarding CSA and adult rape, perceptions of sexual control, sex-related alcohol expectancies, and likelihood of engaging in risky sexual behavior. Results The data fit the hypothesized model well and all hypothesized path coefficients were significant and in the expected directions. As expected, sex-related alcohol expectancies and likelihood of risky sexual behavior only predicted substance-related rape, not forcible rape. Conclusions Findings suggested that low perceived sexual control stemming from CSA is associated with increased sex-related alcohol expectancies and a higher likelihood of engaging in sexual behavior in the context of alcohol use. In turn these proximal risk factors heighten vulnerability to substance-related rape. Programs which aim to reduce risk for substance-related rape could be improved by addressing expectancies and motivations for risky sexual behavior in the context of substance use. Implications and future directions are discussed. PMID:23312991

  8. Low Vitamin D Levels Do Not Predict Hyperglycemia in Elderly Endurance Athletes (but in Controls)

    PubMed Central

    Nistler, Sonja; Batmyagmar, Delgerdalai; Ponocny-Seliger, Elisabeth; Perkmann, Thomas; Scherzer, Thomas M.; Kundi, Michael; Endler, Georg; Ratzinger, Franz; Pilger, Alexander; Wagner, Oswald F.; Winker, Robert

    2016-01-01

    Background and Aim Recent studies revealed a link between hypovitaminosis D3 and the risk for hyperglycemia. Further mechanistic and interventional investigations suggested a common reason for both conditions rather than a causal relationship. Exposure to sunlight is the most relevant source of vitamin D3 (25(OH)D), whereas adipose tissue is able to store relevant amounts of the lipophilic vitamin. Since running/bicycling leads to increased out-door time and alters physiological response mechanisms, it can be hypothesized that the correlation between hypovitaminosis D3 and hyperglycemia might be disturbed in outdoor athletes. Methods 47 elderly marathoners/bicyclists and 47 age/sex matched controls were studied in a longitudinal setting at baseline and after three years. HbA1c as a surrogate for (pre-)diabetic states was quantified via HPLC, 25(OH)D levels were measured by means of chemiluminescent assays. Physical performance was assessed by ergometry. Results When adjusted for seasonal variations, 25(OH)D was significantly higher in athletes than in controls. 25(OH)D levels inversely correlated with triglycerides in both groups, whereas only in controls an association between high BMI or low physical performance with hypovitaminosis D3 had been found. Likewise, the presence of hypovitaminosis D3 at baseline successfully predicted hyperglycemia at the follow up examinations within the control group (AUC = 0.85, 95% CI [0.74, 0.96], p < .001, statistically independent from BMI), but not in athletes. Conclusion Our data suggest that mechanisms of HbA1c elevation might differ between athletes and controls. Thus, intense physical activity must be taken into account as a potential pre-analytic confounder when it is aimed to predict metabolic risk by vitamin D3 levels. PMID:27304888

  9. Internally directed cognition and mindfulness: an integrative perspective derived from predictive and reactive control systems theory

    PubMed Central

    Tops, Mattie; Boksem, Maarten A. S.; Quirin, Markus; IJzerman, Hans; Koole, Sander L.

    2013-01-01

    In the present paper, we will apply the predictive and reactive control systems (PARCS) theory as a framework that integrates competing theories of neural substrates of awareness by describing the “default mode network” (DMN) and anterior insula (AI) as parts of two different behavioral and homeostatic control systems. The DMN, a network that becomes active at rest when there is no external stimulation or task to perform, has been implicated in self-reflective awareness and prospection. By contrast, the AI is associated with awareness and task-related attention. This has led to competing theories stressing the role of the DMN in self-awareness vs. the role of interoceptive and emotional information integration in the AI in awareness of the emotional moment. In PARCS, the respective functions of the DMN and AI in a specific control system explains their association with different qualities of awareness, and how mental states can shift from one state (e.g., prospective self-reflection) to the other (e.g., awareness of the emotional moment) depending on the relative dominance of control systems. These shifts between reactive and predictive control are part of processes that enable the intake of novel information, integration of this novel information within existing knowledge structures, and the creation of a continuous personal context in which novel information can be integrated and understood. As such, PARCS can explain key characteristics of mental states, such as their temporal and spatial focus (e.g., a focus on the here and now vs. the future; a first person vs. a third person perspective). PARCS further relates mental states to brain states and functions, such as activation of the DMN or hemispheric asymmetry in frontal cortical functions. Together, PARCS deepens the understanding of a broad range of mental states, including mindfulness, mind wandering, rumination, autobiographical memory, imagery, and the experience of self. PMID:24904455

  10. Multivariate morphological brain signatures predict chronic abdominal pain patients from healthy control subjects

    PubMed Central

    Labus, Jennifer S.; Van Horn, John D.; Gupta, Arpana; Alaverdyan, Mher; Torgerson, Carinna; Ashe-McNalley, Cody; Irimia, Andrei; Hong, Jui-Yang; Naliboff, Bruce; Tillisch, Kirsten; Mayer, Emeran A.

    2015-01-01

    Irritable bowel syndrome (IBS) is the most common chronic visceral pain disorder. The pathophysiology of IBS is incompletely understood, however evidence strongly suggests dysregulation of the brain-gut axis. The aim of this study was to apply multivariate pattern analysis to identify an IBS-related morphometric brain signature which could serve as a central biological marker and provide new mechanistic insights into the pathophysiology of IBS. Parcellation of 165 cortical and subcortical regions was performed using Freesurfer and the Destrieux and Harvard-Oxford atlases. Volume, mean curvature, surface area and cortical thickness were calculated for each region. Sparse partial least squares-discriminant analysis was applied to develop a diagnostic model using a training set of 160 females (80 healthy controls, 80 IBS). Predictive accuracy was assessed in an age matched holdout test set of 52 females (26 health controls, 26 IBS). A two-component classification algorithm comprised of the morphometry of 1) primary somato-sensory and motor regions, and 2) multimodal network regions, explained 36% of the variance. Overall predictive accuracy of the classification algorithm was 70%. Small effect size associations were observed between the somatosensory and motor signature and non-gastrointestinal somatic symptoms. The findings demonstrate the predictive accuracy of a classification algorithm based solely on regional brain morphometry is not sufficient but they do provide support for the utility of multivariate pattern analysis for identifying meaningful neurobiological markers in IBS. Perspective This article presents the development, optimization, and testing of a classification algorithm for discriminating female IBS patients from healthy controls using only brain morphometry data. The results provide support for utility of multivariate pattern analysis for identifying meaningful neurobiological markers in IBS. PMID:25906347

  11. Prediction and Control of Slip-Free Rotation States in Sphere Assemblies.

    PubMed

    Stäger, D V; Araújo, N A M; Herrmann, H J

    2016-06-24

    We study fixed assemblies of touching spheres that can individually rotate. From any initial state, sliding friction drives an assembly toward a slip-free rotation state. For bipartite assemblies, which have only even loops, this state has at least four degrees of freedom. For exactly four degrees of freedom, we analytically predict the final state, which we prove to be independent of the strength of sliding friction, from an arbitrary initial one. With a tabletop experiment, we show how to impose any slip-free rotation state by only controlling two spheres, regardless of the total number.

  12. Numerical prediction of energy consumption in buildings with controlled interior temperature

    SciTech Connect

    Jarošová, P.; Št’astník, S.

    2015-03-10

    New European directives bring strong requirement to the energy consumption of building objects, supporting the renewable energy sources. Whereas in the case of family and similar houses this can lead up to absurd consequences, for building objects with controlled interior temperature the optimization of energy demand is really needed. The paper demonstrates the system approach to the modelling of thermal insulation and accumulation abilities of such objetcs, incorporating the significant influence of additional physical processes, as surface heat radiation and moisture-driven deterioration of insulation layers. An illustrative example shows the numerical prediction of energy consumption of a freezing plant in one Central European climatic year.

  13. Applications of aero-acoustics to wind turbine noise prediction and control

    NASA Astrophysics Data System (ADS)

    Lowson, Martin V.

    1993-01-01

    Wind turbine noise generation mechanisms are essentially equivalent to the aero-acoustic mechanisms of other rotors, which have been studied in depth for many years. Basic sources for the wind turbine noise radiation process are defined, and their significance assessed. From the analysis, areas of potential improvement in wind turbine noise prediction are defined. Suggestions are made for approaches to wind turbine noise control which separate the noise problems at cut-in from those at rated power. Some of these offer the possibility of noise reduction without unfavourable effects on performance.

  14. Predicted magnetoelectric effect in Fe/BaTiO3 multilayers: ferroelectric control of magnetism.

    PubMed

    Duan, Chun-Gang; Jaswal, S S; Tsymbal, E Y

    2006-07-28

    An unexplored physical mechanism which produces a magnetoelectric effect in ferroelectric-ferromagnetic multilayers is studied based on first-principles calculations. Its origin is a change in bonding at the ferroelectric-ferromagnet interface that alters the interface magnetization when the electric polarization reverses. Using Fe/BaTiO3 multilayers as a representative model, we show a sizable difference in magnetic moments of Fe and Ti atoms at the two interfaces dissimilar by the orientation of the local electric dipole moments. The predicted magnetoelectric effect opens a new direction to control magnetic properties of thin-film layered structures by electric fields. PMID:16907608

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

    NASA Astrophysics Data System (ADS)

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

    2015-01-01

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

  16. Prediction and control of vortex-dominated and vortex-wake flows

    NASA Technical Reports Server (NTRS)

    Kandil, Osama

    1993-01-01

    This progress report documents the accomplishments achieved in the period from December 1, 1992 until November 30, 1993. These accomplishments include publications, national and international presentations, NASA presentations, and the research group supported under this grant. Topics covered by documents incorporated into this progress report include: active control of asymmetric conical flow using spinning and rotary oscillation; supersonic vortex breakdown over a delta wing in transonic flow; shock-vortex interaction over a 65-degree delta wing in transonic flow; three dimensional supersonic vortex breakdown; numerical simulation and physical aspects of supersonic vortex breakdown; and prediction of asymmetric vortical flows around slender bodies using Navier-Stokes equations.

  17. A swarm intelligence-based tuning method for the Sliding Mode Generalized Predictive Control.

    PubMed

    Oliveira, J B; Boaventura-Cunha, J; Moura Oliveira, P B; Freire, H

    2014-09-01

    This work presents an automatic tuning method for the discontinuous component of the Sliding Mode Generalized Predictive Controller (SMGPC) subject to constraints. The strategy employs Particle Swarm Optimization (PSO) to minimize a second aggregated cost function. The continuous component is obtained by the standard procedure, by Quadratic Programming (QP), thus yielding an online dual optimization scheme. Simulations and performance indexes for common process models in industry, such as nonminimum phase and time delayed systems, result in a better performance, improving robustness and tracking accuracy.

  18. On prediction of longitudinal attitude of planing craft based on controllable hydrofoils

    NASA Astrophysics Data System (ADS)

    Ling, Hongjie; Wang, Zhidong; Wu, Na

    2013-09-01

    The purpose of this research study was to examine the attitude response of a planing craft under the controllable hydrofoils. Firstly, a non-linear longitudinal attitude model was established. In the mathematical model, effects of wind loads were considered. Both the wetted length and windward area varied in different navigation conditions. Secondly, control strategies for hydrofoils were specified. Using the above strategies, the heave and trim of the planing craft was adjusted by controllable hydrofoils. Finally, a simulation program was developed to predict the longitudinal attitudes of the planing craft with wind loads. A series of simulations were performed and effects of control strategies on longitudinal attitudes were analyzed. The results show that under effects of wind loads, heave of fixed hydrofoils planing craft decreased by 6.3%, and pitch increased by 8.6% when the main engine power was constant. Heave decreased by less than 1% and trim angle decreased by 1.7% as a result of using variable attack angle hydrofoils; however, amplitude changes of heave and pitch were less than 1% under the control of changeable attack angle hydrofoils and longitudinal attitude.

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

    PubMed

    Arrieta-Camacho, Juan José; Biegler, Lorenz T

    2005-12-01

    Real time optimal guidance is considered for a class of low thrust spacecraft. In particular, nonlinear model predictive control (NMPC) is utilized for computing the optimal control actions required to transfer a spacecraft from a low Earth orbit to a mission orbit. The NMPC methodology presented is able to cope with unmodeled disturbances. The dynamics of the transfer are modeled using a set of modified equinoctial elements because they do not exhibit singularities for zero inclination and zero eccentricity. The idea behind NMPC is the repeated solution of optimal control problems; at each time step, a new control action is computed. The optimal control problem is solved using a direct method-fully discretizing the equations of motion. The large scale nonlinear program resulting from the discretization procedure is solved using IPOPT--a primal-dual interior point algorithm. Stability and robustness characteristics of the NMPC algorithm are reviewed. A numerical example is presented that encourages further development of the proposed methodology: the transfer from low-Earth orbit to a molniya orbit. PMID:16510409

  20. Algorithms for a Closed-Loop Artificial Pancreas: The Case for Model Predictive Control

    PubMed Central

    Bequette, B. Wayne

    2013-01-01

    The relative merits of model predictive control (MPC) and proportional-integral-derivative (PID) control are discussed, with the end goal of a closed-loop artificial pancreas (AP). It is stressed that neither MPC nor PID are single algorithms, but rather are approaches or strategies that may be implemented very differently by different engineers. The primary advantages to MPC are that (i) constraints on the insulin delivery rate (and/or insulin on board) can be explicitly included in the control calculation; (ii) it is a general framework that makes it relatively easy to include the effect of meals, exercise, and other events that are a function of the time of day; and (iii) it is flexible enough to include many different objectives, from set-point tracking (target) to zone (control to range). In the end, however, it is recognized that the control algorithm, while important, represents only a portion of the effort required to develop a closed-loop AP. Thus, any number of algorithms/approaches can be successful—the engineers involved in the design must have experience with the particular technique, including the important experience of implementing the algorithm in human studies and not simply through simulation studies. PMID:24351190

  1. 1-Bit processing based model predictive control for fractionated satellite missions

    NASA Astrophysics Data System (ADS)

    Bai, Xueliang; Wu, Xiaofeng

    2014-02-01

    The model predictive control (MPC) has great advantages in dealing with complex control constraints. However, traditional MPCs are too complex to implement in real-time embedded systems. This is especially true for nano-satellites due to limited on-board resources. This paper introduces a novel 1-bit processing based MPC (OBMPC) algorithm for a fractionated satellite mission, which can significantly reduce online calculations by removing multiply operations. The resulted pulse signals can be used to drive the actuator directly. The quantized state feedback fits the OBMPC in the frame work of quantized MPC. The stability issues and the design criterion are discussed in this paper. The simulation is based on a 2U CubeSat model in a fractionated satellite structure, in which the payload and actuators are separated from the controller and controlled via wireless inter-satellite link (ISL). Compared to the equivalent traditional MPC controller, FPGA implementation based performance analysis shows that OBMPC is feasible for fractionated satellite missions.

  2. Modelling and control issues of dynamically substructured systems: adaptive forward prediction taken as an example

    PubMed Central

    Tu, Jia-Ying; Hsiao, Wei-De; Chen, Chih-Ying

    2014-01-01

    Testing techniques of dynamically substructured systems dissects an entire engineering system into parts. Components can be tested via numerical simulation or physical experiments and run synchronously. Additional actuator systems, which interface numerical and physical parts, are required within the physical substructure. A high-quality controller, which is designed to cancel unwanted dynamics introduced by the actuators, is important in order to synchronize the numerical and physical outputs and ensure successful tests. An adaptive forward prediction (AFP) algorithm based on delay compensation concepts has been proposed to deal with substructuring control issues. Although the settling performance and numerical conditions of the AFP controller are improved using new direct-compensation and singular value decomposition methods, the experimental results show that a linear dynamics-based controller still outperforms the AFP controller. Based on experimental observations, the least-squares fitting technique, effectiveness of the AFP compensation and differences between delay and ordinary differential equations are discussed herein, in order to reflect the fundamental issues of actuator modelling in relevant literature and, more specifically, to show that the actuator and numerical substructure are heterogeneous dynamic components and should not be collectively modelled as a homogeneous delay differential equation. PMID:25104902

  3. Design of a data-driven predictive controller for start-up process of AMT vehicles.

    PubMed

    Lu, Xiaohui; Chen, Hong; Wang, Ping; Gao, Bingzhao

    2011-12-01

    In this paper, a data-driven predictive controller is designed for the start-up process of vehicles with automated manual transmissions (AMTs). It is obtained directly from the input-output data of a driveline simulation model constructed by the commercial software AMESim. In order to obtain offset-free control for the reference input, the predictor equation is gained with incremental inputs and outputs. Because of the physical characteristics, the input and output constraints are considered explicitly in the problem formulation. The contradictory requirements of less friction losses and less driveline shock are included in the objective function. The designed controller is tested under nominal conditions and changed conditions. The simulation results show that, during the start-up process, the AMT clutch with the proposed controller works very well, and the process meets the control objectives: fast clutch lockup time, small friction losses, and the preservation of driver comfort, i.e., smooth acceleration of the vehicle. At the same time, the closed-loop system has the ability to reject uncertainties, such as the vehicle mass and road grade.

  4. Nonlinear predictive control for durability enhancement and efficiency improvement in a fuel cell power system

    NASA Astrophysics Data System (ADS)

    Luna, Julio; Jemei, Samir; Yousfi-Steiner, Nadia; Husar, Attila; Serra, Maria; Hissel, Daniel

    2016-10-01

    In this work, a nonlinear model predictive control (NMPC) strategy is proposed to improve the efficiency and enhance the durability of a proton exchange membrane fuel cell (PEMFC) power system. The PEMFC controller is based on a distributed parameters model that describes the nonlinear dynamics of the system, considering spatial variations along the gas channels. Parasitic power from different system auxiliaries is considered, including the main parasitic losses which are those of the compressor. A nonlinear observer is implemented, based on the discretised model of the PEMFC, to estimate the internal states. This information is included in the cost function of the controller to enhance the durability of the system by means of avoiding local starvation and inappropriate water vapour concentrations. Simulation results are presented to show the performance of the proposed controller over a given case study in an automotive application (New European Driving Cycle). With the aim of representing the most relevant phenomena that affects the PEMFC voltage, the simulation model includes a two-phase water model and the effects of liquid water on the catalyst active area. The control model is a simplified version that does not consider two-phase water dynamics.

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

    NASA Technical Reports Server (NTRS)

    Cabell, Randolph H.; Gibbs, Gary P.

    2000-01-01

    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

  6. Iterated non-linear model predictive control based on tubes and contractive constraints.

    PubMed

    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.

  7. Infrared thermography for process control and predictive maintenance purposes in a steel wire drawing machine

    NASA Astrophysics Data System (ADS)

    Rozlosnik, Andres E.; Lardone, Victor M.

    1999-03-01

    The industrial drawing process reduces the section of the wire rod by pulling the material through a hard die. This process generates heat by deformation and friction. The heat generated must be immediately removed after every reduction diameter step, otherwise the final quality of wire and the drawing performance will be poor. That is the reason why the improvement of the cooling efficiency in the drawing machine is of great importance. The better the cooling efficiency, the greater the wire quality and the productivity of the process will be. Nowadays, the infrared thermography control offer the possibility to analyze how the drawing aspect is affecting the cooling efficiency and how this technique allows the drawing process improvements. As you look at the capstans in the wire process control, you are doing at the same time, predictive maintenance in the machine.

  8. Iterated non-linear model predictive control based on tubes and contractive constraints.

    PubMed

    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. PMID:26850752

  9. Interior noise control prediction study for high-speed propeller-driven aircraft

    NASA Technical Reports Server (NTRS)

    Rennison, D. C.; Wilby, J. F.; Marsh, A. H.; Wilby, E. G.

    1979-01-01

    An analytical model was developed to predict the noise levels inside propeller-driven aircraft during cruise at M = 0.8. The model was applied to three study aircraft with fuselages of different size (wide body, narrow body and small diameter) in order to determine the noise reductions required to achieve the goal of an A-weighted sound level which does not exceed 80 dB. The model was then used to determine noise control methods which could achieve the required noise reductions. Two classes of noise control treatments were investigated: add-on treatments which can be added to existing structures, and advanced concepts which would require changes to the fuselage primary structure. Only one treatment, a double wall with limp panel, provided the required noise reductions. Weight penalties associated with the treatment were estimated for the three study aircraft.

  10. Simulation of complex glazing products; from optical data measurements to model based predictive controls

    SciTech Connect

    Kohler, Christian

    2012-08-01

    Complex glazing systems such as venetian blinds, fritted glass and woven shades require more detailed optical and thermal input data for their components than specular non light-redirecting glazing systems. Various methods for measuring these data sets are described in this paper. These data sets are used in multiple simulation tools to model the thermal and optical properties of complex glazing systems. The output from these tools can be used to generate simplified rating values or as an input to other simulation tools such as whole building annual energy programs, or lighting analysis tools. I also describe some of the challenges of creating a rating system for these products and which factors affect this rating. A potential future direction of simulation and building operations is model based predictive controls, where detailed computer models are run in real-time, receiving data for an actual building and providing control input to building elements such as shades.

  11. HIV-1 DNA predicts disease progression and post-treatment virological control

    PubMed Central

    Williams, James P; Hurst, Jacob; Stöhr, Wolfgang; Robinson, Nicola; Brown, Helen; Fisher, Martin; Kinloch, Sabine; Cooper, David; Schechter, Mauro; Tambussi, Giuseppe; Fidler, Sarah; Carrington, Mary; Babiker, Abdel; Weber, Jonathan

    2014-01-01

    In HIV-1 infection, a population of latently infected cells facilitates viral persistence despite antiretroviral therapy (ART). With the aim of identifying individuals in whom ART might induce a period of viraemic control on stopping therapy, we hypothesised that quantification of the pool of latently infected cells in primary HIV-1 infection (PHI) would predict clinical progression and viral replication following ART. We measured HIV-1 DNA in a highly characterised randomised population of individuals with PHI. We explored associations between HIV-1 DNA and immunological and virological markers of clinical progression, including viral rebound in those interrupting therapy. In multivariable analyses, HIV-1 DNA was more predictive of disease progression than plasma viral load and, at treatment interruption, predicted time to plasma virus rebound. HIV-1 DNA may help identify individuals who could safely interrupt ART in future HIV-1 eradication trials. Clinical trial registration: ISRCTN76742797 and EudraCT2004-000446-20 DOI: http://dx.doi.org/10.7554/eLife.03821.001 PMID:25217531

  12. Backflow length predictions during flow-controlled infusions using a nonlinear biphasic finite element model.

    PubMed

    Orozco, Gustavo A; Smith, Joshua H; García, José J

    2014-10-01

    A previously proposed finite element model that considers geometric and material nonlinearities and the free boundary problems that occur at the catheter tip and in the annular zone around the lateral surface of the catheter was revised and was used to fit a power-law formula to predict backflow length during infusions into brain tissue. Compared to a closed-form solution based on linear elasticity, the power-law formula for compliant materials predicted a substantial lower influence of the shear modulus and catheter radius on the backflow length, whereas the corresponding influence for stiffer materials was more consistent with the closed-form solution. The finite element model predicted decreases of the backflow length for reduction of the shear modulus for highly compliant materials (shear modulus less than 500 Pa) due to the increased area of infusion and the high fluid fraction near the infusion cavity that greatly increased the surface area available for fluid transfer and reduced the hydraulic resistance toward the tissue. These results show the importance of taking into account the material and geometrical nonlinearities that arise near the infusion surface as well as the change of hydraulic conductivity with strain for a proper characterization of backflow length during flow-controlled infusions into the brain. PMID:25154980

  13. Prediction of forces and moments for hypersonic flight vehicle control effectors

    NASA Technical Reports Server (NTRS)

    Maughmer, Mark D.; Long, Lyle N.; Guilmette, Neal; Pagano, Peter

    1993-01-01

    This research project includes three distinct phases. For completeness, all three phases of the work are briefly described in this report. The goal was to develop methods of predicting flight control forces and moments for hypersonic vehicles which could be used in a preliminary design environment. The first phase included a preliminary assessment of subsonic/supersonic panel methods and hypersonic local flow inclination methods for such predictions. While these findings clearly indicated the usefulness of such methods for conceptual design activities, deficiencies exist in some areas. Thus, a second phase of research was conducted in which a better understanding was sought for the reasons behind the successes and failures of the methods considered, particularly for the cases at hypersonic Mach numbers. This second phase involved using computational fluid dynamics methods to examine the flow fields in detail. Through these detailed predictions, the deficiencies in the simple surface inclination methods were determined. In the third phase of this work, an improvement to the surface inclination methods was developed. This used a novel method for including viscous effects by modifying the geometry to include the viscous/shock layer.

  14. Battery available power prediction of hybrid electric vehicle based on improved Dynamic Matrix Control algorithms

    NASA Astrophysics Data System (ADS)

    Wang, Limei; Cheng, Yong; Zou, Ju

    2014-09-01

    The core technology to any hybrid engine vehicle (HEV) is the design of energy management strategy (EMS). To develop a reasonable EMS, it is necessary to monitor the state of capacity, state of health and instantaneous available power of battery packs. A new method that linearizes RC equivalent circuit model and predicts battery available power according to original Dynamic Matrix Control algorithm is proposed. To verify the validity of the new algorithm, a bench test with lithium-ion battery cell and a HEV test with lithium-ion battery packs are carried out. The bench test results indicate that a single RC block equivalent circuit model could be used to describe the dynamic and the steady state characteristics of a battery under testing conditions. However, lacking of long time constant of RC modules, there is a sample deviation in the open-circuit voltage identified and that measured. The HEV testing results show that the battery voltage predicted is in good agreement with that measured, the maximum difference is within 3.7%. Fixing the time constant to a numeric value, satisfactory results can still be achieved. After setting a battery discharge cut-off voltage, the instantaneous available power of the battery can be predicted.

  15. Backflow length predictions during flow-controlled infusions using a nonlinear biphasic finite element model.

    PubMed

    Orozco, Gustavo A; Smith, Joshua H; García, José J

    2014-10-01

    A previously proposed finite element model that considers geometric and material nonlinearities and the free boundary problems that occur at the catheter tip and in the annular zone around the lateral surface of the catheter was revised and was used to fit a power-law formula to predict backflow length during infusions into brain tissue. Compared to a closed-form solution based on linear elasticity, the power-law formula for compliant materials predicted a substantial lower influence of the shear modulus and catheter radius on the backflow length, whereas the corresponding influence for stiffer materials was more consistent with the closed-form solution. The finite element model predicted decreases of the backflow length for reduction of the shear modulus for highly compliant materials (shear modulus less than 500 Pa) due to the increased area of infusion and the high fluid fraction near the infusion cavity that greatly increased the surface area available for fluid transfer and reduced the hydraulic resistance toward the tissue. These results show the importance of taking into account the material and geometrical nonlinearities that arise near the infusion surface as well as the change of hydraulic conductivity with strain for a proper characterization of backflow length during flow-controlled infusions into the brain.

  16. Conjunctively optimizing flash flood control and water quality in urban water reservoirs by model predictive control and dynamic emulation

    NASA Astrophysics Data System (ADS)

    Galelli, Stefano; Goedbloed, Albert; Schmitter, Petra; Castelletti, Andrea

    2014-05-01

    Urban water reservoirs are a viable adaptation option to account for increasing drinking water demand of urbanized areas as they allow storage and re-use of water that is normally lost. In addition, the direct availability of freshwater reduces pumping costs and diversifies the portfolios of drinking water supply. Yet, these benefits have an associated twofold cost. Firstly, the presence of large, impervious areas increases the hydraulic efficiency of urban catchments, with short time of concentration, increased runoff rates, losses of infiltration and baseflow, and higher risk of flash floods. Secondly, the high concentration of nutrients and sediments characterizing urban discharges is likely to cause water quality problems. In this study we propose a new control scheme combining Model Predictive Control (MPC), hydro-meteorological forecasts and dynamic model emulation to design real-time operating policies that conjunctively optimize water quantity and quality targets. The main advantage of this scheme stands in its capability of exploiting real-time hydro-meteorological forecasts, which are crucial in such fast-varying systems. In addition, the reduced computational requests of the MPC scheme allows coupling it with dynamic emulators of water quality processes. The approach is demonstrated on Marina Reservoir, a multi-purpose reservoir located in the heart of Singapore and characterized by a large, highly urbanized catchment with a short (i.e. approximately one hour) time of concentration. Results show that the MPC scheme, coupled with a water quality emulator, provides a good compromise between different operating objectives, namely flood risk reduction, drinking water supply and salinity control. Finally, the scheme is used to assess the effect of source control measures (e.g. green roofs) aimed at restoring the natural hydrological regime of Marina Reservoir catchment.

  17. Real-time implementation of model predictive control on Maricopa-Stanfield irrigation and drainage district's WM canal

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Water resources are limited in many agricultural areas. One method to improve the effective use of water is to improve delivery service from irrigation canals. This can be done by applying automatic control methods that control the gates in an irrigation canal. The model predictive control MPC is ...

  18. No site unseen: predicting the failure to control problematic Internet use among young adults.

    PubMed

    Yamada, Tetsuhiro; Moshier, Samantha J; Otto, Michael W

    2016-11-01

    Problematic Internet use has been associated with the neglect of valued activities such as work, exercise, social activities, and relationships. In the present study, we expanded the understanding of problematic Internet use by identifying an important predictor of the inability to curb Internet use despite the desire to do so. Specifically, in a college student sample reporting a mean of 27.8 h of recreational Internet use in the past week, we investigated the role of distress intolerance (DI)-an individual difference variable that refers to the inability of an individual to tolerate emotional discomfort and to engage in goal-directed behavior when distressed-to predict the failure to meet personal restrictions on Internet use. Consistent with hypotheses, DI emerged as a significant predictor of the failure to meet self-control goals in both bivariate and multivariate models, indicating that DI offers unique prediction of self-control failure with problematic Internet use. Given that DI is a modifiable trait, these results encourage consideration of DI-focused early intervention strategies. PMID:27426432

  19. Model Predictive Obstacle Avoidance and Wheel Allocation Control of Mobile Robots Using Embedded CPU

    NASA Astrophysics Data System (ADS)

    Takahashi, Naoki; Nonaka, Kenichiro

    In this study, we propose a real-time model predictive control method for leg/wheel mobile robots which simultaneously achieves both obstacle avoidance and wheel allocation at a flexible position. The proposed method generates both obstacle avoidance path and dynamical wheel positions, and controls the heading angle depending on the slope of the predicted path so that the robot can keep a good balance between stability and mobility in narrow and complex spaces like indoor environments. Moreover, we reduce the computational effort of the algorithm by deleting usage of mathematical function in the repetitive numerical computation. Thus the proposed real-time optimization method can be applied to low speed on-board CPUs used in commercially-produced vehicles. We conducted experiments to verify efficacy and feasibility of the real-time implementation of the proposed method. We used a leg/wheel mobile robot which is equipped with two laser range finders to detect obstacles and an embedded CPU whose clock speed is only 80MHz. Experiments indicate that the proposed method achieves improved obstacle avoidance comparing with the previous method in the sense that it generates an avoidance path with balanced allocation of right and left side wheels.

  20. Pragmatism, mathematical models, and the scientific ideal of prediction and control.

    PubMed

    Moore, J

    2015-05-01

    Mathematical models are often held to be valuable, if not necessary, for theories and explanations in the quantitative analysis of behavior. The present review suggests that mathematical models primarily derived from the observation of functional relations do indeed contribute to the scientific value of theories and explanations, even though the final form of the models appears to be highly abstract. However, mathematical models not primarily so derived risk being essentialist in character, based on a particular view of formal causation. Such models invite less effective and frequently mentalistic theories and explanations of behavior. Models may be evaluated in terms of both (a) the verbal processes responsible for their origin and development and (b) the prediction and control engendered by the theories and explanations that incorporate the models, however indirect or abstract that prediction and control may be. Overall, the present review suggests that technological application and theoretical contemplation may be usefully viewed as continuous and overlapping forms of scientific activity, rather than dichotomous and mutually exclusive.

  1. Pragmatism, mathematical models, and the scientific ideal of prediction and control.

    PubMed

    Moore, J

    2015-05-01

    Mathematical models are often held to be valuable, if not necessary, for theories and explanations in the quantitative analysis of behavior. The present review suggests that mathematical models primarily derived from the observation of functional relations do indeed contribute to the scientific value of theories and explanations, even though the final form of the models appears to be highly abstract. However, mathematical models not primarily so derived risk being essentialist in character, based on a particular view of formal causation. Such models invite less effective and frequently mentalistic theories and explanations of behavior. Models may be evaluated in terms of both (a) the verbal processes responsible for their origin and development and (b) the prediction and control engendered by the theories and explanations that incorporate the models, however indirect or abstract that prediction and control may be. Overall, the present review suggests that technological application and theoretical contemplation may be usefully viewed as continuous and overlapping forms of scientific activity, rather than dichotomous and mutually exclusive. PMID:25596451

  2. No site unseen: predicting the failure to control problematic Internet use among young adults.

    PubMed

    Yamada, Tetsuhiro; Moshier, Samantha J; Otto, Michael W

    2016-11-01

    Problematic Internet use has been associated with the neglect of valued activities such as work, exercise, social activities, and relationships. In the present study, we expanded the understanding of problematic Internet use by identifying an important predictor of the inability to curb Internet use despite the desire to do so. Specifically, in a college student sample reporting a mean of 27.8 h of recreational Internet use in the past week, we investigated the role of distress intolerance (DI)-an individual difference variable that refers to the inability of an individual to tolerate emotional discomfort and to engage in goal-directed behavior when distressed-to predict the failure to meet personal restrictions on Internet use. Consistent with hypotheses, DI emerged as a significant predictor of the failure to meet self-control goals in both bivariate and multivariate models, indicating that DI offers unique prediction of self-control failure with problematic Internet use. Given that DI is a modifiable trait, these results encourage consideration of DI-focused early intervention strategies.

  3. To predict sufentanil requirement for postoperative pain control using a real-time method

    PubMed Central

    Zhang, Yuhao; Duan, Guangyou; Guo, Shanna; Ying, Ying; Huang, Penghao; Zhang, Mi; Li, Ningbo; Zhang, Xianwei

    2016-01-01

    Abstract Preoperative identification of individual sensitivity to opioid analgesics could improve the quality of postoperative analgesia. We explored the feasibility and utility of a real-time assessment of sufentanil sensitivity in predicting postoperative analgesic requirement. Our primary study included 111 patients who underwent measurements of pressure and quantitative pricking pain thresholds before and 5 minutes after sufentanil infusion. Pain intensity was assessed during the first 24-hour postsurgery, and patients who reported inadequate levels of analgesia were excluded from the study. The sufentanil requirement for patient-controlled analgesia was recorded, and a subsequent exploratory study of 20 patients facilitated the interpretation of the primary study results. In the primary study, experimental pain thresholds increased (P < 0.001) 5 minutes after sufentanil infusion, and the percent change in pricking pain threshold was positively associated with sufentanil requirement at 12 and 24 hours after surgery (β = 0.318, P = 0.001; and β = 0.335, P = 0.001). A receiver-operating characteristic curve analysis showed that patients with a change in pricking pain threshold >188% were >50% likely to require more sufentanil for postoperative pain control. In the exploratory study, experimental pain thresholds significantly decreased after the operation (P < 0.001), and we observed a positive correlation (P < 0.001) between the percent change in pricking pain threshold before and after surgery. Preoperative detection of individual sensitivity to sufentanil via the above described real-time method was effective in predicting postoperative sufentanil requirement. Thus, percent change in pricking pain threshold might be a feasible predictive marker of postoperative analgesia requirement. PMID:27336880

  4. Model Predictive Control of HVAC Systems: Implementation and Testing at the University of California, Merced

    SciTech Connect

    Haves, Phillip; Hencey, Brandon; Borrell, Francesco; Elliot, John; Ma, Yudong; Coffey, Brian; Bengea, Sorin; Wetter, Michael

    2010-06-29

    A Model Predictive Control algorithm was developed for the UC Merced campus chilled water plant. Model predictive control (MPC) is an advanced control technology that has proven successful in the chemical process industry and other industries. The main goal of the research was to demonstrate the practical and commercial viability of MPC for optimization of building energy systems. The control algorithms were developed and implemented in MATLAB, allowing for rapid development, performance, and robustness assessment. The UC Merced chilled water plant includes three water-cooled chillers and a two million gallon chilled water storage tank. The tank is charged during the night to minimize on-peak electricity consumption and take advantage of the lower ambient wet bulb temperature. The control algorithms determined the optimal chilled water plant operation including chilled water supply (CHWS) temperature set-point, condenser water supply (CWS) temperature set-point and the charging start and stop times to minimize a cost function that includes energy consumption and peak electrical demand over a 3-day prediction horizon. A detailed model of the chilled water plant and simplified models of the buildings served by the plant were developed using the equation-based modeling language Modelica. Steady state models of the chillers, cooling towers and pumps were developed, based on manufacturers performance data, and calibrated using measured data collected and archived by the control system. A detailed dynamic model of the chilled water storage tank was also developed and calibrated. Simple, semi-empirical models were developed to predict the temperature and flow rate of the chilled water returning to the plant from the buildings. These models were then combined and simplified for use in a model predictive control algorithm that determines the optimal chiller start and stop times and set-points for the condenser water temperature and the chilled water supply temperature. The

  5. Exploratory Studies in Generalized Predictive Control for Active Gust Load Alleviation

    NASA Technical Reports Server (NTRS)

    Kvaternik, Raymond G.; Eure, Kenneth W.; Juang, Jer-Nan

    2006-01-01

    The results of numerical simulations aimed at assessing the efficacy of Generalized Predictive Control (GPC) for active gust load alleviation using trailing- and leading-edge control surfaces are presented. The equations underlying the method are presented and discussed, including system identification, calculation of control law matrices, and calculation of commands applied to the control effectors. Both embedded and explicit feedforward paths for inclusion of disturbance effects are addressed. Results from two types of simulations are shown. The first used a 3-DOF math model of a mass-spring-dashpot system subject to user-defined external disturbances. The second used open-loop data from a wind-tunnel test in which a wing model was excited by sinusoidal vertical gusts; closed-loop behavior was simulated in post-test calculations. Results obtained from these simulations have been decidedly positive. In particular, results of closed-loop simulations for the wing model showed reductions in root moments by factors as high as 1000, depending on whether the excitation is from a constant- or variable-frequency gust and on the direction of the response.

  6. 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.

  7. An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems.

    PubMed

    Nandola, Naresh N; Rivera, Daniel E

    2013-01-01

    We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty. PMID:24348004

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

    PubMed Central

    Coelho, Antonio Augusto Rodrigues

    2016-01-01

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

  9. Simulation of the Predictive Control Algorithm for Container Crane Operation using Matlab Fuzzy Logic Tool Box

    NASA Technical Reports Server (NTRS)

    Richardson, Albert O.

    1997-01-01

    This research has investigated the use of fuzzy logic, via the Matlab Fuzzy Logic Tool Box, to design optimized controller systems. The engineering system for which the controller was designed and simulate was the container crane. The fuzzy logic algorithm that was investigated was the 'predictive control' algorithm. The plant dynamics of the container crane is representative of many important systems including robotic arm movements. The container crane that was investigated had a trolley motor and hoist motor. Total distance to be traveled by the trolley was 15 meters. The obstruction height was 5 meters. Crane height was 17.8 meters. Trolley mass was 7500 kilograms. Load mass was 6450 kilograms. Maximum trolley and rope velocities were 1.25 meters per sec. and 0.3 meters per sec., respectively. The fuzzy logic approach allowed the inclusion, in the controller model, of performance indices that are more effectively defined in linguistic terms. These include 'safety' and 'cargo swaying'. Two fuzzy inference systems were implemented using the Matlab simulation package, namely the Mamdani system (which relates fuzzy input variables to fuzzy output variables), and the Sugeno system (which relates fuzzy input variables to crisp output variable). It is found that the Sugeno FIS is better suited to including aspects of those plant dynamics whose mathematical relationships can be determined.

  10. Ambulatory wireless sensor network power management using constrained explicit generalised predictive control

    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.

  11. Predictive ion source control using artificial neural network for RFT-30 cyclotron

    NASA Astrophysics Data System (ADS)

    Kong, Young Bae; Hur, Min Goo; Lee, Eun Je; Park, Jeong Hoon; Park, Yong Dae; Yang, Seung Dae

    2016-01-01

    An RFT-30 cyclotron is a 30 MeV proton accelerator for radioisotope production and fundamental research. The ion source of the RFT-30 cyclotron creates plasma from hydrogen gas and transports an ion beam into the center region of the cyclotron. Ion source control is used to search source parameters for best quality of the ion beam. Ion source control in a real system is a difficult and time consuming task, and the operator should search the source parameters by manipulating the cyclotron directly. In this paper, we propose an artificial neural network based predictive control approach for the RFT-30 ion source. The proposed approach constructs the ion source model by using an artificial neural network and finds the optimized parameters with the simulated annealing algorithm. To analyze the performance of the proposed approach, we evaluated the simulations with the experimental data of the ion source. The performance results show that the proposed approach can provide an efficient way to analyze and control the ion source of the RFT-30 cyclotron.

  12. Predicting the effects of unmodeled dynamics on an aircraft flight control system design using eigenspace assignment

    NASA Technical Reports Server (NTRS)

    Johnson, Eric N.; Davidson, John B.; Murphy, Patrick C.

    1994-01-01

    When using eigenspace assignment to design an aircraft flight control system, one must first develop a model of the plant. Certain questions arise when creating this model as to which dynamics of the plant need to be included in the model and which dynamics can be left out or approximated. The answers to these questions are important because a poor choice can lead to closed-loop dynamics that are unpredicted by the design model. To alleviate this problem, a method has been developed for predicting the effect of not including certain dynamics in the design model on the final closed-loop eigenspace. This development provides insight as to which characteristics of unmodeled dynamics will ultimately affect the closed-loop rigid-body dynamics. What results from this insight is a guide for eigenstructure control law designers to aid them in determining which dynamics need or do not need to be included and a new way to include these dynamics in the flight control system design model to achieve a required accuracy in the closed-loop rigid-body dynamics. The method is illustrated for a lateral-directional flight control system design using eigenspace assignment for the NASA High Alpha Research Vehicle (HARV).

  13. An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems

    PubMed Central

    Nandola, Naresh N.; Rivera, Daniel E.

    2013-01-01

    We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty. PMID:24348004

  14. A distributed model predictive control (MPC) fault reconfiguration strategy for formation flying satellites

    NASA Astrophysics Data System (ADS)

    Esfahani, N. R.; Khorasani, K.

    2016-05-01

    In this paper, an active distributed (also referred to as semi-decentralised) fault recovery control scheme is proposed that employs inaccurate and unreliable fault information into a model-predictive-control-based design. The objective is to compensate for the identified actuator faults that are subject to uncertainties and detection time delays, in the attitude control subsystems of formation flying satellites. The proposed distributed fault recovery scheme is developed through a two-level hierarchical framework. In the first level, or the agent level, the fault is recovered locally to maintain as much as possible the design specifications, feasibility, and tracking performance of all the agents. In the second level, or the formation level, the recovery is carried out by enhancing the entire team performance. The fault recovery performance of our proposed distributed (semi-decentralised) scheme is compared with two other alternative schemes, namely the centralised and the decentralised fault recovery schemes. It is shown that the distributed (semi-decentralised) fault recovery scheme satisfies the recovery design specifications and also imposes lower fault compensation control effort cost and communication bandwidth requirements as compared to the centralised scheme. Our proposed distributed (semi-decentralised) scheme also outperforms the achievable performance capabilities of the decentralised scheme. Simulation results corresponding to a network of four precision formation flight satellites are also provided to demonstrate and illustrate the advantages of our proposed distributed (semi-decentralised) fault recovery strategy.

  15. A practical approach to Model Predictive Control (MPC) for solar communities

    NASA Astrophysics Data System (ADS)

    Quintana, Humberto

    Solar district heating (SDH) systems are part of the solution to reduce energy consumption and GHG emissions required for space heating. This kind of installation takes advantage of the convenience of a centralized system and of solar energy to reduce dependency on fossil-fuels. An SDH system is a proven concept that can be enhanced with the addition of long-term thermal energy storage to compensate the seasonal disparity between solar energy supply and heating load demand. These systems are especially deployed in Europe. In Canada, the only SDH installation is the Drake Landing Solar Community (DLSC). This project, which includes seasonal storage (Borehole Thermal Energy Storage-BTES), has been a remarkable success, reaching a solar fraction of 97% by the fifth year of operation. An SDH system cannot be complete without an appropriate supervisory control that coordinates the operation and interaction of system components. The control is based on a set of rules that must consider the system's internal status and external conditions to guarantee occupant comfort with minimal fossil-fuels consumption. This research project is mainly focused on conceiving and assessing new control mechanisms aiming towards an increase of SDH systems' overall energy efficiency. The case study is the DLSC plant, and the proposed control strategies are based on the practical application of Model Predictive Control (MPC) theory. A calibrated model of DLSC including the supervisory control strategies was developed in TRNSYS, building upon the model used for design studies. The model was improved and new components were created when needed. The calibration process delivered a very good agreement for the most important yearly energy performance indices (2 % for solar heat input to the district and for gas consumption, and 5 % for electricity use). Proposed control strategies were conceived for modifying four aspects of the current control: the parameters that define the interaction between

  16. Integrating Predictive Modeling with Control System Design for Managed Aquifer Recharge and Recovery Applications

    NASA Astrophysics Data System (ADS)

    Drumheller, Z. W.; Regnery, J.; Lee, J. H.; Illangasekare, T. H.; Kitanidis, P. K.; Smits, K. M.

    2014-12-01

    Aquifers around the world show troubling signs of irreversible depletion and seawater intrusion as climate change, population growth, and urbanization led to reduced natural recharge rates and overuse. Scientists and engineers have begun to re-investigate the technology of managed aquifer recharge and recovery (MAR) as a means to increase the reliability of the diminishing and increasingly variable groundwater supply. MAR systems offer the possibility of naturally increasing groundwater storage while improving the quality of impaired water used for recharge. Unfortunately, MAR systems remain wrought with operational challenges related to the quality and quantity of recharged and recovered water stemming from a lack of data-driven, real-time control. Our project seeks to ease the operational challenges of MAR facilities through the implementation of active sensor networks, adaptively calibrated flow and transport models, and simulation-based meta-heuristic control optimization methods. The developed system works by continually collecting hydraulic and water quality data from a sensor network embedded within the aquifer. The data is fed into an inversion algorithm, which calibrates the parameters and initial conditions of a predictive flow and transport model. The calibrated model is passed to a meta-heuristic control optimization algorithm (e.g. genetic algorithm) to execute the simulations and determine the best course of action, i.e., the optimal pumping policy for current aquifer conditions. The optimal pumping policy is manually or autonomously applied. During operation, sensor data are used to assess the accuracy of the optimal prediction and augment the pumping strategy as needed. At laboratory-scale, a small (18"H x 46"L) and an intermediate (6'H x 16'L) two-dimensional synthetic aquifer were constructed and outfitted with sensor networks. Data collection and model inversion components were developed and sensor data were validated by analytical measurements.

  17. The predicted and observed decline in onchocerciasis infection during 14 years of successful control of Simulium spp. in west Africa.

    PubMed Central

    Remme, J.; De Sole, G.; van Oortmarssen, G. J.

    1990-01-01

    In 55 villages from the well-protected central area of the Onchocerciasis Control Programme in West Africa (OCP), skin snip surveys have been carried out at regular intervals since the programme started, and the latest round of surveys was undertaken after 12-14 years of successful vector control. The observed trends in the prevalence and intensity of onchocerciasis infection in cohorts of adults were compared with the trends predicted using a host-parasite model. After 12-14 years of control the community microfilarial load (CMFL) was close to zero in all villages. During the last few years of control, the prevalence of infection declined at an accelerated rate, and this was predicted by the model. There was generally good agreement between observed and predicted trends. The predictions were based on an estimated average duration of infection of 10.4 years, which corresponds to a mean reproductive lifespan for Onchocerca volvulus of 9-9.5 years, and an upper limit of 15 years for 95% of the infections. Differences between the observed and predicted data included the trend in CMFL between the first and second surveys, which in 18 villages did not show the predicted decline. Furthermore, the observed final decline in prevalence was faster than predicted in the north-eastern part of the central OCP area. After 14 years of vector control, the level of onchocerciasis has fallen to such a low level that consideration is being given to ending larviciding. PMID:2393979

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

    NASA Astrophysics Data System (ADS)

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

    2010-08-01

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

  19. Predictive Sea State Estimation for Automated Ride Control and Handling - PSSEARCH

    NASA Technical Reports Server (NTRS)

    Huntsberger, Terrance L.; Howard, Andrew B.; Aghazarian, Hrand; Rankin, Arturo L.

    2012-01-01

    PSSEARCH provides predictive sea state estimation, coupled with closed-loop feedback control for automated ride control. It enables a manned or unmanned watercraft to determine the 3D map and sea state conditions in its vicinity in real time. Adaptive path-planning/ replanning software and a control surface management system will then use this information to choose the best settings and heading relative to the seas for the watercraft. PSSEARCH looks ahead and anticipates potential impact of waves on the boat and is used in a tight control loop to adjust trim tabs, course, and throttle settings. The software uses sensory inputs including IMU (Inertial Measurement Unit), stereo, radar, etc. to determine the sea state and wave conditions (wave height, frequency, wave direction) in the vicinity of a rapidly moving boat. This information can then be used to plot a safe path through the oncoming waves. The main issues in determining a safe path for sea surface navigation are: (1) deriving a 3D map of the surrounding environment, (2) extracting hazards and sea state surface state from the imaging sensors/map, and (3) planning a path and control surface settings that avoid the hazards, accomplish the mission navigation goals, and mitigate crew injuries from excessive heave, pitch, and roll accelerations while taking into account the dynamics of the sea surface state. The first part is solved using a wide baseline stereo system, where 3D structure is determined from two calibrated pairs of visual imagers. Once the 3D map is derived, anything above the sea surface is classified as a potential hazard and a surface analysis gives a static snapshot of the waves. Dynamics of the wave features are obtained from a frequency analysis of motion vectors derived from the orientation of the waves during a sequence of inputs. Fusion of the dynamic wave patterns with the 3D maps and the IMU outputs is used for efficient safe path planning.

  20. Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training

    PubMed Central

    Witte, Matthias; Kober, Silvia Erika; Ninaus, Manuel; Neuper, Christa; Wood, Guilherme

    2013-01-01

    Technological progress in computer science and neuroimaging has resulted in many approaches that aim to detect brain states and translate them to an external output. Studies from the field of brain-computer interfaces (BCI) and neurofeedback (NF) have validated the coupling between brain signals and computer devices; however a cognitive model of the processes involved remains elusive. Psychological parameters usually play a moderate role in predicting the performance of BCI and NF users. The concept of a locus of control, i.e., whether one’s own action is determined by internal or external causes, may help to unravel inter-individual performance capacities. Here, we present data from 20 healthy participants who performed a feedback task based on EEG recordings of the sensorimotor rhythm (SMR). One group of 10 participants underwent 10 training sessions where the amplitude of the SMR was coupled to a vertical feedback bar. The other group of ten participants participated in the same task but relied on sham feedback. Our analysis revealed that a locus of control score focusing on control beliefs with regard to technology negatively correlated with the power of SMR. These preliminary results suggest that participants whose confidence in control over technical devices is high might consume additional cognitive resources. This higher effort in turn may interfere with brain states of relaxation as reflected in the SMR. As a consequence, one way to improve control over brain signals in NF paradigms may be to explicitly instruct users not to force mastery but instead to aim at a state of effortless relaxation. PMID:23966933

  1. Predictive factors of death in patients with tuberculosis: a nested case-control study.

    PubMed

    Moosazadeh, M; Nezammahalleh, A; Movahednia, M; Movahednia, N; Khanjani, N; Afshari, M

    2015-06-09

    Tuberculosis is one of the main causes of death worldwide. This study aimed to determine predictive factors for death in patients with tuberculosis to set priorities for public heath interventions to reduce mortality in these patients. This nested case-control study was carried out in Mazandaran province of Islamic Republic of Iran among tuberculosis patients who were treated during 2002-2009. Each deceased patient was individually matched with a control patient according to sex, age, area of involvement and time of follow-up. Potential risk factors for death were evaluated using multivariate conditional logistic regression models. From 2206 patients 376 cases and 376 matched controls were selected. Only positive serology for HIV (OR = 19.1), history of kidney disease (OR = 6.81) and use of immunosuppressant drugs (OR = 3.96) significantly increased the risk of death in tuberculosis patients. These potentially modifiable risk factors could be taken into account in preventive interventions for tuberculosis patients in our country.

  2. Grasp and release with surface functional electrical stimulation using a Model Predictive Control approach.

    PubMed

    Westerveld, Ard J; Kuck, Alexander; Schouten, Alfred C; Veltink, Peter H; van der Kooij, Herman

    2012-01-01

    Stroke often has a disabling effect on the ability to use the hand in a functional manner. Accurate finger and thumb positioning is necessary for many activities of daily living. In the current study, the feasibility of novel FES based approaches for positioning the thumb and fingers for grasp and release of differently sized objects is evaluated. Assistance based on these approaches may be used in rehabilitation of grasp and release after stroke. A model predictive controller (MPC) is compared with a proportional (P) feedback controller. Both methods are compared on their performance in tracking reference trajectories and in the capability of grasping, holding and releasing objects. Both methods are able to selectively activate the fingers such that differently sized objects, selected from the Action Research Arm test, can be grasped. The MPC method is easier to use in practice, as this method is based on a single identification of a model of the biological system. The P-controller has more parameters which need to be set correctly, and therefore needs more time to initialise. The current results are very promising. Evaluation in patients will be done to explore the possibilities to apply these methods in rehabilitation of grasp and release after stroke.

  3. Cognitive task load in a naval ship control centre: from identification to prediction.

    PubMed

    Grootjen, M; Neerincx, M A; Veltman, J A

    Deployment of information and communication technology will lead to further automation of control centre tasks and an increasing amount of information to be processed. A method for establishing adequate levels of cognitive task load for the operators in such complex environments has been developed. It is based on a model distinguishing three load factors: time occupied, task-set switching, and level of information processing. Application of the method resulted in eight scenarios for eight extremes of task load (i.e. low and high values for each load factor). These scenarios were performed by 13 teams in a high-fidelity control centre simulator of the Royal Netherlands Navy. The results show that the method provides good prediction of the task load that will actually appear in the simulator. The model allowed identification of under- and overload situations showing negative effects on operator performance corresponding to controlled experiments in a less realistic task environment. Tools proposed to keep the operator at an optimum task load are (adaptive) task allocation and interface support. PMID:17008255

  4. An Optimal Current Observer for Predictive Current Controlled Buck DC-DC Converters

    PubMed Central

    Min, Run; Chen, Chen; Zhang, Xiaodong; Zou, Xuecheng; Tong, Qiaoling; Zhang, Qiao

    2014-01-01

    In digital current mode controlled DC-DC converters, conventional current sensors might not provide isolation at a minimized price, power loss and size. Therefore, a current observer which can be realized based on the digital circuit itself, is a possible substitute. However, the observed current may diverge due to the parasitic resistors and the forward conduction voltage of the diode. Moreover, the divergence of the observed current will cause steady state errors in the output voltage. In this paper, an optimal current observer is proposed. It achieves the highest observation accuracy by compensating for all the known parasitic parameters. By employing the optimal current observer-based predictive current controller, a buck converter is implemented. The converter has a convergently and accurately observed inductor current, and shows preferable transient response than the conventional voltage mode controlled converter. Besides, costs, power loss and size are minimized since the strategy requires no additional hardware for current sensing. The effectiveness of the proposed optimal current observer is demonstrated experimentally. PMID:24854061

  5. An optimal current observer for predictive current controlled buck DC-DC converters.

    PubMed

    Min, Run; Chen, Chen; Zhang, Xiaodong; Zou, Xuecheng; Tong, Qiaoling; Zhang, Qiao

    2014-01-01

    In digital current mode controlled DC-DC converters, conventional current sensors might not provide isolation at a minimized price, power loss and size. Therefore, a current observer which can be realized based on the digital circuit itself, is a possible substitute. However, the observed current may diverge due to the parasitic resistors and the forward conduction voltage of the diode. Moreover, the divergence of the observed current will cause steady state errors in the output voltage. In this paper, an optimal current observer is proposed. It achieves the highest observation accuracy by compensating for all the known parasitic parameters. By employing the optimal current observer-based predictive current controller, a buck converter is implemented. The converter has a convergently and accurately observed inductor current, and shows preferable transient response than the conventional voltage mode controlled converter. Besides, costs, power loss and size are minimized since the strategy requires no additional hardware for current sensing. The effectiveness of the proposed optimal current observer is demonstrated experimentally.

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

    PubMed Central

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

    2013-01-01

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

  7. Real-time optical path control method that utilizes multiple support vector machines for traffic prediction

    NASA Astrophysics Data System (ADS)

    Kawase, Hiroshi; Mori, Yojiro; Hasegawa, Hiroshi; Sato, Ken-ichi

    2016-02-01

    An effective solution to the continuous Internet traffic expansion is to offload traffic to lower layers such as the L2 or L1 optical layers. One possible approach is to introduce dynamic optical path operations such as adaptive establishment/tear down according to traffic variation. Path operations cannot be done instantaneously; hence, traffic prediction is essential. Conventional prediction techniques need optimal parameter values to be determined in advance by averaging long-term variations from the past. However, this does not allow adaptation to the ever-changing short-term variations expected to be common in future networks. In this paper, we propose a real-time optical path control method based on a machinelearning technique involving support vector machines (SVMs). A SVM learns the most recent traffic characteristics, and so enables better adaptation to temporal traffic variations than conventional techniques. The difficulty lies in determining how to minimize the time gap between optical path operation and buffer management at the originating points of those paths. The gap makes the required learning data set enormous and the learning process costly. To resolve the problem, we propose the adoption of multiple SVMs running in parallel, trained with non-overlapping subsets of the original data set. The maximum value of the outputs of these SVMs will be the estimated number of necessary paths. Numerical experiments prove that our proposed method outperforms a conventional prediction method, the autoregressive moving average method with optimal parameter values determined by Akaike's information criterion, and reduces the packet-loss ratio by up to 98%.

  8. A tide prediction and tide height control system for laboratory mesocosms.

    PubMed

    Miller, Luke P; Long, Jeremy D

    2015-01-01

    Experimental mesocosm studies of rocky shore and estuarine intertidal systems may benefit from the application of natural tide cycles to better replicate variation in immersion time, water depth, and attendant fluctuations in abiotic and edaphic conditions. Here we describe a stand-alone microcontroller tide prediction open-source software program, coupled with a mechanical tidal elevation control system, which allows continuous adjustment of aquarium water depths in synchrony with local tide cycles. We used this system to monitor the growth of Spartina foliosa marsh cordgrass and scale insect herbivores at three simulated shore elevations in laboratory mesocosms. Plant growth decreased with increasing shore elevation, while scale insect population growth on the plants was not strongly affected by immersion time. This system shows promise for a range of laboratory mesocosm studies where natural tide cycling could impact organism performance or behavior, while the tide prediction system could additionally be utilized in field experiments where treatments need to be applied at certain stages of the tide cycle.

  9. A tide prediction and tide height control system for laboratory mesocosms

    PubMed Central

    Long, Jeremy D.

    2015-01-01

    Experimental mesocosm studies of rocky shore and estuarine intertidal systems may benefit from the application of natural tide cycles to better replicate variation in immersion time, water depth, and attendant fluctuations in abiotic and edaphic conditions. Here we describe a stand-alone microcontroller tide prediction open-source software program, coupled with a mechanical tidal elevation control system, which allows continuous adjustment of aquarium water depths in synchrony with local tide cycles. We used this system to monitor the growth of Spartina foliosa marsh cordgrass and scale insect herbivores at three simulated shore elevations in laboratory mesocosms. Plant growth decreased with increasing shore elevation, while scale insect population growth on the plants was not strongly affected by immersion time. This system shows promise for a range of laboratory mesocosm studies where natural tide cycling could impact organism performance or behavior, while the tide prediction system could additionally be utilized in field experiments where treatments need to be applied at certain stages of the tide cycle. PMID:26623195

  10. Predictions for the formation of atomic chains in mechanically controllable break-junction experiments

    NASA Astrophysics Data System (ADS)

    Fernández-Seivane, Lucas; García-Suárez, Víctor M.; Ferrer, Jaime

    2007-02-01

    We analyze the stability and magnetic properties of infinite zigzag atomic chains of a large number of late third-, fourth-, and fifth-row transition-metal atoms, as well as of the group-IV elements Si, Ge, Sn, and Pb. We find that zigzag chains of third- and fourth-row elements are not stable, while those made of Si, Ge, Sn, Pb, W, Os, Ir, Pt, and Au are. These results correlate well with known data in mechanically controllable break-junction experiments (MCBJEs). We therefore conjecture that the stability of an infinite chain is at least a necessary condition for the formation of a finite-sized one in MCBJEs. We therefore predict that Sn and Os, and possibly W and Pb chains, may be found in those experiments. We also find that the bonds in Hg chains are extremely soft. We finally show that the magnetic moments and anisotropies of Ir and Pt chains show a nontrivial behavior.

  11. Preliminary evidence of salivary cortisol predicting performance in a controlled setting.

    PubMed

    Lautenbach, Franziska; Laborde, Sylvain; Achtzehn, Silvia; Raab, Markus

    2014-04-01

    The aims of this study were to examine the influence of salivary cortisol on tennis serve performance in a controlled setting and to investigate if cortisol predicts unique variance in performance beyond a subjective anxiety measure (i.e., Competitive State Anxiety Inventory-2 [CSAI-2]). Twenty-three tennis players performed two series of second tennis serves separated by an anxiety induction (i.e., arithmetic task). Cortisol was assessed six times during the experiment. Results show that cortisol response and a drop in serving performance are positively correlated (r=.68, p<.001). Cortisol also explains unique variance in performance (i.e., 19%) beyond CSAI-2 measures. Thus, considering cortisol measurements seems warranted in future research aimed at understanding performance.

  12. Parenting and Child DRD4 Genotype Interact to Predict Children’s Early Emerging Effortful Control

    PubMed Central

    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. The current study examined whether the DRD4 exon III variable number tandem repeat polymorphism moderated the relationship between parenting and children’s EC. A total of 382 three-year-olds and primary caregivers completed behavioural tasks assessing children’s EC and parenting. Children’s DRD4 genotypes moderated the relationship between parenting and EC: children with at least one 7-repeat allele displayed lower EC in the context of negative parenting than children without this allele. These findings suggest opportunities for modifying early risk for low EC. PMID:22862680

  13. Distributed model predictive control with hierarchical architecture for communication: application in automated irrigation channels

    NASA Astrophysics Data System (ADS)

    Farhadi, Alireza; Khodabandehlou, Ali

    2016-08-01

    This paper is concerned with a distributed model predictive control (DMPC) method that is based on a distributed optimisation method with two-level architecture for communication. Feasibility (constraints satisfaction by the approximated solution), convergence and optimality of this distributed optimisation method are mathematically proved. For an automated irrigation channel, the satisfactory performance of the proposed DMPC method in attenuation of the undesired upstream transient error propagation and amplification phenomenon is illustrated and compared with the performance of another DMPC method that exploits a single-level architecture for communication. It is illustrated that the DMPC that exploits a two-level architecture for communication has a better performance by better managing communication overhead.

  14. Optimization of microalgal photobioreactor system using model predictive control with experimental validation.

    PubMed

    Yoo, Sung Jin; Jeong, Dong Hwi; Kim, Jung Hun; Lee, Jong Min

    2016-08-01

    To maximize biomass and lipid concentrations, various optimization methods were investigated in microalgal photobioreactor systems under mixotrophic conditions. Lipid concentration was estimated using unscented Kalman filter (UKF) with other measurable sources and subsequently used as lipid data for performing model predictive control (MPC). In addition, the maximized biomass and lipid trajectory obtained by open-loop optimization were used as target trajectory for tracking by MPC. Simulation studies and experimental validation were performed and significant improvements in biomass and lipid productivity were achieved in the case where MPC was applied. However, occurence of a lag phase was observed while manipulating the feed flow rates, which is induced by large amount of inputs. This is an important phenomenon that can lead to model-plant mismatch and requires further study for the optimization of microalgal photobioreactors. PMID:27094678

  15. Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory

    SciTech Connect

    Gregor P. Henze; Moncef Krarti

    2005-09-30

    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 investigated the merits of harnessing both storage media concurrently in the context of predictive optimal control. To pursue the analysis, modeling, and simulation research of Phase 1, two separate simulation environments were developed. Based on the new dynamic building simulation program EnergyPlus, a utility rate module, two thermal energy storage models were added. Also, a sequential optimization approach to the cost minimization problem using direct search, gradient-based, and dynamic programming methods was incorporated. The objective function was the total utility bill including the cost of reheat and a time-of-use electricity rate either with or without demand charges. An alternative simulation environment based on TRNSYS and Matlab was developed to allow for comparison and cross-validation with EnergyPlus. The initial evaluation of the theoretical potential of the combined optimal control assumed perfect weather prediction and match between the building model and the actual building counterpart. The analysis showed that the combined utilization leads to cost savings that is significantly greater than either storage but less than the sum of the individual savings. The findings reveal that the cooling-related on-peak electrical demand of commercial buildings can be considerably reduced. A subsequent analysis of the impact of forecasting uncertainty in the required short-term weather forecasts determined that it takes only very simple

  16. Implementation of reactive and predictive real-time control strategies to optimize dry stormwater detention ponds

    NASA Astrophysics Data System (ADS)

    Gaborit, Étienne; Anctil, François; Vanrolleghem, Peter A.; Pelletier, Geneviève

    2013-04-01

    Dry detention ponds have been widely implemented in U.S.A (National Research Council, 1993) and Canada (Shammaa et al. 2002) to mitigate the impacts of urban runoff on receiving water bodies. The aim of such structures is to allow a temporary retention of the water during rainfall events, decreasing runoff velocities and volumes (by infiltration in the pond) as well as providing some water quality improvement from sedimentation. The management of dry detention ponds currently relies on static control through a fixed pre-designed limitation of their maximum outflow (Middleton and Barrett 2008), for example via a proper choice of their outlet pipe diameter. Because these ponds are designed for large storms, typically 1- or 2-hour duration rainfall events with return periods comprised between 5 and 100 years, one of their main drawbacks is that they generally offer almost no retention for smaller rainfall events (Middleton and Barrett 2008), which are by definition much more common. Real-Time Control (RTC) has a high potential for optimizing retention time (Marsalek 2005) because it allows adopting operating strategies that are flexible and hence more suitable to the prevailing fluctuating conditions than static control. For dry ponds, this would basically imply adapting the outlet opening percentage to maximize water retention time, while being able to open it completely for severe storms. This study developed several enhanced RTC scenarios of a dry detention pond located at the outlet of a small urban catchment near Québec City, Canada, following the previous work of Muschalla et al. (2009). The catchment's runoff quantity and TSS concentration were simulated by a SWMM5 model with an improved wash-off formulation. The control procedures rely on rainfall detection and measures of the pond's water height for the reactive schemes, and on rainfall forecasts in addition to these variables for the predictive schemes. The automatic reactive control schemes implemented

  17. Predictive control of water distribution in the Dutch National Hydrological Instrument (NHI)

    NASA Astrophysics Data System (ADS)

    Talsma, J.; Patzke, S.; Becker, B. P. J.; Schwanenberg, D.; Jansen, M.

    2012-04-01

    In the Netherlands, water is extracted from rivers, lakes and canals for drinking water supply as well as industrial, agricultural and environmental water demands. These water extractions must be managed in such a way that constraints such as water quality, safety and minimum water levels for navigation are maintained as long as possible. The National Hydrological Instrument (NHI) has been developed for modeling the water distribution in the Netherlands and supporting the development of water management strategies. It is also integrated into the national Dutch forecasting system for predicting dry periods and their impacts on water supply, agriculture, aquatic ecosystems and navigation. With such setup, the NHI will be a fundamental tool for drought forecast in the Netherlands. The NHI consists of a groundwater model (MODFLOW), an unsaturated zone model (Metaswap) and surface water models which interact with each other in every time step via an OpenMI interface. The surface water models consist of a hydrological model MOZART for representing the regional catchments and computing a desired water demand, a SOBEK open channel flow model for flow routing in the network of the larger rivers, lakes and canals, and a real-time control component (RTC-Tools). The latter links the water demand generated by MOZART to the availably supply in the network for generating optimum water allocation policies within the prediction horizon of 10 days of the operational forecasting system. The approach relies on predictive control consisting of a simplified internal model of the network within a system-wide optimization algorithm. In a period of water shortages, the user can refine the water allocation by defining specific objectives and related priorities. Finally, the optimum water extractions from RTC-Tools are passed back to MOZART and SOBEK as allocated values. The RTC-Tools integration into the NHI is an ongoing activity. We present the new functionality based on a pilot system

  18. Remotely Controlled Mandibular Protrusion during Sleep Predicts Therapeutic Success with Oral Appliances in Patients with Obstructive Sleep Apnea

    PubMed Central

    Remmers, John; Charkhandeh, Shouresh; Grosse, Joshua; Topor, Zbigniew; Brant, Rollin; Santosham, Peter; Bruehlmann, Sabina

    2013-01-01

    Study Objectives: The present study addresses the need for a validated tool that prospectively identifies favorable candidates for oral appliance therapy in treatment of obstructive sleep apnea. The objective of the study was to evaluate the ability of a mandibular titration study, performed with a remotely controlled mandibular positioner (RCMP), to predict treatment outcome with a mandibular repositioning appliance (MRA) and to predict an effective target protrusive position (ETPP). Design: A prospective, blinded, outcome study. Setting: Standard clinical care with tests performed in the polysomnographic laboratory. Participants: Consecutive patients (n = 67) recruited from a sleep center or a dental practice using broad inclusion criteria (age 21-80 years; AHI > 10/h; BMI < 40 kg/m2). Interventions: Therapeutic outcome with a mandibular protruding oral appliance was predicted following a mandibular protrusive titration study in the polysomnographic laboratory using a remotely controlled positioner and prospectively established predictive rules. An ETPP was also prospectively determined for participants predicted to be therapeutically successful with MRA therapy. All participants were blindly treated with a MRA, at either the predicted ETPP or a sham position, and therapeutic outcome was compared against prediction. Measurements and Results: At the final protrusive position, standard predictive parameters (sensitivity, specificity, positive and negative predictive values) showed statistically significant predictive accuracy (P < 0.05) in the range of 83% to 94%. The predicted ETPP provided an efficacious protrusive position in 87% of participants predicted to be therapeutically successful with MRA therapy (P < 0.05). Conclusions: Using prospectively established rules for interpreting the polysomnographic data, the mandibular titration study predicted mandibular repositioning appliance therapeutic outcome with significant accuracy, particularly with regard to

  19. Inhibitory Control in Preschool Predicts Early Math Skills in First Grade: Evidence from an Ethnically Diverse Sample

    ERIC Educational Resources Information Center

    Ng, Florrie Fei-Yin; Tamis-LeMonda, Catherine; Yoshikawa, Hirokazu; Sze, Irene Nga-Lam

    2015-01-01

    Preschoolers' inhibitory control and early math skills were concurrently and longitudinally examined in 255 Chinese, African American, Dominican, and Mexican 4-year-olds in the United States. Inhibitory control at age 4, assessed with a peg-tapping task, was associated with early math skills at age 4 and predicted growth in such skills from…

  20. Internal disinhibition predicts 5‐year weight regain in the National Weight Control Registry (NWCR)

    PubMed Central

    Thomas, J. G.; Niemeier, H.; Wing, R. R.

    2016-01-01

    Summary Background Maintenance of weight loss remains elusive for most individuals. One potential innovative target is internal disinhibition (ID) or the tendency to eat in response to negative thoughts, feelings or physical sensations. Individuals high on ID do worse on average in standard behavioural treatment programmes, and recent studies suggest that disinhibition could play a significant role in weight regain. Purpose The purpose of the current study was to examine whether ID was associated with weight change over 5 years of follow‐up in the National Weight Control Registry, a registry of individuals who have successfully lost weight and maintained it. Methods From the National Weight Control Registry, 5,320 participants were examined across 5 years. Weight data were gathered annually. The disinhibition subscale of the Eating Inventory was used to calculate internal disinhibition and External Disinhibition (ED) and was collected at baseline, year 1, year 3 and year 5. Linear mixed models were used to estimate the weight loss maintained across follow‐up years 1 to 5 using ID and ED as baseline and prospective predictors. Results Internal disinhibition predicted weight regain in all analyses. ED interacted with ID, such that individuals who were high on ID showed greater weight regain if they were also higher on ED. Conclusions The ID scale could be a useful screening measure for risk of weight regain, given its brevity. Improved psychological coping could be a useful target for maintenance or booster interventions.

  1. Application of explicit model predictive control to a hybrid battery-ultracapacitor power source

    NASA Astrophysics Data System (ADS)

    Hredzak, Branislav; Agelidis, Vassilios G.; Demetriades, Georgios

    2015-03-01

    An explicit model predictive control (EMPC) system for a hybrid battery-ultracapacitor power source is proposed and experimentally verified in this paper. The main advantage of using the EMPC system is that the control law computation is reduced to evaluation of an explicitly defined piecewise linear function of the states. Separate EMPC systems for the total output current loop, the battery loop and the ultracapacitor loop are designed. This modular design approach allows evaluation of the performance of each individual EMPC system separately and also improves the convergence of the EMPC system design algorithm as the models used to design each loop are smaller. In order to protect the hybrid power source, the designed EMPC systems maintain operation of the hybrid power source within specified constraints, namely, battery and ultracapacitor current constraints, battery state of charge constraints and ultracapacitor voltage constraints. At the same time, the total output current EMPC system allocates high frequency current changes to the ultracapacitor and the low frequency current changes to the battery thus extending the battery lifetime. Presented experimental results verify that the hybrid power source operates within the specified constraints while allocating high and low frequency current changes to the ultracapacitor and battery respectively.

  2. Control and prediction for blackouts caused by frequency collapse in smart grids

    NASA Astrophysics Data System (ADS)

    Wang, Chengwei; Grebogi, Celso; Baptista, Murilo S.

    2016-09-01

    The electric power system is one of the cornerstones of modern society. One of its most serious malfunctions is the blackout, a catastrophic event that may disrupt a substantial portion of the system, playing havoc to human life and causing great economic losses. Thus, understanding the mechanisms leading to blackouts and creating a reliable and resilient power grid has been a major issue, attracting the attention of scientists, engineers, and stakeholders. In this paper, we study the blackout problem in power grids by considering a practical phase-oscillator model. This model allows one to simultaneously consider different types of power sources (e.g., traditional AC power plants and renewable power sources connected by DC/AC inverters) and different types of loads (e.g., consumers connected to distribution networks and consumers directly connected to power plants). We propose two new control strategies based on our model, one for traditional power grids and another one for smart grids. The control strategies show the efficient function of the fast-response energy storage systems in preventing and predicting blackouts in smart grids. This work provides innovative ideas which help us to build up a robuster and more economic smart power system.

  3. A Risk-based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

    PubMed Central

    Zafra-Cabeza, Ascensión; Rivera, Daniel E.; Collins, Linda M.; Ridao, Miguel A.; Camacho, Eduardo F.

    2010-01-01

    This paper examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based Model Predictive Control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this paper can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm. PMID:21643450

  4. A Novel Model Predictive Control Formulation for Hybrid Systems With Application to Adaptive Behavioral Interventions

    PubMed Central

    Nandola, Naresh N.; Rivera, Daniel E.

    2010-01-01

    This paper presents a novel model predictive control (MPC) formulation for linear hybrid systems. The algorithm relies on a multiple-degree-of-freedom formulation that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on move suppression weights as traditionally used in MPC schemes. The formulation is motivated by the need to achieve robust performance in using the algorithm in emerging applications, for instance, as a decision policy for adaptive, time-varying interventions used in behavioral health. The proposed algorithm is demonstrated on a hypothetical adaptive intervention problem inspired by the Fast Track program, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results in the presence of simultaneous disturbances and significant plant-model mismatch are presented. These demonstrate that a hybrid MPC-based approach for this class of interventions can be tuned for desired performance under demanding conditions that resemble participant variability that is experienced in practice when applying an adaptive intervention to a population. PMID:20830213

  5. Thermal control system of the Exoplanet Characterisation Observatory Payload: design and predictions

    NASA Astrophysics Data System (ADS)

    Morgante, G.; Terenzi, L.; Eccleston, P.; Bradshaw, T.; Crook, M.; Linder, M.; Hunt, T.; Winter, B.; Focardi, M.; Malaguti, G.; Micela, G.; Pace, E.; Tinetti, G.

    2015-12-01

    The Exoplanet Characterisation Observatory (EChO) is a space mission dedicated to investigate exoplanetary atmospheres by undertaking spectroscopy of transiting planets in a wide spectral region from the visible to the mid-InfraRed (IR). The high sensitivity and the long exposures required by the mission need an extremely stable thermo-mechanical platform. The instrument is passively cooled down to approximately 40 K, together with the telescope assembly, by a V-Groove based design that exploits the L2 orbit favourable thermal conditions. The visible and short-IR wavelength detectors are maintained at the operating temperature of 40 K by a dedicated radiator coupled to the cold space. The mid-IR channels, require a lower operating temperature and are cooled by an active refrigerator: a 28 K Neon Joule-Thomson (JT) cold end, fed by a mechanical compressor. Temperature stability is one of the challenging issues of the whole architecture: periodical perturbations must be controlled before they reach the sensitive units of the instrument. An efficient thermal control system is required: the design is based on a combination of passive and active solutions. In this paper we describe the thermal architecture of the payload with the main cryo-chain stages and their temperature control systems. The requirements that drive the design and the trade-offs needed to enable the EChO exciting science in a technically feasible payload design are discussed. Thermal modelling results and preliminary performance predictions in terms of steady state and transient conditions are also reported. This paper is presented on behalf of the EChO Consortium.

  6. Multi-objective optimal design of online PID controllers using model predictive control based on the group method of data handling-type neural networks

    NASA Astrophysics Data System (ADS)

    Majdabadi-Farahani, V.; Hanif, M.; Gholaminezhad, I.; Jamali, A.; Nariman-Zadeh, N.

    2014-10-01

    In this paper, model predictive control (MPC) is used for optimal selection of proportional-integral-derivative (PID) controller gains. In conventional tuning methods a history of response error of the system under control in the passed time is measured and used to adjust PID parameters in order to improve the performance of the system in proceeding time. But MPC obviates this characteristic of classic PID. In fact MPC tries to tune the controller by predicting the system's behaviour some time steps ahead. In this way, PID parameters are adjusted before any real error occurs in the system's response. For this purpose, polynomial meta-models based on the evolved group method of data handling neural networks are obtained to simply simulate the time response of the dynamic system. Moreover, a non-dominated sorting genetic algorithm has been used in a multi-objective Pareto optimisation to select the parameters of the MPC which are prediction horizon, control horizon and relation of weight of Δ u and error, to minimise simultaneously two objective functions that are control effort and integral time absolute error of the system response. The results mentioned at the end obviously declare that the proposed method surpasses conventional tuning methods for PID controllers, and Pareto optimal selection of predictive parameters also improves the performance of the introduced method.

  7. Predictions of epidemiology and the evaluation of cancer control measures and the setting of policy priorities.

    PubMed

    Hakulinen, T; Hakama, M

    1991-01-01

    Cancer incidence predictions may be constructed for administrative and scientific purposes. For administrative purposes it is often important that the predictions come true. The resources planned on the basis of the predictions and allocated on the diagnostics, treatment and rehabilitation can then be optimally utilized. However, predictions that do not materialize can also be useful. The effects of intervention or early detection programmes express themselves as failures of predictions that have been made in the absence of such programmes. Predictions of cancer incidence in Finland are used as examples. The prerequisite for the predictions is a well-functioning population-based cancer registry. The predictions were constructed using time trends and differentials in cancer incidence with or without the aetiological or other risk factors. Short-term, 10-15 year predictions with no explicit use of risk factors, have proven successful with most cancers, e.g. those of the colon, rectum, pancreas and urinary organs, and lymphomas. The marked prediction failures have occurred for cancers of the lung and breast. Predictions for these cancers have been improved by taking aetiological or other risk factors explicitly into account. The cancer consequences of the preventive cardiovascular programme in North Karelia have been evaluated using predictions. The effectiveness of screening for cervical cancer at population level was predicted on the basis of estimated parameters of the natural history of the disease.(ABSTRACT TRUNCATED AT 250 WORDS)

  8. Target switching in curved human arm movements is predicted by changing a single control parameter.

    PubMed

    Hoffmann, Heiko

    2011-01-01

    Straight-line movements have been studied extensively in the human motor-control literature, but little is known about how to generate curved movements and how to adjust them in a dynamic environment. The present work studied, for the first time to my knowledge, how humans adjust curved hand movements to a target that switches location. Subjects (n = 8) sat in front of a drawing tablet and looked at a screen. They moved a cursor on a curved trajectory (spiral or oval shaped) toward a goal point. In half of the trials, this goal switched 200 ms after movement onset to either one of two alternative positions, and subjects smoothly adjusted their movements to the new goal. To explain this adjustment, we compared three computational models: a superposition of curved and minimum-jerk movements (Flash and Henis in J Cogn Neurosci 3(3):220-230, 1991), Vector Planning (Gordon et al. in Exp Brain Res 99(1):97-111, 1994) adapted to curved movements (Rescale), and a nonlinear dynamical system, which could generate arbitrarily curved smooth movements and had a point attractor at the goal. For each model, we predicted the trajectory adjustment to the target switch by changing only the goal position in the model. As result, the dynamical model could explain the observed switch behavior significantly better than the two alternative models (spiral: P = 0.0002 vs. Flash, P = 0.002 vs. Rescale; oval: P = 0.04 vs. Flash; P values obtained from Wilcoxon test on R (2) values). We conclude that generalizing arbitrary hand trajectories to new targets may be explained by switching a single control command, without the need to re-plan or re-optimize the whole movement or superimpose movements.

  9. A predicted and controlled jøkulhlaup (GLOF) from Harbardsbreen, Norway.

    NASA Astrophysics Data System (ADS)

    Jackson, Miriam; Engeset, Rune; Elvehøy, Hallgeir

    2016-04-01

    A jøkulhlaup (GLOF) from Harbardsbreen, Norway in 2015 was predicted and the runoff of over five million cubic metres of water was released in a controlled manner through a hydropower reservoir. The glacier-dammed lake was investigated in early August and showed evidence (change in water-level, radial crevasses) of movement of water from the lake to subglacial storage. The water level in the glacier-dammed lake was over overburden pressure but the event didn't occur until two weeks later. There have been previous events from this glacier, with annual events between 1996 and 2001 due to glacier thinning. The glacier continued to thin substantially (20m) over the next decade but the next event didn't occur until 2010 when over five million cubic metres of water was released as well as additional runoff due to heavy precipitation and melting. At this time the reservoir couldn't accommodate the extra water and there was flow over the dam. Several glaciers in Norway have had one or several jøkulhlaups in the last fifteen years due to glacier thinning, and at several others, glacier-dammed lakes have appeared for the first time. Hydropower reservoirs are situated downstream from some of these glaciers, so such an event can have a beneficial effect. However, negative glacier mass balance and subsequent glacier thinning is increasing the magnitude and frequency of events.

  10. Predicting childhood effortful control from interactions between early parenting quality and children's dopamine transporter gene haplotypes.

    PubMed

    Li, Yi; Sulik, Michael J; Eisenberg, Nancy; Spinrad, Tracy L; Lemery-Chalfant, Kathryn; Stover, Daryn A; Verrelli, Brian C

    2016-02-01

    Children's observed effortful control (EC) at 30, 42, and 54 months (n = 145) was predicted from the interaction between mothers' observed parenting with their 30-month-olds and three variants of the solute carrier family C6, member 3 (SLC6A3) dopamine transporter gene (single nucleotide polymorphisms in intron8 and intron13, and a 40 base pair variable number tandem repeat [VNTR] in the 3'-untranslated region [UTR]), as well as haplotypes of these variants. Significant moderating effects were found. Children without the intron8-A/intron13-G, intron8-A/3'-UTR VNTR-10, or intron13-G/3'-UTR VNTR-10 haplotypes (i.e., haplotypes associated with the reduced SLC6A3 gene expression and thus lower dopamine functioning) appeared to demonstrate altered levels of EC as a function of maternal parenting quality, whereas children with these haplotypes demonstrated a similar EC level regardless of the parenting quality. Children with these haplotypes demonstrated a trade-off, such that they showed higher EC, relative to their counterparts without these haplotypes, when exposed to less supportive maternal parenting. The findings revealed a diathesis-stress pattern and suggested that different SLC6A3 haplotypes, but not single variants, might represent different levels of young children's sensitivity/responsivity to early parenting. PMID:25924976

  11. Predicting childhood effortful control from interactions between early parenting quality and children's dopamine transporter gene haplotypes.

    PubMed

    Li, Yi; Sulik, Michael J; Eisenberg, Nancy; Spinrad, Tracy L; Lemery-Chalfant, Kathryn; Stover, Daryn A; Verrelli, Brian C

    2016-02-01

    Children's observed effortful control (EC) at 30, 42, and 54 months (n = 145) was predicted from the interaction between mothers' observed parenting with their 30-month-olds and three variants of the solute carrier family C6, member 3 (SLC6A3) dopamine transporter gene (single nucleotide polymorphisms in intron8 and intron13, and a 40 base pair variable number tandem repeat [VNTR] in the 3'-untranslated region [UTR]), as well as haplotypes of these variants. Significant moderating effects were found. Children without the intron8-A/intron13-G, intron8-A/3'-UTR VNTR-10, or intron13-G/3'-UTR VNTR-10 haplotypes (i.e., haplotypes associated with the reduced SLC6A3 gene expression and thus lower dopamine functioning) appeared to demonstrate altered levels of EC as a function of maternal parenting quality, whereas children with these haplotypes demonstrated a similar EC level regardless of the parenting quality. Children with these haplotypes demonstrated a trade-off, such that they showed higher EC, relative to their counterparts without these haplotypes, when exposed to less supportive maternal parenting. The findings revealed a diathesis-stress pattern and suggested that different SLC6A3 haplotypes, but not single variants, might represent different levels of young children's sensitivity/responsivity to early parenting.

  12. Cognitive trait anxiety, situational stress, and mental effort predict shifting efficiency: Implications for attentional control theory.

    PubMed

    Edwards, Elizabeth J; Edwards, Mark S; Lyvers, Michael

    2015-06-01

    Attentional control theory (ACT) predicts that trait anxiety and situational stress interact to impair performance on tasks that involve attentional shifting. The theory suggests that anxious individuals recruit additional effort to prevent shortfalls in performance effectiveness (accuracy), with deficits becoming evident in processing efficiency (the relationship between accuracy and time taken to perform the task). These assumptions, however, have not been systematically tested. The relationship between cognitive trait anxiety, situational stress, and mental effort in a shifting task (Wisconsin Card Sorting Task) was investigated in 90 participants. Cognitive trait anxiety was operationalized using questionnaire scores, situational stress was manipulated through ego threat instructions, and mental effort was measured using a visual analogue scale. Dependent variables were performance effectiveness (an inverse proportion of perseverative errors) and processing efficiency (an inverse proportion of perseverative errors divided by response time on perseverative error trials). The predictors were not associated with performance effectiveness; however, we observed a significant 3-way interaction on processing efficiency. At higher mental effort (+1 SD), higher cognitive trait anxiety was associated with poorer efficiency independently of situational stress, whereas at lower effort (-1 SD), this relationship was highly significant and most pronounced for those in the high-stress condition. These results are important because they provide the first systematic test of the relationship between trait anxiety, situational stress, and mental effort on shifting performance. The data are also consistent with the notion that effort moderates the relationship between anxiety and shifting efficiency, but not effectiveness.

  13. Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR).

    PubMed

    Hammer, Eva M; Kaufmann, Tobias; Kleih, Sonja C; Blankertz, Benjamin; Kübler, Andrea

    2014-01-01

    Modulation of sensorimotor rhythms (SMR) was suggested as a control signal for brain-computer interfaces (BCI). Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80-100%) performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCI performance from psychological variables that were assessed prior to the BCI sessions and BCI control was supported with machine-learning techniques. We described two significant psychological predictors, namely the visuo-motor coordination ability and the ability to concentrate on the task. The purpose of the current study was to replicate these results thereby validating these predictors within a neurofeedback based SMR-BCI that involved no machine learning.Thirty-three healthy BCI novices participated in a calibration session and three further neurofeedback training sessions. Two variables were related with mean SMR-BCI performance: (1) a measure for the accuracy of fine motor skills, i.e., a trade for a person's visuo-motor control ability; and (2) subject's "attentional impulsivity". In a linear regression they accounted for almost 20% in variance of SMR-BCI performance, but predictor (1) failed significance. Nevertheless, on the basis of our prior regression model for sensorimotor control ability we could predict current SMR-BCI performance with an average prediction error of M = 12.07%. In more than 50% of the participants, the prediction error was smaller than 10%. Hence, psychological variables played a moderate role in predicting SMR-BCI performance in a neurofeedback approach that involved no machine learning. Future studies are needed to further consolidate (or reject) the present predictors.

  14. Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR)

    PubMed Central

    Hammer, Eva M.; Kaufmann, Tobias; Kleih, Sonja C.; Blankertz, Benjamin; Kübler, Andrea

    2014-01-01

    Modulation of sensorimotor rhythms (SMR) was suggested as a control signal for brain-computer interfaces (BCI). Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80–100%) performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCI performance from psychological variables that were assessed prior to the BCI sessions and BCI control was supported with machine-learning techniques. We described two significant psychological predictors, namely the visuo-motor coordination ability and the ability to concentrate on the task. The purpose of the current study was to replicate these results thereby validating these predictors within a neurofeedback based SMR-BCI that involved no machine learning.Thirty-three healthy BCI novices participated in a calibration session and three further neurofeedback training sessions. Two variables were related with mean SMR-BCI performance: (1) a measure for the accuracy of fine motor skills, i.e., a trade for a person’s visuo-motor control ability; and (2) subject’s “attentional impulsivity”. In a linear regression they accounted for almost 20% in variance of SMR-BCI performance, but predictor (1) failed significance. Nevertheless, on the basis of our prior regression model for sensorimotor control ability we could predict current SMR-BCI performance with an average prediction error of M = 12.07%. In more than 50% of the participants, the prediction error was smaller than 10%. Hence, psychological variables played a moderate role in predicting SMR-BCI performance in a neurofeedback approach that involved no machine learning. Future studies are needed to further consolidate (or reject) the present predictors. PMID:25147518

  15. Assessment of quantitative structure-activity relationship of toxicity prediction models for Korean chemical substance control legislation

    PubMed Central

    Kim, Kwang-Yon; Shin, Seong Eun; No, Kyoung Tai

    2015-01-01

    Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used

  16. Implementation of PFC (Predictive Functional Control) in a PLC (Programmable Logic Controller) for a HVAC (Heating, Ventilation and Air Conditioning) system

    NASA Astrophysics Data System (ADS)

    Kreutz, M.; Richalet, J.; Mocha, K.; Haber, R.

    2014-12-01

    HVAC systems of industrial buildings consume a lot of energy. Therefore it is important to know the performance of these systems and strategies to optimize the hardware and the control. Tackling the temperature control of the HVAC system promises quick savings by tuning the control within specified tolerance limits, which mostly can be done by low investment. This paper mainly deals with the implementation strategy of a new controller in a PLC using the predictive functional control for temperature control. The different stages of the implementation from the simulation over the SCL code till to the real-time operation are presented. A bumpless switch between the PI(D) and the PFC control was realized, as well.

  17. Encoderless Model Predictive Control of Doubly-Fed Induction Generators in Variable-Speed Wind Turbine Systems

    NASA Astrophysics Data System (ADS)

    Abdelrahem, Mohamed; Hackl, Christoph; Kennel, Ralph

    2016-09-01

    In this paper, an encoderless finite-control-set model predictive control (FCS-MPC) strategy for doubly-fed induction generators (DFIGs) based on variable-speed wind turbine systems (WTSs) is proposed. According to the FCS-MPC concept, the discrete states of the power converter are taken into account and the future converter performance is predicted for each sampling period. Subsequently, the voltage vector that minimizes a predefined cost function is selected to be applied in the next sampling instant. Furthermore, a model reference adaptive system (MRAS) observer is used to estimate the rotor speed and position of the DFIG. Estimation and control performance of the proposed encoderless control method are validated by simulation results for all operation conditions. Moreover, the performance of the MRAS observer is tested under variations of the DFIG parameters.

  18. Operation assistance for the Bio-Remote environmental control system using a Bayesian Network-based prediction model.

    PubMed

    Shibanoki, Taro; Nakamura, Go; Shima, Keisuke; Chin, Takaaki; Tsuji, Toshio

    2015-08-01

    This paper proposes a Bayesian Network (BN) based prediction model for a layer-based selection and its application to an operation assistance for the environmental control system Bio-Remote (BR). In the proposed method, each node of the BN model is involved in the layer-based selection function, which corresponds to an individual operation command, appliance, etc., and previous logs of operation commands and time division are used as input factors to predict the user's intended operation. The prediction results are displayed on the layer-based selection for the BR, and the number of times of operations and time taken for the operations can be reduced with the proposed prediction model. In the experiments, life-logs were collected from a cervical spinal injury patient who used the BR in daily life, and the proposed model was trained based on these recorded life-logs. The prediction accuracy for control devices of the BR system using the proposed model was 84.3 ± 6.5 %. The results indicated that the proposed prediction model could be useful for the operation assistance of the BR system. PMID:26736472

  19. Predictive factors for surgical site infection in head and neck cancer surgery: A case-control study.

    PubMed

    Henry, J S; Buffet-Bataillon, S; Deberge, S; Jegoux, F

    2014-01-01

    Surgical Site Infection (SSI) after head and neck cancer surgery may be life threatening and induces increasing in healthcare cost. The objective of this present study was to identify predictive factors associated to surgical site infection in head and neck cancer surgery. Numerous predictive factors were analyzed with univariate case-control method, then with multivariate method. This retrospective study included 71 patients who have been hospitalized in our department during 2010 for a head and neck cancer surgery. The incidence of surgical site infection was 15.5%. The T3-T4 stages were identified as an independent predictive factor (p = 0.04). Our study does not find other predictive factor for a SSI. The NNIS index (National Nosocomial Infections Surveillance), used by the Center for disease control and prevention as predictive factor, was not valid in our study. A specific predictive index should include the tumor stage for Head and Neck Cancer surgery and should be taken into account for the management of a preventive antibiotic treatment.

  20. Use of Plant Hydraulic Theory to Predict Ecosystem Fluxes Across Mountainous Gradients in Environmental Controls and Insect Disturbances

    NASA Astrophysics Data System (ADS)

    Ewers, B. E.; Pendall, E.; Reed, D. E.; Barnard, H. R.; Whitehouse, F.; Frank, J. M.; Massman, W. J.; Brooks, P. D.; Biederman, J. A.; Harpold, A. A.; Naithani, K. J.; Mitra, B.; Mackay, D. S.; Norton, U.; Borkhuu, B.

    2011-12-01

    While mountainous areas are critical for providing numerous ecosystem benefits at the regional scale, the strong gradients in environmental controls make predictions difficult. A key part of the problem is quantifying and predicting the feedback between mountain gradients and plant function which then controls ecosystem cycling. The emerging theory of plant hydraulics provides a rigorous yet simple platform from which to generate testable hypotheses and predictions of ecosystem pools and fluxes. Plant hydraulic theory predicts that plant controls over carbon, water, energy and nutrient fluxes can be derived from the limitation of plant water transport from the soil through xylem and out of stomata. In addition, the limit to plant water transport can be predicted by combining plant structure (e.g. xylem diameters or root-to-shoot ratios) and plant function (response of stomatal conductance to vapor pressure deficit or root vulnerability to cavitation). We evaluate the predictions of the plant hydraulic theory by testing it against data from a mountain gradient encompassing sagebrush steppe through subalpine forests (2700 to 3400 m). We further test the theory by predicting the carbon, water and nutrient exchanges from several coniferous trees in the same gradient that are dying from xylem dysfunction caused by blue-stain fungi carried by bark beetles. The common theme of both of these data sets is a change in water limitation caused by either changing precipitation along the mountainous gradient or lack of access to soil water from xylem-occluding fungi. Across all of the data sets which range in scale from individual plants to hillslopes, the data fit the predictions of plant hydraulic theory. Namely, there was a proportional tradeoff between the reference canopy stomatal conductance to water vapor and the sensitivity of that conductance to vapor pressure deficit that quantitatively fits the predictions of plant hydraulic theory. Incorporating this result into

  1. Positron Emission Tomography (PET) Evaluation After Initial Chemotherapy and Radiation Therapy Predicts Local Control in Rhabdomyosarcoma

    SciTech Connect

    Dharmarajan, Kavita V.; Wexler, Leonard H.; Gavane, Somali; Fox, Josef J.; Schoder, Heiko; Tom, Ashlyn K.; Price, Alison N.; Meyers, Paul A.; Wolden, Suzanne L.

    2012-11-15

    Purpose: 18-fluorodeoxyglucose positron emission tomography (PET) is already an integral part of staging in rhabdomyosarcoma. We investigated whether primary-site treatment response characterized by serial PET imaging at specific time points can be correlated with local control. Patients and Methods: We retrospectively examined 94 patients with rhabdomyosarcoma who received initial chemotherapy 15 weeks (median) before radiotherapy and underwent baseline, preradiation, and postradiation PET. Baseline PET standardized uptake values (SUVmax) and the presence or absence of abnormal uptake (termed PET-positive or PET-negative) both before and after radiation were examined for the primary site. Local relapse-free survival (LRFS) was calculated according to baseline SUVmax, PET-positive status, and PET-negative status by the Kaplan-Meier method, and comparisons were tested with the log-rank test. Results: The median patient age was 11 years. With 3-year median follow-up, LRFS was improved among postradiation PET-negative vs PET-positive patients: 94% vs 75%, P=.02. By contrast, on baseline PET, LRFS was not significantly different for primary-site SUVmax {<=}7 vs >7 (median), although the findings suggested a trend toward improved LRFS: 96% for SUVmax {<=}7 vs 79% for SUVmax >7, P=.08. Preradiation PET also suggested a statistically insignificant trend toward improved LRFS for PET-negative (97%) vs PET-positive (81%) patients (P=.06). Conclusion: Negative postradiation PET predicted improved LRFS. Notably, 77% of patients with persistent postradiation uptake did not experience local failure, suggesting that these patients could be closely followed up rather than immediately referred for intervention. Negative baseline and preradiation PET findings suggested statistically insignificant trends toward improved LRFS. Additional study may further understanding of relationships between PET findings at these time points and outcome in rhabdomyosarcoma.

  2. Image Discrimination Predictions of a Single Channel Model with Contrast Gain Control

    NASA Technical Reports Server (NTRS)

    Ahumada, Albert J., Jr.; Null, Cynthia H.

    1995-01-01

    Image discrimination models predict the number of just-noticeable-differences between two images. We report the predictions of a single channel model with contrast masking for a range of standard discrimination experiments. Despite its computational simplicity, this model has performed as well as a multiple channel model in an object detection task.

  3. On the incremental validity of irrational beliefs to predict subjective well-being while controlling for personality factors.

    PubMed

    Spörrle, Matthias; Strobel, Maria; Tumasjan, Andranik

    2010-11-01

    This research examines the incremental validity of irrational thinking as conceptualized by Albert Ellis to predict diverse aspects of subjective well-being while controlling for the influence of personality factors. Rational-emotive behavior therapy (REBT) argues that irrational beliefs result in maladaptive emotions leading to reduced well-being. Although there is some early scientific evidence for this relation, it has never been investigated whether this connection would still persist when statistically controlling for the Big Five personality factors, which were consistently found to be important determinants of well-being. Regression analyses revealed significant incremental validity of irrationality over personality factors when predicting life satisfaction, but not when predicting subjective happiness. Results are discussed with respect to conceptual differences between these two aspects of subjective well-being.

  4. Predicting Participation in Group Parenting Education in an Australian Sample: The Role of Attitudes, Norms, and Control Factors

    ERIC Educational Resources Information Center

    White, Katherine M.; Wellington, Larne

    2009-01-01

    We examined the theory of planned behavior (TPB) in predicting intentions to participate in group parenting education. One hundred and seventy-six parents (138 mothers and 38 fathers) with a child under 12 years completed TPB items assessing attitude, subjective norms, perceived behavioral control (PBC), and two additional social influence…

  5. Intentions and Trait Self-Control Predict Fruit and Vegetable Consumption during the Transition to First-Year University

    ERIC Educational Resources Information Center

    Tomasone, Jennifer R.; Meikle, Natasha; Bray, Steven R.

    2015-01-01

    Objective: To examine the independent and combined effects of Theory of Planned Behavior (TPB) variables and trait self-control (TSC) in the prediction of fruit and vegetable consumption (FVC) among first-year university students. Participants: Seventy-six first-year undergraduate university students. Methods: In their first week of class…

  6. Model Predictive Control techniques with application to photovoltaic, DC Microgrid, and a multi-sourced hybrid energy system

    NASA Astrophysics Data System (ADS)

    Shadmand, Mohammad Bagher

    Renewable energy sources continue to gain popularity. However, two major limitations exist that prevent widespread adoption: availability and variability of the electricity generated and the cost of the equipment. The focus of this dissertation is Model Predictive Control (MPC) for optimal sized photovoltaic (PV), DC Microgrid, and multi-sourced hybrid energy systems. The main considered applications are: maximum power point tracking (MPPT) by MPC, droop predictive control of DC microgrid, MPC of grid-interaction inverter, MPC of a capacitor-less VAR compensator based on matrix converter (MC). This dissertation firstly investigates a multi-objective optimization technique for a hybrid distribution system. The variability of a high-penetration PV scenario is also studied when incorporated into the microgrid concept. Emerging (PV) technologies have enabled the creation of contoured and conformal PV surfaces; the effect of using non-planar PV modules on variability is also analyzed. The proposed predictive control to achieve maximum power point for isolated and grid-tied PV systems speeds up the control loop since it predicts error before the switching signal is applied to the converter. The low conversion efficiency of PV cells means we want to ensure always operating at maximum possible power point to make the system economical. Thus the proposed MPPT technique can capture more energy compared to the conventional MPPT techniques from same amount of installed solar panel. Because of the MPPT requirement, the output voltage of the converter may vary. Therefore a droop control is needed to feed multiple arrays of photovoltaic systems to a DC bus in microgrid community. Development of a droop control technique by means of predictive control is another application of this dissertation. Reactive power, denoted as Volt Ampere Reactive (VAR), has several undesirable consequences on AC power system network such as reduction in power transfer capability and increase in

  7. A double-loop structure in the adaptive generalized predictive control algorithm for control of robot end-point contact force.

    PubMed

    Wen, Shuhuan; Zhu, Jinghai; Li, Xiaoli; Chen, Shengyong

    2014-09-01

    Robot force control is an essential issue in robotic intelligence. There is much high uncertainty when robot end-effector contacts with the environment. Because of the environment stiffness effects on the system of the robot end-effector contact with environment, the adaptive generalized predictive control algorithm based on quantitative feedback theory is designed for robot end-point contact force system. The controller of the internal loop is designed on the foundation of QFT to control the uncertainty of the system. An adaptive GPC algorithm is used to design external loop controller to improve the performance and the robustness of the system. Two closed loops used in the design approach realize the system׳s performance and improve the robustness. The simulation results show that the algorithm of the robot end-effector contacting force control system is effective. PMID:24973336

  8. A double-loop structure in the adaptive generalized predictive control algorithm for control of robot end-point contact force.

    PubMed

    Wen, Shuhuan; Zhu, Jinghai; Li, Xiaoli; Chen, Shengyong

    2014-09-01

    Robot force control is an essential issue in robotic intelligence. There is much high uncertainty when robot end-effector contacts with the environment. Because of the environment stiffness effects on the system of the robot end-effector contact with environment, the adaptive generalized predictive control algorithm based on quantitative feedback theory is designed for robot end-point contact force system. The controller of the internal loop is designed on the foundation of QFT to control the uncertainty of the system. An adaptive GPC algorithm is used to design external loop controller to improve the performance and the robustness of the system. Two closed loops used in the design approach realize the system׳s performance and improve the robustness. The simulation results show that the algorithm of the robot end-effector contacting force control system is effective.

  9. Adjoint Method and Predictive Control for 1-D Flow in NASA Ames 11-Foot Transonic Wind Tunnel

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan; Ardema, Mark

    2006-01-01

    This paper describes a modeling method and a new optimal control approach to investigate a Mach number control problem for the NASA Ames 11-Foot Transonic Wind Tunnel. The flow in the wind tunnel is modeled by the 1-D unsteady Euler equations whose boundary conditions prescribe a controlling action by a compressor. The boundary control inputs to the compressor are in turn controlled by a drive motor system and an inlet guide vane system whose dynamics are modeled by ordinary differential equations. The resulting Euler equations are thus coupled to the ordinary differential equations via the boundary conditions. Optimality conditions are established by an adjoint method and are used to develop a model predictive linear-quadratic optimal control for regulating the Mach number due to a test model disturbance during a continuous pitch

  10. Poor Self-Control and Harsh Punishment in Childhood Prospectively Predict Borderline Personality Symptoms in Adolescent Girls

    PubMed Central

    Hallquist, Michael N.; Hipwell, Alison E.; Stepp, Stephanie D.

    2015-01-01

    Developmental theories of borderline personality disorder (BPD) propose that harsh, invalidating parenting of a child with poor self-control and heightened negative emotionality often leads to a coercive cycle of parent-child transactions that increase risk for BPD symptoms such as emotion dysregulation. Although parenting practices and child temperament have previously been linked with BPD, less is known about the prospective influences of caregiver and child characteristics. Using annual longitudinal data from the Pittsburgh Girls Study (n = 2450), our study examined how reciprocal influences among harsh parenting, self-control, and negative emotionality between ages 5 and 14 predicted the development of BPD symptoms in adolescent girls ages 14 to 17. Consistent with developmental theories, we found that harsh punishment, poor self-control, and negative emotionality predicted BPD symptom severity at age 14. Only worsening self-control between ages 12 and 14, however, predicted growth in BPD symptoms from 14 to 17. Furthermore, the effects of harsh punishment and poor self-control on age 14 BPD symptoms were partially mediated by their earlier reciprocal effects on each other between ages 5 and 14. Our findings underscore the need to address both child and parental contributions to dysfunctional transactions in order to stem the development of BPD symptoms. Moreover, problems with self-regulation in early adolescence may indicate heightened risk for subsequent BPD. Altogether, these results increase our understanding of developmental trajectories associated with BPD symptoms in adolescent girls. PMID:25961815

  11. Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function.

    PubMed

    Gilzenrat, Mark S; Nieuwenhuis, Sander; Jepma, Marieke; Cohen, Jonathan D

    2010-05-01

    An important dimension of cognitive control is the adaptive regulation of the balance between exploitation (pursuing known sources of reward) and exploration (seeking new ones) in response to changes in task utility. Recent studies have suggested that the locus coeruleus-norepinephrine system may play an important role in this function and that pupil diameter can be used to index locus coeruleus activity. On the basis of this, we reasoned that pupil diameter may correlate closely with control state and associated changes in behavior. Specifically, we predicted that increases in baseline pupil diameter would be associated with decreases in task utility and disengagement from the task (exploration), whereas reduced baseline diameter (but increases in task-evoked dilations) would be associated with task engagement (exploitation). Findings in three experiments were consistent with these predictions, suggesting that pupillometry may be useful as an index of both control state and, indirectly, locus coeruleus function.

  12. The minimum requirements of language control: evidence from sequential predictability effects in language switching.

    PubMed

    Declerck, Mathieu; Koch, Iring; Philipp, Andrea M

    2015-03-01

    The current study systematically examined the influence of sequential predictability of languages and concepts on language switching. To this end, 2 language switching paradigms were combined. To measure language switching with a random sequence of languages and/or concepts, we used a language switching paradigm that implements visual cues and stimuli. The other paradigm implements a fixed sequence of languages and/or concepts to measure predictable language switching. Four experiments that used these 2 paradigms showed that switch costs were smaller when both the language and concept were predictably known, whereas no overall switch cost reduction was found when just the language or concept was predictable. These results indicate that knowing both language and concept (i.e., response) can resolve language interference. However, interference resolution does not start solely based on the knowledge of which concept or language one has to produce. We discuss how existent models should be revised to accommodate these results.

  13. Ongoing Activity in Temporally Coherent Networks Predicts Intra-Subject Fluctuation of Response Time to Sporadic Executive Control Demands

    PubMed Central

    Nozawa, Takayuki; Sugiura, Motoaki; Yokoyama, Ryoichi; Ihara, Mizuki; Kotozaki, Yuka; Miyauchi, Carlos Makoto; Kanno, Akitake; Kawashima, Ryuta

    2014-01-01

    Can ongoing fMRI BOLD signals predict fluctuations in swiftness of a person’s response to sporadic cognitive demands? This is an important issue because it clarifies whether intrinsic brain dynamics, for which spatio-temporal patterns are expressed as temporally coherent networks (TCNs), have effects not only on sensory or motor processes, but also on cognitive processes. Predictivity has been affirmed, although to a limited extent. Expecting a predictive effect on executive performance for a wider range of TCNs constituting the cingulo-opercular, fronto-parietal, and default mode networks, we conducted an fMRI study using a version of the color–word Stroop task that was specifically designed to put a higher load on executive control, with the aim of making its fluctuations more detectable. We explored the relationships between the fluctuations in ongoing pre-trial activity in TCNs and the task response time (RT). The results revealed the existence of TCNs in which fluctuations in activity several seconds before the onset of the trial predicted RT fluctuations for the subsequent trial. These TCNs were distributed in the cingulo-opercular and fronto-parietal networks, as well as in perceptual and motor networks. Our results suggest that intrinsic brain dynamics in these networks constitute “cognitive readiness,” which plays an active role especially in situations where information for anticipatory attention control is unavailable. Fluctuations in these networks lead to fluctuations in executive control performance. PMID:24901995

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

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

  15. The Natural Course of Intermittent Exotropia over a 3-year Period and the Factors Predicting the Control Deterioration.

    PubMed

    Kwok, Jeremy J S W; Chong, Gabriela S L; Ko, Simon T C; Yam, Jason C S

    2016-01-01

    The natural course of intermittent exotropia and the factors affecting its control has been unclear. We aim to report the natural course of our cohort of 117 Chinese children with intermittent exotropia and to identify baseline parameters that may have predictive value in the control deterioration of the disease. The visual acuity, spherical equivalent, compliance to orthoptic exercise, angle of deviation fusional convergence parameters and Newcastle Control Score were recorded for all children at baseline and at 3 years apart. Patients were divided into two groups according to the change in control over the 3 years: group 1 included patients who had no deterioration or had improvement in disease control; and group 2 were those who had deteriorated control or had undergone surgery. There were 77 patients (66%) in group 1 and 40 (34%) patients in group 2. Comparing the baseline parameters of the two groups, group 1 had statistically significantly smaller angle of deviation, larger fusional reserve, larger fusional recovery, and higher fusional reserve ratio (p < 0.05). Other baseline parameters were similar between the two groups. The baseline fusional parameters may have predictive value in determining the control of intermittent exotropia.

  16. The Natural Course of Intermittent Exotropia over a 3-year Period and the Factors Predicting the Control Deterioration

    PubMed Central

    Kwok, Jeremy J. S. W.; Chong, Gabriela S. L.; Ko, Simon T. C.; Yam, Jason C.S.

    2016-01-01

    The natural course of intermittent exotropia and the factors affecting its control has been unclear. We aim to report the natural course of our cohort of 117 Chinese children with intermittent exotropia and to identify baseline parameters that may have predictive value in the control deterioration of the disease. The visual acuity, spherical equivalent, compliance to orthoptic exercise, angle of deviation fusional convergence parameters and Newcastle Control Score were recorded for all children at baseline and at 3 years apart. Patients were divided into two groups according to the change in control over the 3 years: group 1 included patients who had no deterioration or had improvement in disease control; and group 2 were those who had deteriorated control or had undergone surgery. There were 77 patients (66%) in group 1 and 40 (34%) patients in group 2. Comparing the baseline parameters of the two groups, group 1 had statistically significantly smaller angle of deviation, larger fusional reserve, larger fusional recovery, and higher fusional reserve ratio (p < 0.05). Other baseline parameters were similar between the two groups. The baseline fusional parameters may have predictive value in determining the control of intermittent exotropia. PMID:27257120

  17. The predictability of frequency-altered auditory feedback changes the weighting of feedback and feedforward input for speech motor control.

    PubMed

    Scheerer, Nichole E; Jones, Jeffery A

    2014-12-01

    Speech production requires the combined effort of a feedback control system driven by sensory feedback, and a feedforward control system driven by internal models. However, the factors that dictate the relative weighting of these feedback and feedforward control systems are unclear. In this event-related potential (ERP) study, participants produced vocalisations while being exposed to blocks of frequency-altered feedback (FAF) perturbations that were either predictable in magnitude (consistently either 50 or 100 cents) or unpredictable in magnitude (50- and 100-cent perturbations varying randomly within each vocalisation). Vocal and P1-N1-P2 ERP responses revealed decreases in the magnitude and trial-to-trial variability of vocal responses, smaller N1 amplitudes, and shorter vocal, P1 and N1 response latencies following predictable FAF perturbation magnitudes. In addition, vocal response magnitudes correlated with N1 amplitudes, vocal response latencies, and P2 latencies. This pattern of results suggests that after repeated exposure to predictable FAF perturbations, the contribution of the feedforward control system increases. Examination of the presentation order of the FAF perturbations revealed smaller compensatory responses, smaller P1 and P2 amplitudes, and shorter N1 latencies when the block of predictable 100-cent perturbations occurred prior to the block of predictable 50-cent perturbations. These results suggest that exposure to large perturbations modulates responses to subsequent perturbations of equal or smaller size. Similarly, exposure to a 100-cent perturbation prior to a 50-cent perturbation within a vocalisation decreased the magnitude of vocal and N1 responses, but increased P1 and P2 latencies. Thus, exposure to a single perturbation can affect responses to subsequent perturbations.

  18. The role of insecure attachment and gender role stress in predicting controlling behaviors in men who batter.

    PubMed

    Mahalik, James R; Aldarondo, Etiony; Gilbert-Gokhale, Steven; Shore, Erika

    2005-05-01

    The authors hypothesized that masculine gender role stress would mediate the relationship between insecure attachment and controlling behaviors in a sample of men who batter. To examine this hypothesis,143 men who were court mandated to attend a batterers' intervention program in a northeastern state completed measures including the Controlling Behavior Index, the Gender Role Stress Scale, the Relationship Questionnaire, and the Marlowe-Crowne Social Desirability Scale. After controlling statistically for social desirability, results indicated that both fearful attachment and gender role stress significantly predicted controlling behaviors, with gender role stress partially mediating the relationship between fearful attachment and controlling behaviors. The discussion focuses on the importance of understanding partner abuse through a gendered context.

  19. Prediction of fruit and vegetable intake from biomarkers using individual participant data of diet-controlled intervention studies.

    PubMed

    Souverein, Olga W; de Vries, Jeanne H M; Freese, Riitta; Watzl, Bernhard; Bub, Achim; Miller, Edgar R; Castenmiller, Jacqueline J M; Pasman, Wilrike J; van Het Hof, Karin; Chopra, Mridula; Karlsen, Anette; Dragsted, Lars O; Winkels, Renate; Itsiopoulos, Catherine; Brazionis, Laima; O'Dea, Kerin; van Loo-Bouwman, Carolien A; Naber, Ton H J; van der Voet, Hilko; Boshuizen, Hendriek C

    2015-05-14

    Fruit and vegetable consumption produces changes in several biomarkers in blood. The present study aimed to examine the dose-response curve between fruit and vegetable consumption and carotenoid (α-carotene, β-carotene, β-cryptoxanthin, lycopene, lutein and zeaxanthin), folate and vitamin C concentrations. Furthermore, a prediction model of fruit and vegetable intake based on these biomarkers and subject characteristics (i.e. age, sex, BMI and smoking status) was established. Data from twelve diet-controlled intervention studies were obtained to develop a prediction model for fruit and vegetable intake (including and excluding fruit and vegetable juices). The study population in the present individual participant data meta-analysis consisted of 526 men and women. Carotenoid, folate and vitamin C concentrations showed a positive relationship with fruit and vegetable intake. Measures of performance for the prediction model were calculated using cross-validation. For the prediction model of fruit, vegetable and juice intake, the root mean squared error (RMSE) was 258.0 g, the correlation between observed and predicted intake was 0.78 and the mean difference between observed and predicted intake was - 1.7 g (limits of agreement: - 466.3, 462.8 g). For the prediction of fruit and vegetable intake (excluding juices), the RMSE was 201.1 g, the correlation was 0.65 and the mean bias was 2.4 g (limits of agreement: -368.2, 373.0 g). The prediction models which include the biomarkers and subject characteristics may be used to estimate average intake at the group level and to investigate the ranking of individuals with regard to their intake of fruit and vegetables when validating questionnaires that measure intake.

  20. Prediction of fruit and vegetable intake from biomarkers using individual participant data of diet-controlled intervention studies.

    PubMed

    Souverein, Olga W; de Vries, Jeanne H M; Freese, Riitta; Watzl, Bernhard; Bub, Achim; Miller, Edgar R; Castenmiller, Jacqueline J M; Pasman, Wilrike J; van Het Hof, Karin; Chopra, Mridula; Karlsen, Anette; Dragsted, Lars O; Winkels, Renate; Itsiopoulos, Catherine; Brazionis, Laima; O'Dea, Kerin; van Loo-Bouwman, Carolien A; Naber, Ton H J; van der Voet, Hilko; Boshuizen, Hendriek C

    2015-05-14

    Fruit and vegetable consumption produces changes in several biomarkers in blood. The present study aimed to examine the dose-response curve between fruit and vegetable consumption and carotenoid (α-carotene, β-carotene, β-cryptoxanthin, lycopene, lutein and zeaxanthin), folate and vitamin C concentrations. Furthermore, a prediction model of fruit and vegetable intake based on these biomarkers and subject characteristics (i.e. age, sex, BMI and smoking status) was established. Data from twelve diet-controlled intervention studies were obtained to develop a prediction model for fruit and vegetable intake (including and excluding fruit and vegetable juices). The study population in the present individual participant data meta-analysis consisted of 526 men and women. Carotenoid, folate and vitamin C concentrations showed a positive relationship with fruit and vegetable intake. Measures of performance for the prediction model were calculated using cross-validation. For the prediction model of fruit, vegetable and juice intake, the root mean squared error (RMSE) was 258.0 g, the correlation between observed and predicted intake was 0.78 and the mean difference between observed and predicted intake was - 1.7 g (limits of agreement: - 466.3, 462.8 g). For the prediction of fruit and vegetable intake (excluding juices), the RMSE was 201.1 g, the correlation was 0.65 and the mean bias was 2.4 g (limits of agreement: -368.2, 373.0 g). The prediction models which include the biomarkers and subject characteristics may be used to estimate average intake at the group level and to investigate the ranking of individuals with regard to their intake of fruit and vegetables when validating questionnaires that measure intake. PMID:25850683

  1. Demand response-enabled model predictive HVAC load control in buildings using real-time electricity pricing

    NASA Astrophysics Data System (ADS)

    Avci, Mesut

    A practical cost and energy efficient model predictive control (MPC) strategy is proposed for HVAC load control under dynamic real-time electricity pricing. The MPC strategy is built based on a proposed model that jointly minimizes the total energy consumption and hence, cost of electricity for the user, and the deviation of the inside temperature from the consumer's preference. An algorithm that assigns temperature set-points (reference temperatures) to price ranges based on the consumer's discomfort tolerance index is developed. A practical parameter prediction model is also designed for mapping between the HVAC load and the inside temperature. The prediction model and the produced temperature set-points are integrated as inputs into the MPC controller, which is then used to generate signal actions for the AC unit. To investigate and demonstrate the effectiveness of the proposed approach, a simulation based experimental analysis is presented using real-life pricing data. An actual prototype for the proposed HVAC load control strategy is then built and a series of prototype experiments are conducted similar to the simulation studies. The experiments reveal that the MPC strategy can lead to significant reductions in overall energy consumption and cost savings for the consumer. Results suggest that by providing an efficient response strategy for the consumers, the proposed MPC strategy can enable the utility providers to adopt efficient demand management policies using real-time pricing. Finally, a cost-benefit analysis is performed to display the economic feasibility of implementing such a controller as part of a building energy management system, and the payback period is identified considering cost of prototype build and cost savings to help the adoption of this controller in the building HVAC control industry.

  2. A study of the prediction of cruise noise and laminar flow control noise criteria for subsonic air transports

    NASA Technical Reports Server (NTRS)

    Swift, G.; Mungur, P.

    1979-01-01

    General procedures for the prediction of component noise levels incident upon airframe surfaces during cruise are developed. Contributing noise sources are those associated with the propulsion system, the airframe and the laminar flow control (LFC) system. Transformation procedures from the best prediction base of each noise source to the transonic cruise condition are established. Two approaches to LFC/acoustic criteria are developed. The first is a semi-empirical extension of the X-21 LFC/acoustic criteria to include sensitivity to the spectrum and directionality of the sound field. In the second, the more fundamental problem of how sound excites boundary layer disturbances is analyzed by deriving and solving an inhomogeneous Orr-Sommerfeld equation in which the source terms are proportional to the production and dissipation of sound induced fluctuating vorticity. Numerical solutions are obtained and compared with corresponding measurements. Recommendations are made to improve and validate both the cruise noise prediction methods and the LFC/acoustic criteria.

  3. Nomogram to predict the number of oocytes retrieved in controlled ovarian stimulation

    PubMed Central

    Moon, Kyoung Yong; Kim, Hoon; Lee, Joong Yeup; Lee, Jung Ryeol; Jee, Byung Chul; Suh, Chang Suk; Kim, Ki Chul; Lee, Won Don; Lim, Jin Ho

    2016-01-01

    Objective Ovarian reserve tests are commonly used to predict ovarian response in infertile patients undergoing ovarian stimulation. Although serum markers such as basal follicle-stimulating hormone (FSH) or random anti-Müllerian hormone (AMH) level and ultrasonographic markers (antral follicle count, AFC) are good predictors, no single test has proven to be the best predictor. In this study, we developed appropriate equations and novel nomograms to predict the number of oocytes that will be retrieved using patients' age, serum levels of basal FSH and AMH, and AFC. Methods We analyzed a database containing clinical and laboratory information of 141 stimulated in vitro fertilization (IVF) cycles performed at a university-based hospital between September 2009 and December 2013. We used generalized linear models for prediction of the number of oocytes. Results Age, basal serum FSH level, serum AMH level, and AFC were significantly related to the number of oocytes retrieved according to the univariate and multivariate analyses. The equations that predicted the number of oocytes retrieved (log scale) were as follows: model (1) 3.21–0.036×(age)+0.089×(AMH), model (2) 3.422–0.03×(age)–0.049×(FSH)+0.08×(AMH), model (3) 2.32–0.017×(age)+0.039×(AMH)+0. 03×(AFC), model (4) 2.584–0.015×(age)–0.035×(FSH)+0.038×(AMH)+0.026×(AFC). model 4 showed the best performance. On the basis of these variables, we developed nomograms to predict the number of oocytes that can be retrieved. Conclusion Our nomograms helped predict the number of oocytes retrieved in stimulated IVF cycles. PMID:27358830

  4. The Brief Self-Control Scale Predicts Jail Inmates’ Recidivism, Substance Dependence, and Post-Release Adjustment

    PubMed Central

    2015-01-01

    Previous research finds that self-control is positively associated with adaptive and negatively associated with maladaptive behavior. However, most previous studies employ cross-sectional designs, low-risk samples, and limited assessments of self-control. This study of 553 jail inmates examined the relationship of a valid measure of self-control (Brief Self-Control Scale; BSCS) completed upon incarceration with behavior before, during, and one year after incarceration. After controlling for positive impression management (PIM), self-control was negatively related to substance misuse, suicidality, risky sex, and criminal history prior to incarceration and post-release illegal substance misuse, recidivism, and positive adjustment. Lower self-control predicted increases in substance dependence at post-release compared to pre-incarceration. Self-control was not related to misbehavior during incarceration, nor alcohol use or HIV-risk behavior one year post-release. Results were consistent as a function of age, race, and gender. This study supports self-control as an important risk and protective factor in a sample of criminal offenders. PMID:24345712

  5. Sensor-model prediction, monitoring and in-situ control of liquid RTM advanced fiber architecture composite processing

    NASA Astrophysics Data System (ADS)

    Kranbuehl, D.; Kingsley, P.; Hart, S.; Loos, A.; Hasko, G.; Dexter, B.

    In-situ frequency dependent electromagnetic sensors (FDEMS) and the Loos resin transfer model have been used to select and control the processing properties of an epoxy resin during liquid pressure RTM impregnation and cure. Once correlated with viscosity and degree of cure the FDEMS sensor monitors and the RTM processing model predicts the reaction advancement of the resin, viscosity and the impregnation of the fabric. This provides a direct means for predicting, monitoring, and controlling the liquid RTM process in-situ in the mold throughout the fabrication process and the effects of time, temperature, vacuum and pressure. Most importantly, the FDEMS-sensor model system has been developed to make intelligent decisions, thereby automating the liquid RTM process and removing the need for operator direction.

  6. Sensor-model prediction, monitoring and in-situ control of liquid RTM advanced fiber architecture composite processing

    NASA Technical Reports Server (NTRS)

    Kranbuehl, D.; Kingsley, P.; Hart, S.; Loos, A.; Hasko, G.; Dexter, B.

    1992-01-01

    In-situ frequency dependent electromagnetic sensors (FDEMS) and the Loos resin transfer model have been used to select and control the processing properties of an epoxy resin during liquid pressure RTM impregnation and cure. Once correlated with viscosity and degree of cure the FDEMS sensor monitors and the RTM processing model predicts the reaction advancement of the resin, viscosity and the impregnation of the fabric. This provides a direct means for predicting, monitoring, and controlling the liquid RTM process in-situ in the mold throughout the fabrication process and the effects of time, temperature, vacuum and pressure. Most importantly, the FDEMS-sensor model system has been developed to make intelligent decisions, thereby automating the liquid RTM process and removing the need for operator direction.

  7. The Minimum Requirements of Language Control: Evidence from Sequential Predictability Effects in Language Switching

    ERIC Educational Resources Information Center

    Declerck, Mathieu; Koch, Iring; Philipp, Andrea M.

    2015-01-01

    The current study systematically examined the influence of sequential predictability of languages and concepts on language switching. To this end, 2 language switching paradigms were combined. To measure language switching with a random sequence of languages and/or concepts, we used a language switching paradigm that implements visual cues and…

  8. Markov Model Predicts Changes in STH Prevalence during Control Activities Even with a Reduced Amount of Baseline Information

    PubMed Central

    Montresor, Antonio; Deol, Arminder; à Porta, Natacha; Lethanh, Nam; Jankovic, Dina

    2016-01-01

    Background Estimating the reduction in levels of infection during implementation of soil-transmitted helminth (STH) control programmes is important to measure their performance and to plan interventions. Markov modelling techniques have been used with some success to predict changes in STH prevalence following treatment in Viet Nam. The model is stationary and to date, the prediction has been obtained by calculating the transition probabilities between the different classes of intensity following the first year of drug distribution and assuming that these remain constant in subsequent years. However, to run this model longitudinal parasitological data (including intensity of infection) are required for two consecutive years from at least 200 individuals. Since this amount of data is not often available from STH control programmes, the possible application of the model in control programme is limited. The present study aimed to address this issue by adapting the existing Markov model to allow its application when a more limited amount of data is available and to test the predictive capacities of these simplified models. Method We analysed data from field studies conducted with different combination of three parameters: (i) the frequency of drug administration; (ii) the drug distributed; and (iii) the target treatment population (entire population or school-aged children only). This analysis allowed us to define 10 sets of standard transition probabilities to be used to predict prevalence changes when only baseline data are available (simplified model 1). We also formulated three equations (one for each STH parasite) to calculate the predicted prevalence of the different classes of intensity from the total prevalence. These equations allowed us to design a simplified model (SM2) to obtain predictions when the classes of intensity at baseline were not known. To evaluate the performance of the simplified models, we collected data from the scientific literature on

  9. Experimental study of rigorous nonlinear model predictive control for a packed distillation column

    SciTech Connect

    Junesam Lin; Shi-Shang Jang; Junghui Chen

    1996-12-31

    Dynamic modeling of distillation processes has been substantial in the recently years. In this work, a rigorous model for packed bed distillation system is solved using the method of collocation polynomial. The control methodology used is the two-phase approach that devises an on-line identification phase and an on-line optimization phase. The experimental studies show that the on-line control using this approach is feasible and performs better than traditional controllers. 7 refs., 3 figs.

  10. Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

    PubMed

    Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

    2014-05-01

    This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. PMID:24559835

  11. Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

    PubMed

    Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

    2014-05-01

    This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach.

  12. High Self-Control Predicts More Positive Emotions, Better Engagement, and Higher Achievement in School

    ERIC Educational Resources Information Center

    King, Ronnel B.; Gaerlan, Marianne Jennifer M.

    2014-01-01

    The control-value theory of academic emotions has emerged as a useful framework for studying the antecedents and consequences of different emotions in school. This framework focuses on the role of control-related and value-related appraisals as proximal antecedents of emotions. In this study, we take an individual differences approach to examine…

  13. How Home Gets to School: Parental Control Strategies Predict Children's School Readiness

    ERIC Educational Resources Information Center

    Walker, Aimee Kleisner; MacPhee, David

    2011-01-01

    At-risk families' control style (autonomy support and coercive control) was examined in relation to children's school readiness; children's social skills and mastery motivation were hypothesized mediating variables. In two different, low-income samples from diverse ethnic backgrounds, one preschool sample recruited from Head Start (N = 199) and a…

  14. Higher Levels of Psychopathy Predict Poorer Motor Control: Implications for Understanding the Psychopathy Construct

    PubMed Central

    Robinson, Michael D.; Bresin, Konrad

    2014-01-01

    A review of the literature suggests that higher levels of psychopathy may be linked to less effective behavioral control. However, several commentators have urged caution in making statements of this type in the absence of direct evidence. In two studies (total N = 142), moment-to-moment accuracy in a motor control task was examined as a function of dimensional variations in psychopathy in an undergraduate population. As hypothesized, motor control was distinctively worse at higher levels of psychopathy relative to lower levels, both as a function of primary and secondary psychopathy and particularly their shared variance. These novel findings provide support for the idea that motor control systematically varies by psychopathy, in a basic manner, consistent with views of psychopathy emphasizing lesser control. PMID:25419045

  15. Prediction of "fear" acquisition in healthy control participants in a de novo fear-conditioning paradigm.

    PubMed

    Otto, Michael W; Leyro, Teresa M; Christian, Kelly; Deveney, Christen M; Reese, Hannah; Pollack, Mark H; Orr, Scott P

    2007-01-01

    Studies using fear-conditioning paradigms have found that anxiety patients are more conditionable than individuals without these disorders, but these effects have been demonstrated inconsistently. It is unclear whether these findings have etiological significance or whether enhanced conditionability is linked only to certain anxiety characteristics. To further examine these issues, the authors assessed the predictive significance of relevant subsyndromal characteristics in 72 healthy adults, including measures of worry, avoidance, anxious mood, depressed mood, and fears of anxiety symptoms (anxiety sensitivity), as well as the dimensions of Neuroticism and Extraversion. Of these variables, the authors found that the combination of higher levels of subsyndromal worry and lower levels of behavioral avoidance predicted heightened conditionability, raising questions about the etiological significance of these variables in the acquisition or maintenance of anxiety disorders. In contrast, the authors found that anxiety sensitivity was more linked to individual differences in orienting response than differences in conditioning per se. PMID:17179530

  16. Predicting Adoption of Exposure Therapy in a Randomized Controlled Dissemination Trial

    PubMed Central

    Harned, Melanie S.; Dimeff, Linda A.; Woodcock, Eric A.; Contreras, Ignacio

    2013-01-01

    The present study examined organizational, client, and therapist characteristics as predictors of use of and proficiency in exposure therapy (ET) after training. Therapists naïve to ET (N=181) were randomized to: (1) online training (OLT), (2) OLT plus motivational enhancement (ME), or (3) OLT + ME plus a learning community. Twelve weeks after training, self-reported use of ET in clinical practice was high (87.5%) and therapists demonstrated moderate clinical proficiency. Use of ET was predicted by therapist degree, self-efficacy, and knowledge. Clinical proficiency was predicted by therapist anxiety sensitivity, attitudes, and knowledge, as well as organizational and client barriers. Several of these effects were moderated by training condition, indicating that therapists who received more comprehensive training were less impacted by barriers and showed enhanced adoption in the presence of facilitating factors. Overall, these results suggest that the primary barriers to the adoption of ET are therapist, not organizational or client, factors. PMID:23538148

  17. Clinical Evaluation of an Automated Artificial Pancreas Using Zone-Model Predictive Control and Health Monitoring System

    PubMed Central

    Harvey, Rebecca A.; Dassau, Eyal; Bevier, Wendy C.; Seborg, Dale E.; Jovanovič, Lois; Doyle, Francis J.

    2014-01-01

    Abstract Background: This study was performed to evaluate the safety and efficacy of a fully automated artificial pancreas using zone-model predictive control (zone-MPC) with the health monitoring system (HMS) during unannounced meals and overnight and exercise periods. Subjects and Methods: A fully automated closed-loop artificial pancreas was evaluated in 12 subjects (eight women, four men) with type 1 diabetes (mean±SD age, 49.4±10.4 years; diabetes duration, 32.7±16.0 years; glycosylated hemoglobin, 7.3±1.2%). The zone-MPC controller used an a priori model that was initialized using the subject's total daily insulin. The controller was designed to keep glucose levels between 80 and 140 mg/dL. A hypoglycemia prediction algorithm, a module of the HMS, was used in conjunction with the zone controller to alert the user to consume carbohydrates if the glucose level was predicted to fall below 70 mg/dL in the next 15 min. Results: The average time spent in the 70–180 mg/dL range, measured by the YSI glucose and lactate analyzer (Yellow Springs Instruments, Yellow Springs, OH), was 80% for the entire session, 92% overnight from 12 a.m. to 7 a.m., and 69% and 61% for the 5-h period after dinner and breakfast, respectively. The time spent <60 mg/dL for the entire session by YSI was 0%, with no safety events. The HMS sent appropriate warnings to prevent hypoglycemia via short and multimedia message services, at an average of 3.8 treatments per subject. Conclusions: The combination of the zone-MPC controller and the HMS hypoglycemia prevention algorithm was able to safely regulate glucose in a tight range with no adverse events despite the challenges of unannounced meals and moderate exercise. PMID:24471561

  18. Using a hybrid model to predict solute transfer from initially saturated soil into surface runoff with controlled drainage water.

    PubMed

    Tong, Juxiu; Hu, Bill X; Yang, Jinzhong; Zhu, Yan

    2016-06-01

    The mixing layer theory is not suitable for predicting solute transfer from initially saturated soil to surface runoff water under controlled drainage conditions. By coupling the mixing layer theory model with the numerical model Hydrus-1D, a hybrid solute transfer model has been proposed to predict soil solute transfer from an initially saturated soil into surface water, under controlled drainage water conditions. The model can also consider the increasing ponding water conditions on soil surface before surface runoff. The data of solute concentration in surface runoff and drainage water from a sand experiment is used as the reference experiment. The parameters for the water flow and solute transfer model and mixing layer depth under controlled drainage water condition are identified. Based on these identified parameters, the model is applied to another initially saturated sand experiment with constant and time-increasing mixing layer depth after surface runoff, under the controlled drainage water condition with lower drainage height at the bottom. The simulation results agree well with the observed data. Study results suggest that the hybrid model can accurately simulate the solute transfer from initially saturated soil into surface runoff under controlled drainage water condition. And it has been found that the prediction with increasing mixing layer depth is better than that with the constant one in the experiment with lower drainage condition. Since lower drainage condition and deeper ponded water depth result in later runoff start time, more solute sources in the mixing layer are needed for the surface water, and larger change rate results in the increasing mixing layer depth. PMID:26983916

  19. Using a hybrid model to predict solute transfer from initially saturated soil into surface runoff with controlled drainage water.

    PubMed

    Tong, Juxiu; Hu, Bill X; Yang, Jinzhong; Zhu, Yan

    2016-06-01

    The mixing layer theory is not suitable for predicting solute transfer from initially saturated soil to surface runoff water under controlled drainage conditions. By coupling the mixing layer theory model with the numerical model Hydrus-1D, a hybrid solute transfer model has been proposed to predict soil solute transfer from an initially saturated soil into surface water, under controlled drainage water conditions. The model can also consider the increasing ponding water conditions on soil surface before surface runoff. The data of solute concentration in surface runoff and drainage water from a sand experiment is used as the reference experiment. The parameters for the water flow and solute transfer model and mixing layer depth under controlled drainage water condition are identified. Based on these identified parameters, the model is applied to another initially saturated sand experiment with constant and time-increasing mixing layer depth after surface runoff, under the controlled drainage water condition with lower drainage height at the bottom. The simulation results agree well with the observed data. Study results suggest that the hybrid model can accurately simulate the solute transfer from initially saturated soil into surface runoff under controlled drainage water condition. And it has been found that the prediction with increasing mixing layer depth is better than that with the constant one in the experiment with lower drainage condition. Since lower drainage condition and deeper ponded water depth result in later runoff start time, more solute sources in the mixing layer are needed for the surface water, and larger change rate results in the increasing mixing layer depth.

  20. Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use

    PubMed Central

    Stewart, Jennifer L.; Zhang, Shunan; Tapert, Susan F.; Yu, Angela J.; Paulus, Martin P.

    2015-01-01

    Bayesian ideal observer models quantify individuals’ context- and experience-dependent beliefs and expectations about their environment, which provides a powerful approach (i) to link basic behavioural mechanisms to neural processing; and (ii) to generate clinical predictors for patient populations. Here, we focus on (ii) and determine whether individual differences in the neural representation of the need to stop in an inhibitory task can predict the development of problem use (i.e. abuse or dependence) in individuals experimenting with stimulants. One hundred and fifty-seven non-dependent occasional stimulant users, aged 18–24, completed a stop-signal task while undergoing functional magnetic resonance imaging. These individuals were prospectively followed for 3 years and evaluated for stimulant use and abuse/dependence symptoms. At follow-up, 38 occasional stimulant users met criteria for a stimulant use disorder (problem stimulant users), while 50 had discontinued use (desisted stimulant users). We found that those individuals who showed greater neural responses associated with Bayesian prediction errors, i.e. the difference between actual and expected need to stop on a given trial, in right medial prefrontal cortex/anterior cingulate cortex, caudate, anterior insula, and thalamus were more likely to exhibit problem use 3 years later. Importantly, these computationally based neural predictors outperformed clinical measures and non-model based neural variables in predicting clinical status. In conclusion, young adults who show exaggerated brain processing underlying whether to ‘stop’ or to ‘go’ are more likely to develop stimulant abuse. Thus, Bayesian cognitive models provide both a computational explanation and potential predictive biomarkers of belief processing deficits in individuals at risk for stimulant addiction. PMID:26336910

  1. Physical controls and predictability of stream hyporheic flow evaluated with a multiscale model

    USGS Publications Warehouse

    Stonedahl, Susa H.; Harvey, Judson W.; Detty, Joel; Aubeneau, Antoine; Packman, Aaron I.

    2012-01-01

    Improved predictions of hyporheic exchange based on easily measured physical variables are needed to improve assessment of solute transport and reaction processes in watersheds. Here we compare physically based model predictions for an Indiana stream with stream tracer results interpreted using the Transient Storage Model (TSM). We parameterized the physically based, Multiscale Model (MSM) of stream-groundwater interactions with measured stream planform and discharge, stream velocity, streambed hydraulic conductivity and porosity, and topography of the streambed at distinct spatial scales (i.e., ripple, bar, and reach scales). We predicted hyporheic exchange fluxes and hyporheic residence times using the MSM. A Continuous Time Random Walk (CTRW) model was used to convert the MSM output into predictions of in stream solute transport, which we compared with field observations and TSM parameters obtained by fitting solute transport data. MSM simulations indicated that surface-subsurface exchange through smaller topographic features such as ripples was much faster than exchange through larger topographic features such as bars. However, hyporheic exchange varies nonlinearly with groundwater discharge owing to interactions between flows induced at different topographic scales. MSM simulations showed that groundwater discharge significantly decreased both the volume of water entering the subsurface and the time it spent in the subsurface. The MSM also characterized longer timescales of exchange than were observed by the tracer-injection approach. The tracer data, and corresponding TSM fits, were limited by tracer measurement sensitivity and uncertainty in estimates of background tracer concentrations. Our results indicate that rates and patterns of hyporheic exchange are strongly influenced by a continuum of surface-subsurface hydrologic interactions over a wide range of spatial and temporal scales rather than discrete processes.

  2. Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use.

    PubMed

    Harlé, Katia M; Stewart, Jennifer L; Zhang, Shunan; Tapert, Susan F; Yu, Angela J; Paulus, Martin P

    2015-11-01

    Bayesian ideal observer models quantify individuals' context- and experience-dependent beliefs and expectations about their environment, which provides a powerful approach (i) to link basic behavioural mechanisms to neural processing; and (ii) to generate clinical predictors for patient populations. Here, we focus on (ii) and determine whether individual differences in the neural representation of the need to stop in an inhibitory task can predict the development of problem use (i.e. abuse or dependence) in individuals experimenting with stimulants. One hundred and fifty-seven non-dependent occasional stimulant users, aged 18-24, completed a stop-signal task while undergoing functional magnetic resonance imaging. These individuals were prospectively followed for 3 years and evaluated for stimulant use and abuse/dependence symptoms. At follow-up, 38 occasional stimulant users met criteria for a stimulant use disorder (problem stimulant users), while 50 had discontinued use (desisted stimulant users). We found that those individuals who showed greater neural responses associated with Bayesian prediction errors, i.e. the difference between actual and expected need to stop on a given trial, in right medial prefrontal cortex/anterior cingulate cortex, caudate, anterior insula, and thalamus were more likely to exhibit problem use 3 years later. Importantly, these computationally based neural predictors outperformed clinical measures and non-model based neural variables in predicting clinical status. In conclusion, young adults who show exaggerated brain processing underlying whether to 'stop' or to 'go' are more likely to develop stimulant abuse. Thus, Bayesian cognitive models provide both a computational explanation and potential predictive biomarkers of belief processing deficits in individuals at risk for stimulant addiction. PMID:26336910

  3. Separate and interactive contributions of weak inhibitory control and threat sensitivity to prediction of suicide risk

    PubMed Central

    Venables, Noah C.; Sellbom, Martin; Sourander, Andre; Kendler, Kenneth S.; Joiner, Thomas E.; Drislane, Laura E.; Sillanmäki, Lauri; Elonheimo, Henrik; Parkkola, Kai; Multimaki, Petteri; Patrick, Christopher J.

    2015-01-01

    Biobehavioral dispositions can serve as valuable referents for biologically-oriented research on core processes with relevance to many psychiatric conditions. The present study examined two such dispositional variables—weak response inhibition (or disinhibition; INH−) and threat sensitivity (or fearfulness; THT+)—as predictors of the serious transdiagnostic problem of suicide risk in two samples: male and female outpatients from a U.S. clinic (N=1,078), and a population-based male military cohort from Finland (N=3,855). INH− and THT+ were operationalized through scores on scale measures of disinhibition and fear/fearlessness, known to be related to DSM-defined clinical conditions and brain biomarkers. Suicide risk was assessed by clinician ratings (clinic sample) and questionnaires (both samples). Across samples and alternative suicide indices, INH− and THT+ each contributed uniquely to prediction of suicide risk—beyond internalizing and externalizing problems in the case of the clinic sample where diagnostic data were available. Further, in both samples, INH− and THT+ interactively predicted suicide risk, with individuals scoring concurrently high on both dispositions exhibiting markedly augmented risk. Findings demonstrate that dispositional constructs of INH− and THT+ are predictive of suicide risk, and hold potential as referents for biological research on suicidal behavior. PMID:25712631

  4. Predicting Disease Risk, Identifying Stakeholders, and Informing Control Strategies: A Case Study of Anthrax in Montana.

    PubMed

    Morris, Lillian R; Blackburn, Jason K

    2016-06-01

    Infectious diseases that affect wildlife and livestock are challenging to manage and can lead to large-scale die-offs, economic losses, and threats to human health. The management of infectious diseases in wildlife and livestock is made easier with knowledge of disease risk across space and identifying stakeholders associated with high-risk landscapes. This study focuses on anthrax, caused by the bacterium Bacillus anthracis, risk to wildlife and livestock in Montana. There is a history of anthrax in Montana, but the spatial extent of disease risk and subsequent wildlife species at risk are not known. Our objective was to predict the potential geographic distribution of anthrax risk across Montana, identify wildlife species at risk and their distributions, and define stakeholders. We used an ecological niche model to predict the potential distribution of anthrax risk. We overlaid susceptible wildlife species distributions and land ownership delineations on our risk map. We found that there was an extensive region across Montana predicted as potential anthrax risk. These potentially risky landscapes overlapped the ranges of all 6 ungulate species considered in the analysis and livestock grazing allotments, and this overlap was on public and private land for all species. Our findings suggest that there is the potential for a multi-species anthrax outbreak on multiple landscapes across Montana. Our potential anthrax risk map can be used to prioritize landscapes for surveillance and for implementing livestock vaccination programs. PMID:27169560

  5. Separate and interactive contributions of weak inhibitory control and threat sensitivity to prediction of suicide risk.

    PubMed

    Venables, Noah C; Sellbom, Martin; Sourander, Andre; Kendler, Kenneth S; Joiner, Thomas E; Drislane, Laura E; Sillanmäki, Lauri; Elonheimo, Henrik; Parkkola, Kai; Multimaki, Petteri; Patrick, Christopher J

    2015-04-30

    Biobehavioral dispositions can serve as valuable referents for biologically oriented research on core processes with relevance to many psychiatric conditions. The present study examined two such dispositional variables-weak response inhibition (or disinhibition; INH-) and threat sensitivity (or fearfulness; THT+)-as predictors of the serious transdiagnostic problem of suicide risk in two samples: male and female outpatients from a U.S. clinic (N=1078), and a population-based male military cohort from Finland (N=3855). INH- and THT+ were operationalized through scores on scale measures of disinhibition and fear/fearlessness, known to be related to DSM-defined clinical conditions and brain biomarkers. Suicide risk was assessed by clinician ratings (clinic sample) and questionnaires (both samples). Across samples and alternative suicide indices, INH- and THT+ each contributed uniquely to prediction of suicide risk-beyond internalizing and externalizing problems in the case of the clinic sample where diagnostic data were available. Further, in both samples, INH- and THT+ interactively predicted suicide risk, with individuals scoring concurrently high on both dispositions exhibiting markedly augmented risk. Findings demonstrate that dispositional constructs of INH- and THT+ are predictive of suicide risk, and hold potential as referents for biological research on suicidal behavior. PMID:25712631

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

    PubMed

    Salehifar, Mehdi; Moreno-Equilaz, Manuel

    2016-01-01

    Due to its fault tolerance, a multiphase brushless direct current (BLDC) motor can meet high reliability demand for application in electric vehicles. The voltage-source inverter (VSI) supplying the motor is subjected to open circuit faults. Therefore, it is necessary to design a fault-tolerant (FT) control algorithm with an embedded fault diagnosis (FD) block. In this paper, finite control set-model predictive control (FCS-MPC) is developed to implement the fault-tolerant control algorithm of a five-phase BLDC motor. The developed control method is fast, simple, and flexible. A FD method based on available information from the control block is proposed; this method is simple, robust to common transients in motor and able to localize multiple open circuit faults. The proposed FD and FT control algorithm are embedded in a five-phase BLDC motor drive. In order to validate the theory presented, simulation and experimental results are conducted on a five-phase two-level VSI supplying a five-phase BLDC motor. PMID:26549566

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

    PubMed

    Salehifar, Mehdi; Moreno-Equilaz, Manuel

    2016-01-01

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

  8. Linear quadratic game and non-cooperative predictive methods for potential application to modelling driver-AFS interactive steering control

    NASA Astrophysics Data System (ADS)

    Na, Xiaoxiang; Cole, David J.

    2013-02-01

    This paper is concerned with the modelling of strategic interactions between the human driver and the vehicle active front steering (AFS) controller in a path-following task where the two controllers hold different target paths. The work is aimed at extending the use of mathematical models in representing driver steering behaviour in complicated driving situations. Two game theoretic approaches, namely linear quadratic game and non-cooperative model predictive control (non-cooperative MPC), are used for developing the driver-AFS interactive steering control model. For each approach, the open-loop Nash steering control solution is derived; the influences of the path-following weights, preview and control horizons, driver time delay and arm neuromuscular system (NMS) dynamics are investigated, and the CPU time consumed is recorded. It is found that the two approaches give identical time histories as well as control gains, while the non-cooperative MPC method uses much less CPU time. Specifically, it is observed that the introduction of weight on the integral of vehicle lateral displacement error helps to eliminate the steady-state path-following error; the increase in preview horizon and NMS natural frequency and the decline in time delay and NMS damping ratio improve the path-following accuracy.

  9. What predicts intention-behavior discordance? A review of the action control framework.

    PubMed

    Rhodes, Ryan E; de Bruijn, Gert-Jan

    2013-10-01

    The physical activity intention-behavior gap is a focus of considerable research. The purpose of this article is to overview contemporary evidence for predictors of this intention-behavior discordance using the action control framework developed in our laboratories. We propose the hypothesis that intention-behavior discordance is from motivational (affective attitude, perceived behavioral control), self-regulatory (behavioral processes), and habitual (automaticity) constructs. PMID:23873134

  10. Evaluation of model predictive control in run-to-run processing in semiconductor manufacturing

    NASA Astrophysics Data System (ADS)

    Mullins, James A.; Campbell, W. J.; Stock, Allen D.

    1997-08-01

    Many steps in the manufacturing of semiconductors offer no opportunity for real-time measurement of the wafer state, necessitating the use of pre- and post-process measurements of the wafer state in a run-to-run control algorithm. The predominant algorithm in the industry is an extended form of SPC using an EWMA filter to adjust a model parameter vector using the available measurements. This paper evaluates the merits of using an optimal discrete controller relying on a discrete-time constrained state-space process model that incorporates feedforward action using the pre-process measurement and feedback using the post-process measurement, accounts for the process statistics using a noise model and optimal filtering theory, and ensures integral action in the controller by estimating unmeasured disturbances. Comparison to the EWMA algorithm are presented using simulations based on actual plant data from a chemical-mechanical polishing application. The polish process is particularly suitable for the application of such a controller because of the natural method the controller provides for incorporating unmeasured disturbances, like pad and slurry changes, in the control action.

  11. Prediction of forces and moments for flight vehicle control effectors. Part 2: An analysis of delta wing aerodynamic control effectiveness in ground effect

    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. Here, an investigation of the aerodynamic control effectiveness of highly swept delta planforms operating in ground effect is presented. A vortex-lattice computer program incorporating a free wake is developed as a tool to calculate aerodynamic stability and control derivatives. Data generated using this program are compared to experimental data and to data from other vortex-lattice programs. Results show that an elevon deflection produces greater increments in C sub L and C sub M in ground effect than the same deflection produces out of ground effect and that the free wake is indeed necessary for good predictions near the ground.

  12. Casting dimensional control and fatigue life prediction for permanent mold casting dies. Technical report, September 29, 1993--May 31, 1997

    SciTech Connect

    1997-06-01

    Efforts as part of a three year program to address metal casting dimensional control and fatigue life prediction for permanent mold casting dies are described. Procedures have been developed and implemented to collect dimensional variability data from production steel casting. The influence of process variation and casting geometry variables on dimensional tolerances have been investigated. Also efforts leading to the developments and validation of a CAD/CAE model to predict the thermal fatigue life of permanent molds for aluminum castings are described. An appropriate thermomechanical property database for metal, mold and coating materials has been constructed. A finite element model has been developed to simulate the mold temperature distribution during repeated casting cycles. Validation trials using a permanent mold casting machine have indicated the success of the temperature distribution model developed. A combination of experimental and modeling techniques have been employed to extend their knowledge of permanent mold casting. The influence of coatings on casting solidification and mold temperatures has been determined. The computer model has been extended to predict thermally induced stresses and strains in the mold and to predict the number of cycles required to crack the mold. Experimental results have been used to validate the extended model.

  13. Prediction of the Wrist Joint Position During a Postural Tremor Using Neural Oscillators and an Adaptive Controller.

    PubMed

    Kobravi, Hamid Reza; Ali, Sara Hemmati; Vatandoust, Masood; Marvi, Rasoul

    2016-01-01

    The prediction of the joint angle position, especially during tremor bursts, can be useful for detecting, tracking, and forecasting tremors. Thus, this research proposes a new model for predicting the wrist joint position during rhythmic bursts and inter-burst intervals. Since a tremor is an approximately rhythmic and roughly sinusoidal movement, neural oscillators have been selected to underlie the proposed model. Two neural oscillators were adopted. Electromyogram (EMG) signals were recorded from the extensor carpi radialis and flexor carpi radialis muscles concurrent with the joint angle signals of a stroke subject in an arm constant-posture. The output frequency of each oscillator was equal to the frequency corresponding to the maximum value of power spectrum related to the rhythmic wrist joint angle signals which had been recorded during a postural tremor. The phase shift between the outputs of the two oscillators was equal to the phase shift between the muscle activation of the wrist flexor and extensor muscles. The difference between the two oscillators' output signals was considered the main pattern. Along with a proportional compensator, an adaptive neural controller has adjusted the amplitude of the main pattern in such a way so as to minimize the wrist joint prediction error during a stroke patient's tremor burst and a healthy subject's generated artificial tremor. In regard to the range of wrist joint movement during the observed rhythmic motions, a calculated prediction error is deemed acceptable. PMID:27186540

  14. Prediction of the Wrist Joint Position During a Postural Tremor Using Neural Oscillators and an Adaptive Controller

    PubMed Central

    Kobravi, Hamid Reza; Ali, Sara Hemmati; Vatandoust, Masood; Marvi, Rasoul

    2016-01-01

    The prediction of the joint angle position, especially during tremor bursts, can be useful for detecting, tracking, and forecasting tremors. Thus, this research proposes a new model for predicting the wrist joint position during rhythmic bursts and inter-burst intervals. Since a tremor is an approximately rhythmic and roughly sinusoidal movement, neural oscillators have been selected to underlie the proposed model. Two neural oscillators were adopted. Electromyogram (EMG) signals were recorded from the extensor carpi radialis and flexor carpi radialis muscles concurrent with the joint angle signals of a stroke subject in an arm constant-posture. The output frequency of each oscillator was equal to the frequency corresponding to the maximum value of power spectrum related to the rhythmic wrist joint angle signals which had been recorded during a postural tremor. The phase shift between the outputs of the two oscillators was equal to the phase shift between the muscle activation of the wrist flexor and extensor muscles. The difference between the two oscillators’ output signals was considered the main pattern. Along with a proportional compensator, an adaptive neural controller has adjusted the amplitude of the main pattern in such a way so as to minimize the wrist joint prediction error during a stroke patient's tremor burst and a healthy subject's generated artificial tremor. In regard to the range of wrist joint movement during the observed rhythmic motions, a calculated prediction error is deemed acceptable. PMID:27186540

  15. An efficient feedback active noise control algorithm based on reduced-order linear predictive modeling of FMRI acoustic noise.

    PubMed

    Kannan, Govind; Milani, Ali A; Panahi, Issa M S; Briggs, Richard W

    2011-12-01

    Functional magnetic resonance imaging (fMRI) acoustic noise exhibits an almost periodic nature (quasi-periodicity) due to the repetitive nature of currents in the gradient coils. Small changes occur in the waveform in consecutive periods due to the background noise and slow drifts in the electroacoustic transfer functions that map the gradient coil waveforms to the measured acoustic waveforms. The period depends on the number of slices per second, when echo planar imaging (EPI) sequencing is used. Linear predictability of fMRI acoustic noise has a direct effect on the performance of active noise control (ANC) systems targeted to cancel the acoustic noise. It is shown that by incorporating some samples from the previous period, very high linear prediction accuracy can be reached with a very low order predictor. This has direct implications on feedback ANC systems since their performance is governed by the predictability of the acoustic noise to be cancelled. The low complexity linear prediction of fMRI acoustic noise developed in this paper is used to derive an effective and low-cost feedback ANC system.

  16. Piloted Simulation Evaluation of a Model-Predictive Automatic Recovery System to Prevent Vehicle Loss of Control on Approach

    NASA Technical Reports Server (NTRS)

    Litt, Jonathan S.; Liu, Yuan; Sowers, Thomas S.; Owen, A. Karl; Guo, Ten-Huei

    2014-01-01

    This paper describes a model-predictive automatic recovery system for aircraft on the verge of a loss-of-control situation. The system determines when it must intervene to prevent an imminent accident, resulting from a poor approach. It estimates the altitude loss that would result from a go-around maneuver at the current flight condition. If the loss is projected to violate a minimum altitude threshold, the maneuver is automatically triggered. The system deactivates to allow landing once several criteria are met. Piloted flight simulator evaluation showed the system to provide effective envelope protection during extremely unsafe landing attempts. The results demonstrate how flight and propulsion control can be integrated to recover control of the vehicle automatically and prevent a potential catastrophe.

  17. Using an Informative Missing Data Model to Predict the Ability to Assess Recovery of Balance Control after Spaceflight

    NASA Technical Reports Server (NTRS)

    Feiveson, Alan H.; Wood, Scott J.; Jain, Varsha

    2008-01-01

    Astronauts show degraded balance control immediately after spaceflight. To assess this change, astronauts' ability to maintain a fixed stance under several challenging stimuli on a movable platform is quantified by "equilibrium" scores (EQs) on a scale of 0 to 100, where 100 represents perfect control (sway angle of 0) and 0 represents data loss where no sway angle is observed because the subject has to be restrained from falling. By comparing post- to pre-flight EQs for actual astronauts vs. controls, we built a classifier for deciding when an astronaut has recovered. Future diagnostic performance depends both on the sampling distribution of the classifier as well as the distribution of its input data. Taking this into consideration, we constructed a predictive ROC by simulation after modeling P(EQ = 0) in terms of a latent EQ-like beta-distributed random variable with random effects.

  18. Lagrangian and Control Volume Models for Prediction of Cooling Lake Performance at SRP

    SciTech Connect

    Garrett, A.J.

    2001-06-26

    The model validation described in this document indicates that the methods described here and by Cooper (1984) for predicting the performance of the proposed L-Area cooling lake are reliable. Extensive observations from the Par Pond system show that lake surface temperatures exceeding 32.2 degrees C (90 degrees F) are attained occasionally in the summer in areas where there is little or no heating from the P-Area Reactor. Regulations which restrict lake surface temperatures to less than 32.2 degrees C should be structured to allow for these naturally-occurring thermal excursions.

  19. A model-based approach to predict muscle synergies using optimization: application to feedback control

    PubMed Central

    Sharif Razavian, Reza; Mehrabi, Naser; McPhee, John

    2015-01-01

    This paper presents a new model-based method to define muscle synergies. Unlike the conventional factorization approach, which extracts synergies from electromyographic data, the proposed method employs a biomechanical model and formally defines the synergies as the solution of an optimal control problem. As a result, the number of required synergies is directly related to the dimensions of the operational space. The estimated synergies are posture-dependent, which correlate well with the results of standard factorization methods. Two examples are used to showcase this method: a two-dimensional forearm model, and a three-dimensional driver arm model. It has been shown here that the synergies need to be task-specific (i.e., they are defined for the specific operational spaces: the elbow angle and the steering wheel angle in the two systems). This functional definition of synergies results in a low-dimensional control space, in which every force in the operational space is accurately created by a unique combination of synergies. As such, there is no need for extra criteria (e.g., minimizing effort) in the process of motion control. This approach is motivated by the need for fast and bio-plausible feedback control of musculoskeletal systems, and can have important implications in engineering, motor control, and biomechanics. PMID:26500530

  20. Prediction of the spatial evolution and effects of control measures for the unfolding Haiti cholera outbreak

    NASA Astrophysics Data System (ADS)

    Bertuzzo, E.; Mari, L.; Righetto, L.; Gatto, M.; Casagrandi, R.; Blokesch, M.; Rodriguez-Iturbe, I.; Rinaldo, A.

    2011-03-01

    Here we propose spatially explicit predictions of the residual progression of the current Haiti cholera outbreak accounting for the dynamics of susceptible and infected individuals within different local human communities, and for the redistribution among them of Vibrio cholerae, the causative agent of the disease. Spreading mechanisms include the diffusion of pathogens in the aquatic environment and their dissemination due to the movement of human carriers. The model reproduces the spatiotemporal features of the outbreak to date, thus suggesting the robustness of predicted future developments of the epidemic. We estimate that, under unchanged conditions, the number of new cases in the whole country should start to decrease in January. During this month the epidemic should mainly involve the Ouest department (Port-au-Prince) while fading out in northern regions. Our spatially explicit model allows also the analysis of the effectiveness of alternative intervention strategies. To that end our results show that mass vaccinations would have a negligible impact at this stage of the epidemic. We also show that targeted sanitation strategies, providing clean drinking water supply and/or staging educational campaigns aimed at reducing exposure, may weaken the strength of the residual evolution of the infection.

  1. Sorbent utilization prediction methodology: sulfur control in fluidized-bed combustors

    SciTech Connect

    Fee, D.C.; Wilson, W.I.; Shearer, J.A.; Smith, G.W.; Lenc, J.F.; Fan, L.S.; Myles, K.M.; Johnson, I.

    1980-09-01

    The United States Government has embarked on an ambitious program to develop and commercialize technologies to efficiently extract energy from coal in an environmentally acceptable manner. One of the more promising new technologies for steam and power generation is the fluidized-bed combustion of coal. In this process, coal is burned in a fluidized bed composed mainly of calcined limestone sorbent. The calcium oxide reacts chemically to capture the sulfur dioxide formed during the combustion and to maintain the stack gas sulfur emissions at acceptable levels. The spent sulfur sorbent, containing calcium sulfate, is a dry solid that can be disposed of along with coal ash or potentially used. Other major advantages of fluidized-bed combustion are the reduction in nitrogen oxide emissions because of the relatively low combustion temperatures, the capability of burning wide varieties of fuel, the high carbon combustion efficiencies, and the high heat-transfer coefficients. A key to the widespread commercialization of fluidized-bed technology is the ability to accurately predict the amount of sulfur that will be captured by a given sorbent. This handbook meets this need by providing a simple, yet reliable, user-oriented methodology (the ANL method) that allows performance of a sorbent to be predicted. The methodology is based on only three essential sorbent parameters, each of which can be readily obtained from standardized laboratory tests. These standard tests and the subsequent method of data reduction are described in detail.

  2. Final Scientific Technical Report: INTEGRATED PREDICTIVE DEMAND RESPONSE CONTROLLER FOR COMMERCIAL BUILDINGS

    SciTech Connect

    Wenzel, Mike

    2013-10-14

    This project provides algorithms to perform demand response using the thermal mass of a building. Using the thermal mass of the building is an attractive method for performing demand response because there is no need for capital expenditure. The algorithms rely on the thermal capacitance inherent in the building?s construction materials. A near-optimal ?day ahead? predictive approach is developed that is meant to keep the building?s electrical demand constant during the high cost periods. This type of approach is appropriate for both time-of-use and critical peak pricing utility rate structures. The approach uses the past days data in order to determine the best temperature setpoints for the building during the high price periods on the next day. A second ?model predictive approach? (MPC) uses a thermal model of the building to determine the best temperature for the next sample period. The approach uses constant feedback from the building and is capable of appropriately handling real time pricing. Both approaches are capable of using weather forecasts to improve performance.

  3. Morphoelastic control of gastro-intestinal organogenesis: Theoretical predictions and numerical insights

    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.

  4. Predicting Ares I Reaction Control System Performance by Utilizing Analysis Anchored with Development Test Data

    NASA Technical Reports Server (NTRS)

    Stein, William B.; Holt, K.; Holton, M.; Williams, J. H.; Butt, A.; Dervan, M.; Sharp, D.

    2010-01-01

    The Ares I launch vehicle is an integral part of NASA s Constellation Program, providing a foundation for a new era of space access. The Ares I is designed to lift the Orion Crew Module and will enable humans to return to the Moon as well as explore Mars.1 The Ares I is comprised of two inline stages: a Space Shuttle-derived five-segment Solid Rocket Booster (SRB) First Stage (FS) and an Upper Stage (US) powered by a Saturn V-derived J-2X engine. A dedicated Roll Control System (RoCS) located on the connecting interstage provides roll control prior to FS separation. Induced yaw and pitch moments are handled by the SRB nozzle vectoring. The FS SRB operates for approximately two minutes after which the US separates from the vehicle and the US Reaction Control System (ReCS) continues to provide reaction control for the remainder of the mission. A representation of the Ares I launch vehicle in the stacked configuration and including the Orion Crew Exploration Vehicle (CEV) is shown in Figure 1. Each Reaction Control System (RCS) design incorporates a Gaseous Helium (GHe) pressurization system combined with a monopropellant Hydrazine (N2H4) propulsion system. Both systems have two diametrically opposed thruster modules. This architecture provides one failure tolerance for function and prevention of catastrophic hazards such as inadvertent thruster firing, bulk propellant leakage, and over-pressurization. The pressurization system on the RoCS includes two ambient pressure-referenced regulators on parallel strings in order to attain the required system level single Fault Tolerant (FT) design for function while the ReCS utilizes a blow-down approach. A single burst disk and relief valve assembly is also included on the RoCS to ensure single failure tolerance for must-not-occur catastrophic hazards. The Reaction Control Systems are designed to support simultaneously firing multiple thrusters as required

  5. Lower Hippocampal Volume Predicts Decrements in Lane Control among Drivers with Amnestic MCI

    PubMed Central

    Griffith, H Randall; Okonkwo, Ozioma C; Stewart, Christopher C; Stoeckel, Luke E; den Hollander, Jan A; Elgin, Jennifer M; Harrell, Lindy E; Brockington, John C; Clark, David G; Ball, Karlene K; Owsley, Cynthia; Marson, Daniel C; Wadley, Virginia G

    2014-01-01

    Objectives There are few methods to discern driving risks in patients with early dementia and Mild Cognitive Impairment (MCI). We aimed to determine whether structural MRI of the hippocampus – a biomarker of probable Alzheimer pathology and a measure of disease severity in those affected – is linked to objective ratings of on-road driving performance in older adults with and without amnestic MCI. Methods 49 consensus-diagnosed participants from an Alzheimer's Disease Research Center (15 diagnosed with amnestic MCI and 34 demographically similar controls) underwent structural MRI and on-road driving assessments. Results Mild atrophy of the left hippocampus was associated with less-than-optimal ratings in lane control but not with other discrete driving skills. Decrements in left hippocampal volume conferred higher risk for less-than-optimal lane control ratings in the MCI patients (B = −1.63, SE = .74, Wald = 4.85, P = .028), but not in controls (B = 0.13, SE = .415, Wald = 0.10, P = .752). The odds ratio (OR) and 95% confidence interval (CI) for below optimal lane control in the MCI group was 4.41 (1.18, 16.36), which was attenuated to 3.46 (0.88, 13.60) after accounting for the contribution of left hippocampal volume. Conclusion These findings suggest that there may be a link between hippocampal atrophy and difficulties with lane control in persons with amnestic MCI. Further study appears warranted to better discern patterns of brain atrophy in MCI and AD and whether these could be early markers of clinically meaningful driving risk. PMID:24212246

  6. Developing principles for predicting ionic liquid effects on reaction outcome. The importance of the anion in controlling microscopic interactions.

    PubMed

    Keaveney, Sinead T; Haines, Ronald S; Harper, Jason B

    2015-03-28

    A series of ionic liquids containing anions of differing coordination strength were investigated as solvents for the condensation reaction of an alkyl amine and an aromatic aldehyde. As predicted, the rate constant of the process was found to increase with the proportion of the ionic liquid in the reaction mixture. Temperature-dependent kinetic analyses demonstrated that by varying the ability of the anion to interact with the cation the magnitude of both the enthalpy and entropy of activation could be controlled in a predictable manner, with the activation parameters being linearly dependent on the ionic liquid basicity. Interestingly, the unexpected trend in the rate constants observed when altering the anion of the ionic liquid highlighted the presence of more subtle secondary microscopic interactions involving the anion, further emphasizing the fragility of the enthalpy - entropy balance.

  7. Prediction of physical aging in controlled-release coatings: the application of the relaxation coupling model to glassy cellulose acetate.

    PubMed

    Sinko, C M; Yee, A F; Amidon, G L

    1991-06-01

    The effect of physical aging on both the water transport properties and the mechanical properties of glassy cellulose acetate was investigated. Results indicate a reduction in the mechanical rate of relaxation as well as a reduction in the water permeability as the glass ages. A model which describes the low-frequency relaxation behavior of condensed, amorphous systems is used to quantitate the mechanical relaxation data. Systematic changes in key parameters from this model signify alterations in the microscopic or short-range structure as the glass physically ages. Predictions from this model correlate quite closely with the observed water permeability reductions and thus indicate that the transport properties of glassy polymers are dependent on the structure of the glass. This approach may provide further insight into the effects of nonequilibrium behavior on pharmaceutically important properties and may serve as a basis for predicting aging and permeability changes in controlled-release dosage forms.

  8. Optimal control model predictions of system performance and attention allocation and their experimental validation in a display design study

    NASA Technical Reports Server (NTRS)

    Johannsen, G.; Govindaraj, T.

    1980-01-01

    The influence of different types of predictor displays in a longitudinal vertical takeoff and landing (VTOL) hover task is analyzed in a theoretical study. Several cases with differing amounts of predictive and rate information are compared. The optimal control model of the human operator is used to estimate human and system performance in terms of root-mean-square (rms) values and to compute optimized attention allocation. The only part of the model which is varied to predict these data is the observation matrix. Typical cases are selected for a subsequent experimental validation. The rms values as well as eye-movement data are recorded. The results agree favorably with those of the theoretical study in terms of relative differences. Better matching is achieved by revised model input data.

  9. Changes in frontal EEG coherence across infancy predict cognitive abilities at age 3: The mediating role of attentional control.

    PubMed

    Whedon, Margaret; Perry, Nicole B; Calkins, Susan D; Bell, Martha Ann

    2016-09-01

    Theoretical perspectives of cognitive development have maintained that functional integration of the prefrontal cortex across infancy underlies the emergence of attentional control and higher cognitive abilities in early childhood. To investigate these proposed relations, we tested whether functional integration of prefrontal regions across the second half of the first year predicted observed cognitive performance in early childhood 1 year prior indirectly through observed attentional control (N = 300). Results indicated that greater change in left-but not right-frontal EEG coherence between 5 and 10 months was positively associated with attentional control, cognitive flexibility, receptive language, and behavioral inhibitory control. Specifically, a larger increase in coherence between left frontal regions was positively associated with accuracy on a visual search task at Age 2, and visual search accuracy was positively associated with receptive vocabulary, performance on a set-shifting task (DCCS), and delay of gratification at Age 3. Finally, the indirect effects from the change in left frontal EEG coherence to 3-year cognitive flexibility, receptive language, and behavioral inhibitory control were significant, suggesting that internally controlled attention is a mechanism through which early neural maturation influences children's cognitive development. (PsycINFO Database Record PMID:27441486

  10. Catalytic Control in Cyclizations: From Computational Mechanistic Understanding to Selectivity Prediction.

    PubMed

    Peng, Qian; Paton, Robert S

    2016-05-17

    This Account describes the use of quantum-chemical calculations to elucidate mechanisms and develop catalysts to accomplish highly selective cyclization reactions. Chemistry is awash with cyclic molecules, and the creation of rings is central to organic synthesis. Cyclization reactions, the formation of rings by the reaction of two ends of a linear precursor, have been instrumental in the development of predictive models for chemical reactivity, from Baldwin's classification and rules for ring closure to the Woodward and Hoffmann rules based on the conservation of orbital symmetry and beyond. Ring formation provides a productive and fertile testing ground for the exploration of catalytic mechanisms and chemo-, regio-, diastereo-, and enantioselectivity using computational and experimental approaches. This Account is organized around case studies from our laboratory and illustrates the ways in which computations provide a deeper understanding of the mechanisms of catalysis in 5-endo cyclizations and how computational predictions can lead to the development of new catalysts for enhanced stereoselectivities in asymmetric cycloisomerizations. We have explored the extent to which several cation-directed 5-endo ring-closing reactions may be considered as electrocyclic and demonstrated that reaction pathways and magnetic parameters of transition structures computed using quantum chemistry are inconsistent with this notion, instead favoring a polar mechanism. A rare example of selectivity in favor of 5-endo-trig ring closure is shown to result from subtle substrate effects that bias the reactant conformation out-of-plane, limiting the involvement of cyclic conjugation. The mode of action of a chiral ammonium counterion was deduced via conformational sampling of the transition state assembly and involves coordination to the substrate via a series of nonclassical hydrogen bonds. We describe how computational mechanistic understanding has led directly to the discovery of new

  11. Controlling misses and false alarms in a machine learning framework for predicting uniformity of printed pages

    NASA Astrophysics Data System (ADS)

    Nguyen, Minh Q.; Allebach, Jan P.

    2015-01-01

    In our previous work1 , we presented a block-based technique to analyze printed page uniformity both visually and metrically. The features learned from the models were then employed in a Support Vector Machine (SVM) framework to classify the pages into one of the two categories of acceptable and unacceptable quality. In this paper, we introduce a set of tools for machine learning in the assessment of printed page uniformity. This work is primarily targeted to the printing industry, specifically the ubiquitous laser, electrophotographic printer. We use features that are well-correlated with the rankings of expert observers to develop a novel machine learning framework that allows one to achieve the minimum "false alarm" rate, subject to a chosen "miss" rate. Surprisingly, most of the research that has been conducted on machine learning does not consider this framework. During the process of developing a new product, test engineers will print hundreds of test pages, which can be scanned and then analyzed by an autonomous algorithm. Among these pages, most may be of acceptable quality. The objective is to find the ones that are not. These will provide critically important information to systems designers, regarding issues that need to be addressed in improving the printer design. A "miss" is defined to be a page that is not of acceptable quality to an expert observer that the prediction algorithm declares to be a "pass". Misses are a serious problem, since they represent problems that will not be seen by the systems designers. On the other hand, "false alarms" correspond to pages that an expert observer would declare to be of acceptable quality, but which are flagged by the prediction algorithm as "fails". In a typical printer testing and development scenario, such pages would be examined by an expert, and found to be of acceptable quality after all. "False alarm" pages result in extra pages to be examined by expert observers, which increases labor cost. But "false

  12. Predicting the secondary dynamic mode interference phenomenon in thermoacoustic instability control

    NASA Astrophysics Data System (ADS)

    Zalluhoglu, Umut; Olgac, Nejat

    2016-07-01

    This paper brings a novel mathematical perspective in assessing the rise of the secondary dynamic modes to prominence during the suppression of thermoacoustic instability. This phenomenon is observed by many earlier investigators; however, without a complete analytical reasoning. We consider a Rijke tube with both a passive Helmholtz resonator and an active feedback control to suppress instabilities. The core dynamics is represented as a linear time-invariant multiple time-delay system of neutral type. Parametric stability of the resulting infinite-dimensional dynamics is investigated using a recent analytical tool: cluster treatment of characteristic roots paradigm. This tool reveals the stability outlook of such systems exhaustively and non-conservatively in the parameter space of the system. First, we examine the stability with and without the Helmholtz resonator. We then select an unstable operation for the resonator-mounted Rijke tube, impose a time-delayed integral feedback control over it and reveal the stabilizing controller parameters using the cluster treatment of characteristic roots methodology. When high control gains are inappropriately selected, the new analytical procedure declares how the secondary dynamic modes of the system exhibit instability although the initially unstable mode is now stabilized. All of these stability assessments are cross-validated using experimental results from a laboratory-scale Rijke tube set-up.

  13. Predicting Health Care Utilization among Latinos: Health Locus of Control Beliefs or Access Factors?

    ERIC Educational Resources Information Center

    De Jesus, Maria; Xiao, Chenyang

    2014-01-01

    There are two competing research explanations to account for Latinos' underutilization of health services relative to non-Latino Whites in the United States. One hypothesis examines the impact of health locus of control (HLOC) beliefs, while the other focuses on the role of access factors on health care use. To date, the relative strength of…

  14. Frontostriatal Maturation Predicts Cognitive Control Failure to Appetitive Cues in Adolescents

    ERIC Educational Resources Information Center

    Somerville, Leah H.; Hare, Todd; Casey, B. J.

    2011-01-01

    Adolescent risk-taking is a public health issue that increases the odds of poor lifetime outcomes. One factor thought to influence adolescents' propensity for risk-taking is an enhanced sensitivity to appetitive cues, relative to an immature capacity to exert sufficient cognitive control. We tested this hypothesis by characterizing interactions…

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

    ERIC Educational Resources Information Center

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

    2013-01-01

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

  16. Frontostriatal maturation predicts cognitive control failure to appetitive cues in adolescents.

    PubMed

    Somerville, Leah H; Hare, Todd; Casey, B J

    2011-09-01

    Adolescent risk-taking is a public health issue that increases the odds of poor lifetime outcomes. One factor thought to influence adolescents' propensity for risk-taking is an enhanced sensitivity to appetitive cues, relative to an immature capacity to exert sufficient cognitive control. We tested this hypothesis by characterizing interactions among ventral striatal, dorsal striatal, and prefrontal cortical regions with varying appetitive load using fMRI scanning. Child, teen, and adult participants performed a go/no-go task with appetitive (happy faces) and neutral cues (calm faces). Impulse control to neutral cues showed linear improvement with age, whereas teens showed a nonlinear reduction in impulse control to appetitive cues. This performance decrement in teens was paralleled by enhanced activity in the ventral striatum. Prefrontal cortical recruitment correlated with overall accuracy and showed a linear response with age for no-go versus go trials. Connectivity analyses identified a ventral frontostriatal circuit including the inferior frontal gyrus and dorsal striatum during no-go versus go trials. Examining recruitment developmentally showed that teens had greater between-subject ventral-dorsal striatal coactivation relative to children and adults for happy no-go versus go trials. These findings implicate exaggerated ventral striatal representation of appetitive cues in adolescents relative to an intermediary cognitive control response. Connectivity and coactivity data suggest these systems communicate at the level of the dorsal striatum differentially across development. Biased responding in this system is one possible mechanism underlying heightened risk-taking during adolescence. PMID:20809855

  17. Frontostriatal maturation predicts cognitive control failure to appetitive cues in adolescents

    PubMed Central

    Somerville, Leah H.; Hare, Todd; Casey, BJ

    2011-01-01

    Adolescent risk-taking is a public health issue that increases the odds of poor lifetime outcomes. One factor thought to influence adolescents' propensity for risk-taking is an enhanced sensitivity to appetitive cues, relative to an immature capacity to exert sufficient cognitive control. We tested this hypothesis by characterizing interactions among ventral striatal, dorsal striatal and prefrontal cortical regions with varying appetitive load using functional magnetic resonance imaging (fMRI) scanning. Child, teen, and adult participants performed a go nogo task with appetitive (happy faces) and neutral cues (calm faces). Impulse control to neutral cues showed linear improvement with age, whereas teens showed a nonlinear reduction in impulse control to appetitive cues. This performance decrement in teens was paralleled by enhanced activity in the ventral striatum. Prefrontal cortical recruitment correlated with overall accuracy and showed a linear response with age for nogo versus go trials. Connectivity analyses identified a ventral frontostriatal circuit including the inferior frontal gyrus and dorsal striatum during nogo versus go trials. Examining recruitment developmentally showed that teens had greater between-subjects ventral-dorsal striatal coactivation relative to children and adults for happy nogo versus go trials. These findings implicate exaggerated ventral striatal representation of appetitive cues in adolescents relative to an intermediary cognitive control response. Connectivity and coactivity data suggest these systems communicate at the level of the dorsal striatum differentially across development. Biased responding in this system is one possible mechanism underlying heightened risk-taking during adolescence. PMID:20809855

  18. Impaired Activation in Cognitive Control Regions Predicts Reversal Learning in Schizophrenia.

    PubMed

    Culbreth, Adam J; Gold, James M; Cools, Roshan; Barch, Deanna M

    2016-03-01

    Reinforcement learning deficits have been associated with schizophrenia (SZ). However, the pathophysiology that gives rise to these abnormalities remains unclear. To address this question, SZ patients (N = 58) and controls (CN; N = 36) completed a probabilistic reversal-learning paradigm during functional magnetic resonance imaging scanning. During the task, participants choose between 2 stimuli. Initially, 1 stimulus was frequently rewarded (80%); the other was infrequently rewarded (20%). The reward contingencies reversed periodically because the participant learned the more rewarded stimulus. The results indicated that SZ patients achieved fewer reversals than CN, and demonstrated decreased winstay-loseshift decision-making behavior. On loseshift compared to winstay trials, SZ patients showed reduced Blood Oxygen Level Dependent activation compared to CN in a network of brain regions widely associated with cognitive control, and striatal regions. Importantly, relationships between group membership and behavior were mediated by alterations in the activity of cognitive control regions, but not striatum. These findings indicate an important role for the cognitive control network in mediating the use and updating of value representations in SZ. Such results provide biological targets for further inquiry because researchers attempt to better characterize decision-making neural circuitry in SZ as a means to discover new pathways for interventions. PMID:26049083

  19. How Predictive Is Grip Force Control in the Complete Absence of Somatosensory Feedback?

    ERIC Educational Resources Information Center

    Nowak, Dennis A.; Glasauer, Stefan; Hermsdorfer, Joachim

    2004-01-01

    Grip force control relies on accurate internal models of the dynamics of our motor system and the external objects we manipulate. Internal models are not fixed entities, but rather are trained and updated by sensory experience. Sensory feedback signals relevant object properties and mechanical events, e.g. at the skin-object interface, to modify…

  20. A Rapid Test for Prediction of Nutrient Release from Controlled Release Fertilizers

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Nutrient release from soluble granular fertilizers can be modified by polymer coating. The coating technology can be fine-tuned to change the duration (3 to 9 months) and rate of nutrient release, hence these products are termed as controlled release fertilizers (CRF). There is a need to develop a r...