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Sample records for predictive control l-mpc

  1. 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.

  2. 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.

  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. Predictive fuzzy controller for robotic motion control

    SciTech Connect

    Huang, S.J.; Hu, C.F.

    1995-12-31

    A system output prediction strategy incorporated with a fuzzy controller is proposed to manipulate the robotic motion control. Usually, the current position and velocity errors are used to operate the fuzzy logic controller for picking out a corresponding rule. When the system has fast planning speed or time varying behavior, the required tracking accuracy is difficult to achieve by adjusting the fuzzy rules. In order to improve the position control accuracy and system robustness for the industrial application, the current position error in the fuzzy rules look-up table is substituted by the predictive position error of the next step by using the grey predictive algorithm. This idea is implemented on a five degrees of freedom robot. The experimental results show that this fuzzy controller has effectively improve the system performance and achieved the facilitation of fuzzy controller implementation.

  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. 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.

  7. 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.

  8. 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.

  9. 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…

  10. 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.

  11. 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.

  12. 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…

  13. 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)

  14. Model predictive control of constrained LPV systems

    NASA Astrophysics Data System (ADS)

    Yu, Shuyou; Böhm, Christoph; Chen, Hong; Allgöwer, Frank

    2012-06-01

    This article considers robust model predictive control (MPC) schemes for linear parameter varying (LPV) systems in which the time-varying parameter is assumed to be measured online and exploited for feedback. A closed-loop MPC with a parameter-dependent control law is proposed first. The parameter-dependent control law reduces conservativeness of the existing results with a static control law at the cost of higher computational burden. Furthermore, an MPC scheme with prediction horizon '1' is proposed to deal with the case of asymmetric constraints. Both approaches guarantee recursive feasibility and closed-loop stability if the considered optimisation problem is feasible at the initial time instant.

  15. 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.

  16. Controlling vibrations of a cutting process using predictive control

    NASA Astrophysics Data System (ADS)

    Fischer, Achim; Eberhard, Peter

    2014-07-01

    Unwanted vibrations in machining are detrimental to the equipment and the quality of the result. Notably chatter vibrations due to the regenerative effect are difficult to control and limit the achievable results. Typically, active and passive means are employed to prevent chatter from happening. This work proposes a predictive control strategy that actively uses information about the system past to predict future disturbances. Using those predicitions allows to counter the regenerative effect more effectively. The strategy is tested in simulation and improves the dynamic stability of the system greatly. It is robust with respect to quantitative errors in the disturbance predictions.

  17. 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.

  18. Predictive Control of Large Complex Networks

    NASA Astrophysics Data System (ADS)

    Haber, Aleksandar; Motter, Adilson E.

    Networks of coupled dynamical subsystems are increasingly used to represent complex natural and engineered systems. While recent technological developments give us improved means to actively control the dynamics of individual subsystems in various domains, network control remains a challenging problem due to difficulties imposed by intrinsic nonlinearities, control constraints, and the large-scale nature of the systems. In this talk, we will present a model predictive control approach that is effective while accounting for these realistic properties of complex networks. Our method can systematically identify control interventions that steer the trajectory to a desired state, even in the presence of strong nonlinearities and constraints. Numerical tests show that the method is applicable to a variety of networks, ranging from power grids to chemical reaction systems.

  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. Spacecraft Magnetic Cleanliness Prediction and Control

    NASA Astrophysics Data System (ADS)

    Weikert, S.; Mehlem, K.; Wiegand, A.

    2012-05-01

    The paper describes a sophisticated and realistic control and prediction method for the magnetic cleanliness of spacecraft, covering all phases of a project till the final system test. From the first establishment of the so-called magnetic moment allocation list the necessary boom length can be determined. The list is then continuously updated by real unit test results with the goal to ensure that the magnetic cleanliness budget is not exceeded at a given probability level. A complete example is described. The synthetic spacecraft modeling which predicts only quite late the final magnetic state of the spacecraft is also described. Finally, the most important cleanliness verification, the spacecraft system test, is described shortly with an example. The emphasis of the paper is put on the magnetic dipole moment allocation method.

  3. Constrained predictive control using orthogonal expansions

    SciTech Connect

    Finn, C.K. ); Wahlberg, B. . Dept. of Automatic Control); Ydstie, B.E. . Dept. of Chemical Engineering)

    1993-11-01

    Orthogonal expansion is routinely used for multivariable predictive control and optimization in the chemical and petrochemical manufacturing industries. In this article, the authors approximate bounded operators by orthogonal expansion. The rate of convergence depends on the choice of basis functions. Markov-Laguerre functions give rapid convergence for open-loop stable systems with long delay. The Markov-Kautz model can be used for lightly damped systems, and a more general orthogonal expansion is developed for modeling multivariable systems with widely scattered poles. The finite impulse response model is a special case of these models. A-priori knowledge about dominant time constants, time delay and oscillatory modes is used to reduce the model complexity and to improve conditioning of the parameter estimation algorithm. Algorithms for predictive control are developed, as well as conditions for constraint compatibility, closed-loop stability and constraint satisfaction for the ideal case. An H[infinity]--like design technique proposed guarantees robust stability in the presence of input constraints; output constraints may give chatter. A chatter-free algorithm is proposed.

  4. 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.

  5. 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. PMID:16082788

  6. Nonconvex model predictive control for commercial refrigeration

    NASA Astrophysics Data System (ADS)

    Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John

    2013-08-01

    We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.

  7. 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.

  8. Experimental results of a predictive neural network HVAC controller

    SciTech Connect

    Jeannette, E.; Assawamartbunlue, K.; Kreider, J.F.; Curtiss, P.S.

    1998-12-31

    Proportional, integral, and derivative (PID) control is widely used in many HVAC control processes and requires constant attention for optimal control. Artificial neural networks offer the potential for improved control of processes through predictive techniques. This paper introduces and shows experimental results of a predictive neural network (PNN) controller applied to an unstable hot water system in an air-handling unit. Actual laboratory testing of the PNN and PID controllers show favorable results for the PNN controller.

  9. 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.

  10. 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.

  11. Predictive Direct Torque Control for Induction Motor Drive

    NASA Astrophysics Data System (ADS)

    Benzaioua, A.; Ouhrouche, M.; Merabet, A.

    2008-06-01

    A predictive control combined with the direct torque control (DTC) to induction motor drive is presented. A new switching strategy is used in DTC, where the constant switching frequency is taken constant, and the speed tracking is done by a predictive controller. The scheme control is applied to induction motor drive in order to perform the dynamic responses of electromagnetic torque, stator flux and speed. A comparison between the PI controller and predictive controller for speed tracking is done. Results of simulation show that the performance of the proposed control scheme for induction motor drive is accurately achieved.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. Robot trajectory tracking with self-tuning predicted control

    NASA Technical Reports Server (NTRS)

    Cui, Xianzhong; Shin, Kang G.

    1988-01-01

    A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. Predicting psychological symptoms: the role of perceived thought control ability.

    PubMed

    Peterson, Rachel D; Klein, Jenny; Donnelly, Reesa; Renk, Kimberly

    2009-01-01

    The suppression of intrusive thoughts, which have been related significantly to depressive and anxious symptoms (Blumberg, 2000), has become an area of interest for those treating individuals with psychological disorders. The current study sought to extend the findings of Luciano, Algarabel, Tomas, and Martínez (2005), who developed the Thought Control Ability Questionnaire (TCAQ) and found that scores on this measure were predictive of psychopathology. In particular, this study examined the relationship between scores on the TCAQ and the Personality Assessment Inventory. Findings suggested that individuals' perceived thought control ability correlated significantly with several dimensions of commonly-occurring psychological symptoms (e.g. anxiety) and more severe and persistent psychological symptoms (e.g. schizophrenia). Regression analyses also showed that perceived thought control ability predicted significantly a range of psychological symptoms over and above individuals' sex and perceived stress. Findings suggested that thought control ability may be an important future research area in psychological assessment and intervention. PMID:19235599

  8. 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.

  9. 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.

  10. Predicting worsening asthma control following the common cold

    PubMed Central

    Walter, Michael J.; Castro, Mario; Kunselman, Susan J.; Chinchilli, Vernon M; Reno, Melissa; Ramkumar, Thiruvamoor P.; Avila, Pedro C.; Boushey, Homer A.; Ameredes, Bill T.; Bleecker, Eugene R.; Calhoun, William J.; Cherniack, Reuben M.; Craig, Timothy J.; Denlinger, Loren C.; Israel, Elliot; Fahy, John V.; Jarjour, Nizar N.; Kraft, Monica; Lazarus, Stephen C.; Lemanske, Robert F.; Martin, Richard J.; Peters, Stephen P.; Ramsdell, Joe W.; Sorkness, Christine A.; Rand Sutherland, E.; Szefler, Stanley J.; Wasserman, Stephen I.; Wechsler, Michael E.

    2008-01-01

    The asthmatic response to the common cold is highly variable and early characteristics that predict worsening of asthma control following a cold have not been identified. In this prospective multi-center cohort study of 413 adult subjects with asthma, we used the mini-Asthma Control Questionnaire (mini-ACQ) to quantify changes in asthma control and the Wisconsin Upper Respiratory Symptom Survey-21 (WURSS-21) to measure cold severity. Univariate and multivariable models examined demographic, physiologic, serologic, and cold-related characteristics for their relationship to changes in asthma control following a cold. We observed a clinically significant worsening of asthma control following a cold (increase in mini-ACQ of 0.69 ± 0.93). Univariate analysis demonstrated season, center location, cold length, and cold severity measurements all associated with a change in asthma control. Multivariable analysis of the covariates available within the first 2 days of cold onset revealed the day 2 and the cumulative sum of the day 1 and 2 WURSS-21 scores were significant predictors for the subsequent changes in asthma control. In asthmatic subjects the cold severity measured within the first 2 days can be used to predict subsequent changes in asthma control. This information may help clinicians prevent deterioration in asthma control following a cold. PMID:18768579

  11. Prediction of active control of subsonic centrifugal compressor rotating stall

    NASA Technical Reports Server (NTRS)

    Lawless, Patrick B.; Fleeter, Sanford

    1993-01-01

    A mathematical model is developed to predict the suppression of rotating stall in a centrifugal compressor with a vaned diffuser. This model is based on the employment of a control vortical waveform generated upstream of the impeller inlet to damp weak potential disturbances that are the early stages of rotating stall. The control system is analyzed by matching the perturbation pressure in the compressor inlet and exit flow fields with a model for the unsteady behavior of the compressor. The model was effective at predicting the stalling behavior of the Purdue Low Speed Centrifugal Compressor for two distinctly different stall patterns. Predictions made for the effect of a controlled inlet vorticity wave on the stability of the compressor show that for minimum control wave magnitudes, on the order of the total inlet disturbance magnitude, significant damping of the instability can be achieved. For control waves of sufficient amplitude, the control phase angle appears to be the most important factor in maintaining a stable condition in the compressor.

  12. 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.

  13. Model-predictive control of polymer composite manufacturing processes

    NASA Astrophysics Data System (ADS)

    Voorakaranam, Srikanth

    Quality control is crucial for reducing costs and enabling a more widespread use of fiber-resin composites. This research focuses on development of model-based control strategies for controlling product quality in continuous processes for manufacturing polymer composites with injected pultrusion as a prototype. The control objective is to maximize production rates, meeting quality criteria such as eliminating voids, achieving desired degree of cure and preventing backflow of resin from the die entrance. A 2-D mathematical model of IP developed by Kommu is extended to incorporate die dynamics. Exercising the model over a range of operating conditions, the requirements for a control system are formulated. Simultaneous requirements of optimization and control are met by using a cascade strategy consisting of supervisory and regulatory layers. The supervisory layer consists of an optimizer in conjunction with a steady-state cure model and an injection pressure model. The cure model is linear in important process variables. The injection pressure model is also linear in pullspeed. A linear program generates setpoints for pullspeed, injection pressure and temperatures in the three zones of the die which are implemented by the regulatory layer using multiple PID controllers. This formulation operates the process optimally. A major problem in feedback control of the IP process is the inability to measure quality variables on-line. An inferential control strategy is proposed to tackle this. It is then extended so that it can be implemented in a model predictive control formulation. This novel strategy called model predictive inferential control is general enough to accommodate multiple secondary measurements as well as nonlinear estimators and controllers. Collinearity among multiple measurements is addressed through principal component regression. The estimator uses frequent secondary measurements to estimate the effect of the disturbances on the primary variable which are

  14. 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.

  15. 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.

  16. Implementation of model predictive control on a hydrothermal oxidation reactor

    SciTech Connect

    Muske, K.R.; Dell`Orco, P.C.; Le, L.A.; Flesner, R.L.

    1998-12-31

    This paper describes the model-based control algorithm developed for a hydrothermal oxidation reactor at the Pantex Department of Energy facility in Amarillo, Texas. The combination of base hydrolysis and hydrothermal oxidation is used for the disposal of PBX 9404 high explosive at Pantex. The reactor oxidizes the organic compounds in the hydrolysate solutions obtained from the base hydrolysis process. The objective of the model predictive controller is to minimize the total aqueous nitrogen compounds in the effluent of the reactor. The controller also maintains a desired excess oxygen concentration in the reactor effluent to ensure the complete destruction of the organic carbon compounds in the hydrolysate.

  17. 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. PMID:24458216

  18. 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.

  19. Decentralized robust nonlinear model predictive controller for unmanned aerial systems

    NASA Astrophysics Data System (ADS)

    Garcia Garreton, Gonzalo A.

    The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1. A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2. A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3. An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4. A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible.

  20. 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.

  1. 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.

  2. 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

  3. Prediction as Persuasion and Threat: Interaction of Locus of Control and Locus of Prediction on Compliance and Reactance.

    ERIC Educational Resources Information Center

    Goggin, William C.

    A model of persuasion suggests that individuals comply with a prediction of their behavior because they are persuaded by that prediction; a model of threat suggests that they defy prediction because of its threat of control. College students with either internal (N=20) or external (N=20) loci of control were informed of the accuracy of the…

  4. 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.

  5. Punishment Sensitivity Predicts the Impact of Punishment on Cognitive Control

    PubMed Central

    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. PMID:24058520

  6. 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.

  7. 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.

  8. Applying new optimization algorithms to more predictive control

    SciTech Connect

    Wright, S.J.

    1996-03-01

    The connections between optimization and control theory have been explored by many researchers and optimization algorithms have been applied with success to optimal control. The rapid pace of developments in model predictive control has given rise to a host of new problems to which optimization has yet to be applied. Concurrently, developments in optimization, and especially in interior-point methods, have produced a new set of algorithms that may be especially helpful in this context. In this paper, we reexamine the relatively simple problem of control of linear processes subject to quadratic objectives and general linear constraints. We show how new algorithms for quadratic programming can be applied efficiently to this problem. The approach extends to several more general problems in straightforward ways.

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. Constrained model predictive control, state estimation and coordination

    NASA Astrophysics Data System (ADS)

    Yan, Jun

    In this dissertation, we study the interaction between the control performance and the quality of the state estimation in a constrained Model Predictive Control (MPC) framework for systems with stochastic disturbances. This consists of three parts: (i) the development of a constrained MPC formulation that adapts to the quality of the state estimation via constraints; (ii) the application of such a control law in a multi-vehicle formation coordinated control problem in which each vehicle operates subject to a no-collision constraint posed by others' imperfect prediction computed from finite bit-rate, communicated data; (iii) the design of the predictors and the communication resource assignment problem that satisfy the performance requirement from Part (ii). Model Predictive Control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. However, if the state constraints were handled in the same certainty-equivalence fashion, the resulting control law could drive the real state to violate the constraints frequently. Part (i) focuses on exploring the inclusion of state estimates into the constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. In Part (ii), we consider applying constrained MPC as a local control law in a coordinated control problem of a group of distributed autonomous systems. Interactions between the systems are captured via constraints. First, we inspect the application of constrained MPC to a completely deterministic case. Formation stability theorems are derived for the subsystems and conditions on the local constraint set are derived in order to

  14. Cognitive control predicts use of model-based reinforcement learning.

    PubMed

    Otto, A Ross; Skatova, Anya; Madlon-Kay, Seth; Daw, Nathaniel D

    2015-02-01

    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

  15. Predictive maintenance now available for controls and instrumentation

    SciTech Connect

    Frerichs, D.K.

    1999-11-01

    Predictive maintenance (PdM) methods now abound in all areas of the powerhouse. Vibration analysis methods for all rotating machinery, oil analysis for both lubricated parts and transformers, wear particle analysis, acoustic leak detection, and loose parts monitoring are commonplace. But what about the controls and instrumentation arena? Smart positioners/actuators are part of the answer. They can tell us that stroke times are different or something is sticking, and therefore some level of maintenance is in order. Smart transmitters and new sensing technologies allow those devices to hold their calibration longer. But how does one know when it is time to re-calibrate the sensor? When does an RTD or thermocouple and/or it`s signal converter begin to drift? When did the steam temperature controls start to behave sub-optimally? If you perform the maintenance too early, you waste maintenance dollars. If you do it too late, you could be running off normal and not realize it, which in turn wastes operation dollars. There are hundreds of controllers and sensors to be maintained (thousands in a large facility), so guessing wrong can waste a lot of O and M dollars. This paper will explore new technology that finally allows the science of predictive maintenance to lend its benefits to the field of controls and instrumentation.

  16. Robust model predictive control for optimal continuous drug administration.

    PubMed

    Sopasakis, Pantelis; Patrinos, Panagiotis; Sarimveis, Haralambos

    2014-10-01

    In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise. A case study on a PBPK model with 7 compartments, constraints on 5 tissues and a variable drug concentration set-point illustrates the efficiency of the methodology in drug dosing control applications. The proposed methodology is also tested in an uncertain setting and proves successful in presence of modelling errors and inaccurate measurements. PMID:24986530

  17. Vehicle yaw stability control using active limited-slip differential via model predictive control methods

    NASA Astrophysics Data System (ADS)

    Rubin, Daniel; Arogeti, Shai A.

    2015-09-01

    In this paper, the problem of vehicle yaw control using an active limited-slip differential (ALSD) applied on the rear axle is addressed. The controller objective is to minimise yaw-rate and body slip-angle errors, with respect to target values. A novel model predictive controller is designed, using a linear parameter-varying (LPV) vehicle model, which takes into account the ALSD dynamics and its constraints. The controller is simulated using a 10DOF Matlab/Simulink simulation model and a CarSim model. These simulations exemplify the controller yaw-rate and slip-angle tracking performances, under challenging manoeuvres and road conditions. The model predictive controller performances surpass those of a reference sliding mode controller, and can narrow the loss of performances due to the ALSD's inability to transfer torque regardless of driving conditions.

  18. 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.

  19. 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.

  20. Prediction in the Vestibular Control of Arm Movements.

    PubMed

    Blouin, Jean; Bresciani, Jean-Pierre; Guillaud, Etienne; Simoneau, Martin

    2015-01-01

    The contribution of vestibular signals to motor control has been evidenced in postural, locomotor, and oculomotor studies. Here, we review studies showing that vestibular information also contributes to the control of arm movements during whole-body motion. The data reviewed suggest that vestibular information is used by the arm motor system to maintain the initial hand position or the planned hand trajectory unaltered during body motion. This requires integration of vestibular and cervical inputs to determine the trunk motion dynamics. These studies further suggest that the vestibular control of arm movement relies on rapid and efficient vestibulomotor transformations that cannot be considered automatic. We also reviewed evidence suggesting that the vestibular afferents can be used by the brain to predict and counteract body-rotation-induced torques (e.g., Coriolis) acting on the arm when reaching for a target while turning the trunk. PMID:26595953

  1. 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.

  2. Dinucleotide controlled null models for comparative RNA gene prediction

    PubMed Central

    Gesell, Tanja; Washietl, Stefan

    2008-01-01

    Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple

  3. 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.

  4. 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. PMID:21056412

  5. [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

  6. 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.

  7. 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

  8. 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. PMID:26549567

  9. What`s new in multivariable predictive control

    SciTech Connect

    Colwell, L.W.; Poe, W.A.; Papadopoulos, M.N.; Gamez, J.P.

    1995-11-01

    Multivariable control techniques have been successfully applied to a variety of gas processing operations. The technology has been applied to CO{sub 2} recovery towers, cryogenic demethanizers, lean oil absorbers, rich oil demethanizers, rich oil stills, deethanizers, depropanizers, deisobutanizers, amine treaters, sulfur recovery units, nitrogen rejection units and compressors. The system has been developed with a modular structure and employs process model based predictions of key plant variables. Modules for each type of operation are available and, with minimal modification, can be applied to a specific unit since the key plant variables are usually common between plants and are affected by similar disturbances. Adaptive nonlinear multivariable control models allow continuous operation at optimum conditions within plant constraints. In most applications a personal computer (PC) containing the control software dan supervisory control and data acquisition (SCADA) system operates under a UNIX operating system and interfaces with the plant`s existing control system. The PC-based system dispatches setpoints that have been calculated to optimize on-line the profitability of the plant. A typical project can be implemented in 4-6 months with a payout of less than a year by increasing natural gas liquids (NGL) revenues and decreasing plant operating costs. This paper describes the technology and the initial installation results.

  10. 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.

  11. Predictive mechanisms in the control of contour following

    PubMed Central

    Tramper, Julian J.; Flanders, Martha

    2013-01-01

    In haptic exploration, when running a fingertip along a surface, the control system may attempt to anticipate upcoming changes in curvature in order to maintain a consistent level of contact force. Such predictive mechanisms are well known in the visual system, but have yet to be studied in the somatosensory system. Thus the present experiment was designed to reveal human capabilities for different types of haptic prediction. A robot arm with a large 3D workspace was attached to the index fingertip and was programmed to produce virtual surfaces with curvatures that varied within and across trials. With eyes closed, subjects moved the fingertip around elliptical hoops with flattened regions or Limaçon shapes, where the curvature varied continuously. Subjects anticipated the corner of the flattened region rather poorly, but for the Limaçon shapes they varied finger speed with upcoming curvature according to the two-thirds power law. Furthermore, although the Limaçon shapes were randomly presented in various 3D orientations, modulation of contact force also indicated good anticipation of upcoming changes in curvature. The results demonstrate that it is difficult to haptically anticipate the spatial location of an abrupt change in curvature, but smooth changes in curvature may be facilitated by anticipatory predictions. PMID:23649968

  12. 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. PMID:20428856

  13. 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

  14. Study on Noise Prediction Model and Control Schemes for Substation

    PubMed Central

    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

  15. 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

  16. Humans are sensitive to attention control when predicting others' actions.

    PubMed

    Pesquita, Ana; Chapman, Craig S; Enns, James T

    2016-08-01

    Studies of social perception report acute human sensitivity to where another's attention is aimed. Here we ask whether humans are also sensitive to how the other's attention is deployed. Observers viewed videos of actors reaching to targets without knowing that those actors were sometimes choosing to reach to one of the targets (endogenous control) and sometimes being directed to reach to one of the targets (exogenous control). Experiments 1 and 2 showed that observers could respond more rapidly when actors chose where to reach, yet were at chance when guessing whether the reach was chosen or directed. This implicit sensitivity to attention control held when either actor's faces or limbs were masked (experiment 3) and when only the earliest actor's movements were visible (experiment 4). Individual differences in sensitivity to choice correlated with an independent measure of social aptitude. We conclude that humans are sensitive to attention control through an implicit kinematic process linked to empathy. The findings support the hypothesis that social cognition involves the predictive modeling of others' attentional states. PMID:27436897

  17. Design and Performance Analysis of Incremental Networked Predictive Control Systems.

    PubMed

    Pang, Zhong-Hua; Liu, Guo-Ping; Zhou, Donghua

    2016-06-01

    This paper is concerned with the design and performance analysis of networked control systems with network-induced delay, packet disorder, and packet dropout. Based on the incremental form of the plant input-output model and an incremental error feedback control strategy, an incremental networked predictive control (INPC) scheme is proposed to actively compensate for the round-trip time delay resulting from the above communication constraints. The output tracking performance and closed-loop stability of the resulting INPC system are considered for two cases: 1) plant-model match case and 2) plant-model mismatch case. For the former case, the INPC system can achieve the same output tracking performance and closed-loop stability as those of the corresponding local control system. For the latter case, a sufficient condition for the stability of the closed-loop INPC system is derived using the switched system theory. Furthermore, for both cases, the INPC system can achieve a zero steady-state output tracking error for step commands. Finally, both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method. PMID:26186798

  18. 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

  19. Cognitive control predicted by color vision, and vice versa.

    PubMed

    Colzato, Lorenza S; Sellaro, Roberta; Hulka, Lea M; Quednow, Boris B; Hommel, Bernhard

    2014-09-01

    One of the most important functions of cognitive control is to continuously adapt cognitive processes to changing and often conflicting demands of the environment. Dopamine (DA) has been suggested to play a key role in the signaling and resolution of such response conflict. Given that DA is found in high concentration in the retina, color vision discrimination has been suggested as an index of DA functioning and in particular blue-yellow color vision impairment (CVI) has been used to indicate a central hypodopaminergic state. We used color discrimination (indexed by the total color distance score; TCDS) to predict individual differences in the cognitive control of response conflict, as reflected by conflict-resolution efficiency in an auditory Simon task. As expected, participants showing better color discrimination were more efficient in resolving response conflict. Interestingly, participants showing a blue-yellow CVI were associated with less efficiency in handling response conflict. Our findings indicate that color vision discrimination might represent a promising predictor of cognitive controlability in healthy individuals. PMID:25058057

  20. 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.

  1. 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.

  2. Nonlinear model predictive control based on collective neurodynamic optimization.

    PubMed

    Yan, Zheng; Wang, Jun

    2015-04-01

    In general, nonlinear model predictive control (NMPC) entails solving a sequential global optimization problem with a nonconvex cost function or constraints. This paper presents a novel collective neurodynamic optimization approach to NMPC without linearization. Utilizing a group of recurrent neural networks (RNNs), the proposed collective neurodynamic optimization approach searches for optimal solutions to global optimization problems by emulating brainstorming. Each RNN is guaranteed to converge to a candidate solution by performing constrained local search. By exchanging information and iteratively improving the starting and restarting points of each RNN using the information of local and global best known solutions in a framework of particle swarm optimization, the group of RNNs is able to reach global optimal solutions to global optimization problems. The essence of the proposed collective neurodynamic optimization approach lies in the integration of capabilities of global search and precise local search. The simulation results of many cases are discussed to substantiate the effectiveness and the characteristics of the proposed approach. PMID:25608315

  3. 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.

  4. Predictive active disturbance rejection control for processes with time delay.

    PubMed

    Zheng, Qinling; Gao, Zhiqiang

    2014-07-01

    Active disturbance rejection control (ADRC) has been shown to be an effective tool in dealing with real world problems of dynamic uncertainties, disturbances, nonlinearities, etc. This paper addresses its existing limitations with plants that have a large transport delay. In particular, to overcome the delay, the extended state observer (ESO) in ADRC is modified to form a predictive ADRC, leading to significant improvements in the transient response and stability characteristics, as shown in extensive simulation studies and hardware-in-the-loop tests, as well as in the frequency response analysis. In this research, it is assumed that the amount of delay is approximately known, as is the approximated model of the plant. Even with such uncharacteristic assumptions for ADRC, the proposed method still exhibits significant improvements in both performance and robustness over the existing methods such as the dead-time compensator based on disturbance observer and the Filtered Smith Predictor, in the context of some well-known problems of chemical reactor and boiler control problems. PMID:24182516

  5. Factors Predicting Atypical Development of Nighttime Bladder Control

    PubMed Central

    Sullivan, Sarah; Heron, Jon

    2015-01-01

    ABSTRACT: Objective: To derive latent classes (longitudinal “phenotypes”) of frequency of bedwetting from 4 to 9 years and to examine their association with developmental delay, parental history of bedwetting, length of gestation and birth weight. Method: The authors used data from 8,769 children from the UK Avon Longitudinal Study of Parents and Children cohort. Mothers provided repeated reports on their child's frequency of bedwetting from 4 to 9 years. The authors used longitudinal latent class analysis to derive latent classes of bedwetting and examined their association with sex, developmental level at 18 months, parental history of wetting, birth weight, and gestational length. Results: The authors identified 5 latent classes: (1) “normative”—low probability of bedwetting; (2) “infrequent delayed”—delayed attainment of nighttime bladder control with bedwetting control with bedwetting ≥ twice a week; (4) “infrequent persistent”—persistent bedwetting < twice a week; and (5) “frequent persistent”—persistent bedwetting ≥ twice a week. Male gender (odds ratio = 3.20 [95% confidence interval = 2.36–4.34]), developmental delay, for example, delayed social skills (1.33 [1.11–1.58]), and maternal history of wetting (3.91 [2.60–5.88]) were associated with an increase in the odds of bedwetting at 4 to 9 years. There was little evidence that low birth weight and shorter gestation period were associated with bedwetting. Conclusion: The authors described patterns of development of nighttime bladder control and found evidence for factors that predict continuation of bedwetting at school age. Increased knowledge of risk factors for bedwetting is needed to identify children at risk of future problems attaining and maintaining continence. PMID:26468941

  6. 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.

  7. Predictive powertrain control using powertrain history and GPS data

    DOEpatents

    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.

  8. 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

  9. 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.

  10. 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.

  11. Multivariate Prediction of College Grades for Disadvantaged and Control Students

    ERIC Educational Resources Information Center

    Pedrini, Bonnie; Pedrini, D. T.

    1977-01-01

    Shows that attrition/persistence (dropouts or students not continuously enrolled versus students continuously enrolled) is the primary, significant, single variate in the prediction of grades, and that attrition/persistence and American College Test (ACT) scores are the significant multiple variates in the prediction of grade point average. (RL)

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

    NASA Technical Reports Server (NTRS)

    Degroot, N. F.

    1982-01-01

    Susceptibility modeling, prediction of radio frequency interference from satellites, operational radio frequency interference control, and international regulations are considered. The existing satellite interference prediction program DSIP2 is emphasized. A summary status evaluation and recommendations for future work are given.

  13. Predictive motor control of sensory dynamics in auditory active sensing.

    PubMed

    Morillon, Benjamin; Hackett, Troy A; Kajikawa, Yoshinao; Schroeder, Charles E

    2015-04-01

    Neuronal oscillations present potential physiological substrates for brain operations that require temporal prediction. We review this idea in the context of auditory perception. Using speech as an exemplar, we illustrate how hierarchically organized oscillations can be used to parse and encode complex input streams. We then consider the motor system as a major source of rhythms (temporal priors) in auditory processing, that act in concert with attention to sharpen sensory representations and link them across areas. We discuss the circuits that could mediate this audio-motor interaction, notably the potential role of the somatosensory system. Finally, we reposition temporal predictions in the context of internal models, discussing how they interact with feature-based or spatial predictions. We argue that complementary predictions interact synergistically according to the organizational principles of each sensory system, forming multidimensional filters crucial to perception. PMID:25594376

  14. 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.

  15. 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.

  16. 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.

  17. 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…

  18. Implementation of a model for census prediction and control.

    PubMed Central

    Swain, R W; Kilpatrick, K E; Marsh, J J

    1977-01-01

    A model is described that predicts hospital census and computes, for each day, the number of elective admissions that will maximize the census over the short run, subject to constraints on the probability of overflow. Where a computer is available the model provides detailed predictions of census in units as small as 10 beds; used with manual computation the model allows production of tables of the recommended numbers of elective admissions to the hospital as a whole. The model has been tested in five hospitals and is part of the admissions system in two of them; implementation is described, and the results obtained are discussed. PMID:591350

  19. 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.

  20. 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.

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

    ERIC Educational Resources Information Center

    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…

  2. Predicting Changes in Older Adults' Interpersonal Control Strivings

    ERIC Educational Resources Information Center

    Sorkin, Dara H.; Rook, Karen S.; Heckhausen, Jutta; Billimek, John

    2009-01-01

    People vary in the importance they ascribe to, and efforts they invest in, maintaining positive relationships with others. Research has linked such variation in interpersonal control strivings to the quality of social exchanges experienced, but little work has examined the predictors of interpersonal control strivings. Given the importance of…

  3. 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)…

  4. 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

  5. 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

  6. 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

  7. 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…

  8. 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.

  9. Noise prediction and control of Pudong International Airport expansion project.

    PubMed

    Lei, Bin; Yang, Xin; Yang, Jianguo

    2009-04-01

    The Environmental Impact Assessment (EIA) process of the third runway building project of Pudong International Airport is briefly introduced in the paper. The basic principle, the features, and the operation steps of newly imported FAA's Integrated Noise Model (INM) are discussed for evaluating the aircraft noise impacts. The prediction of the aircraft noise and the countermeasures for the noise mitigation are developed, which includes the reasonable runway location, the optimized land use, the selection of low noise aircrafts, the Fly Quit Program, the relocation of sensitive receptors and the noise insulation of sensitive buildings. Finally, the expansion project is justified and its feasibility is confirmed. PMID:18373206

  10. 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. PMID:26320866

  11. 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.

  12. 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). PMID:23627657

  13. 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…

  14. Sustained Attention and Age Predict Inhibitory Control during Early Childhood

    ERIC Educational Resources Information Center

    Reck, Sarah G.; Hund, Alycia M.

    2011-01-01

    Executive functioning skills develop rapidly during early childhood. Recent research has focused on specifying this development, particularly predictors of executive functioning skills. Here we focus on sustained attention as a predictor of inhibitory control, one key executive functioning component. Although sustained attention and inhibitory…

  15. 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…

  16. 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

  17. 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.

  18. 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.

  19. Asynchronous update based networked predictive control system using a novel proactive compensation strategy.

    PubMed

    Duan, Yingyao; Zuo, Xin; Liu, Jianwei

    2016-01-01

    Networked predictive control system (NPCS) has been proposed to address random delays and data dropouts in networked control systems (NCSs). A remaining challenge of this approach is that the controller has uncertain information about the actual control inputs, which leads to the predicted control input errors. The main contribution of this paper is to develop an explicit mechanism running in the distributed network nodes asynchronously, which enables the controller node to keep informed of the states of the actuator node without a priori knowledge about the network. Based on this mechanism, a novel proactive compensation strategy is proposed to develop asynchronous update based networked predictive control system (AUBNPCS). The stability criterion of AUBNPCS is derived analytically. A simulation experiment based on Truetime demonstrates the effectiveness of the scheme. PMID:26582090

  20. Nonlinear model identification and adaptive model predictive control using neural networks.

    PubMed

    Akpan, Vincent A; Hassapis, George D

    2011-04-01

    This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time. PMID:21281932

  1. 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

  2. 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.

  3. Model analysis of remotely controlled rendezvous and docking with display prediction

    NASA Technical Reports Server (NTRS)

    Milgram, P.; Wewerinke, P. H.

    1986-01-01

    Manual control of rendezvous and docking (RVD) of two spacecraft in low earth orbit by a remote human operator is discussed. Experimental evidence has shown that control performance degradation for large transmission delays (between spacecraft and operations control center) can be substantially improved by the introduction of predictor displays. An intial Optimal Control Model (OCM) analysis of RVD translational and rotational perturbation control was performed, with emphasis placed on the predictive capabilities of the combined Kalman estimator/optimal predictor with respect to control performance, for a range of time delays, motor noise levels and tracking axes. OCM predictions are then used as a reference for comparing tracking performance with a simple predictor display, as well as with no display prediction at all. Use is made here of an imperfect internal model formulation, whereby it is assumed that the human operator has no knowledge of the system transmission delay.

  4. 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...

  5. Prediction and Control of Vortex Dominated and Vortex-wake Flows

    NASA Technical Reports Server (NTRS)

    Kandil, Osama

    1996-01-01

    This report describes the activities and accomplishments under this research grant, including a list of publications and dissertations, produced in the field of prediction and control of vortex dominated and vortex wake flows.

  6. Identification-free adaptive optimal control based on switching predictive models

    NASA Astrophysics Data System (ADS)

    Luo, Wenguang; Pan, Shenghui; Ma, Zhaomin; Lan, Hongli

    2008-10-01

    An identification-free adaptive optimal control based on switching predictive models is proposed for the systems with big inertia, long time delay and multi models. Multi predictive models are set in the identification-free adaptive predictive control, and switched according to the optimal switching instants in control of the switching law along with the system running situations in real time. The switching law is designed based on the most important character parameter of the systems, and the optimal switching instants are computed out with the optimal theory for switched systems. The simulation test results show the proposed method is suitable to the systems, such as superheated steam temperature systems of electric power plants, can provide excellent control performance, improve rejecting disturbance ability and self-adaptability, and has lower demand on the predictive model precision.

  7. 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.

  8. 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. PMID:25538025

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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

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

    DOEpatents

    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.

  16. 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

  17. 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

  18. Water Prediction and Control Technologies for Large-scale Water Systems

    NASA Astrophysics Data System (ADS)

    Tian, Xin; van de Giesen, Nick; van Overloop, Peter-Jules

    2014-05-01

    A number of control techniques have been used in the field of operational water management over recent decades. Among these techniques, the ones that utilize prediction to anticipate near-future problems, such as Model Predictive Control (MPC), have shown the most promising results. Constraints handling and multi-objective management can be explicitly taken into account in MPC. To control large-scale systems, several extensions to standard MPC have been proposed. Firstly, Proper Orthogonal Decomposition (POD-MPC) has been applied to reduce the order the states and computational time. Secondly, a tree-based scheme (TB-MPC) has been proposed to cope with uncertainties of the prediction that are inherently parts of large scale systems. Thirdly, a distributed scheme (DMPC) has been proposed to deal with multiple regions and multiple goals in a computationally tractable way. Simulation experiments on the Dutch water system illustrate that tree-based distributed MPC outperforms feedback control, feedforward control and conventional MPC. Keywords: Model Predictive Control; Proper Orthogonal Decomposition; tree-based control; distributed control; Large Scale Systems;

  19. Model Predictive Control for Automobile Ecological Driving Using Traffic Signal Information

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Daisuke; Kamal, M. A. S.; Mukai, Masakazu; Kawabe, Taketoshi

    This paper presents development of a control system for ecological driving of an automobile. Prediction using traffic signal information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from Intelligent Transportation Systems (ITS). Model predictive control is used to calculate optimal vehicle control inputs using traffic signal information. The performance of the proposed method was analyzed through computer simulation results. It was observed that fuel economy was improved compared with driving of a typical human driving model.

  20. 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

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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

  11. Emotional attentional control predicts changes in diurnal cortisol secretion following exposure to a prolonged psychosocial stressor.

    PubMed

    Lenaert, Bert; Barry, Tom J; Schruers, Koen; Vervliet, Bram; Hermans, Dirk

    2016-01-01

    Hypothalamic-pituitary-adrenal (HPA) axis irregularities have been associated with several psychological disorders. Hence, the identification of individual difference variables that predict variations in HPA-axis activity represents an important challenge for psychiatric research. We investigated whether self-reported attentional control in emotionally demanding situations prospectively predicted changes in diurnal salivary cortisol secretion following exposure to a prolonged psychosocial stressor. Low ability to voluntarily control attention has previously been associated with anxiety and depressive symptomatology. Attentional control was assessed using the Emotional Attentional Control Scale. In students who were preparing for academic examination, salivary cortisol was assessed before (time 1) and after (time 2) examination. Results showed that lower levels of self-reported emotional attentional control at time 1 (N=90) predicted higher absolute diurnal cortisol secretion and a slower decline in cortisol throughout the day at time 2 (N=71). Difficulty controlling attention during emotional experiences may lead to chronic HPA-axis hyperactivity after prolonged exposure to stress. These results indicate that screening for individual differences may foster prediction of HPA-axis disturbances, paving the way for targeted disorder prevention. PMID:26539967

  12. 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.

  13. Model Predictive Control application for real time operation of controlled structures for the Water Authority Noorderzijlvest, The Netherlands

    NASA Astrophysics Data System (ADS)

    van Heeringen, Klaas-Jan; Gooijer, Jan; Knot, Floris; Talsma, Jan

    2015-04-01

    In the Netherlands, flood protection has always been a key issue to protect settlements against storm surges and riverine floods. Whereas flood protection traditionally focused on structural measures, nowadays the availability of meteorological and hydrological forecasts enable the application of more advanced real-time control techniques for operating the existing hydraulic infrastructure in an anticipatory and more efficient way. Model Predictive Control (MPC) is a powerful technique to derive optimal control variables with the help of model based predictions evaluated against a control objective. In a project for the regional water authority Noorderzijlvest in the north of the Netherlands, it has been shown that MPC can increase the safety level of the system during flood events by an anticipatory pre-release of water. Furthermore, energy costs of pumps can be reduced by making tactical use of the water storage and shifting pump activities during normal operating conditions to off-peak hours. In this way cheap energy is used in combination of gravity flow through gates during low tide periods. MPC has now been implemented for daily operational use of the whole water system of the water authority Noorderzijlvest. The system developed to a real time decision support system which not only supports the daily operation but is able to directly implement the optimal control settings at the structures. We explain how we set-up and calibrated a prediction model (RTC-Tools) that is accurate and fast enough for optimization purposes, and how we integrated it in the operational flood early warning system (Delft-FEWS). Beside the prediction model, the weights and the factors of the objective function are an important element of MPC, since they shape the control objective. We developed special features in Delft-FEWS to allow the operators to adjust the objective function in order to meet changing requirements and to evaluate different control strategies.

  14. 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.

  15. 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

  16. 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.

  17. Generalized minimum variance control under long-range prediction horizon setups.

    PubMed

    Silveira, Antonio; Trentini, Rodrigo; Coelho, Antonio; Kutzner, Rüdiger; Hofmann, Lutz

    2016-05-01

    This paper presents the design and evaluation of a minimal order Generalized Minimum Variance controller with long-range prediction horizon and how it affects the controller and plant output variances. This study investigates how the increased prediction horizon can contribute to mitigate stochastic disturbances and attenuate oscillations. In order to design high order prediction minimum variance filters, a design procedure independent of the Diophantine Equation solution is used. The evaluation is conducted through simulations and practical essays with two different plants: a first order water flow rate problem and a second order under-damped electronic circuit. Both problems are assessed under an incremental control scheme and based on identified stochastic models. Also, two optimal tuning procedures for the algorithm are proposed. PMID:26899555

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

    NASA Astrophysics Data System (ADS)

    Arumugasamy, Senthil Kumar; Ahmad, Z.

    2015-05-01

    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 ɛ-caprolactone (ɛ-CL) for Poly (ɛ-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 (Mn) 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.

  19. 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.

  20. 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.

  1. 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. PMID:24021543

  2. 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.

  3. 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.

  4. Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control

    PubMed Central

    Deshpande, Sunil; Nandola, Naresh N.; Rivera, Daniel E.; Younger, Jarred W.

    2014-01-01

    The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health. PMID:25506132

  5. The interplay of maternal sensitivity and gentle control when predicting children's subsequent academic functioning: Evidence of mediation by effortful control.

    PubMed

    Kopystynska, Olena; Spinrad, Tracy L; Seay, Danielle M; Eisenberg, Nancy

    2016-06-01

    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 measures of children's academic functioning were combined across 72 and 84 months (T5/T6; Ns = 255, 222, 200, 162, and 143). Using structural equation modeling, results demonstrated that T1 maternal sensitivity moderated the relation between T1 maternal gentle control and T2 EC, and T3 EC predicted children's later academic functioning. There was evidence for moderated mediation, such that when maternal sensitivity was high, children's EC mediated the relation between T1 maternal gentle control and children's academic functioning, even after controlling for stability of the constructs. The relation between maternal gentle control and children's EC was not significant under conditions of low maternal sensitivity. Implications for parenting programs are offered and future research directions are discussed. (PsycINFO Database Record PMID:27228451

  6. 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.

  7. Urinary Bromotyrosine Measures Asthma Control and Predicts Asthma Exacerbations in Children

    PubMed Central

    Wedes, Samuel H.; Wu, Weijia; Comhair, Suzy A. A.; McDowell, Karen M.; DiDonato, Joseph A.; Erzurum, Serpil C.; Hazen, Stanley L.

    2012-01-01

    Objectives To determine the usefulness of urinary bromotyrosine, a noninvasive marker of eosinophil-catalyzed protein oxidation, in tracking with indexes of asthma control and in predicting future asthma exacerbations in children. Study design Children with asthma were recruited consecutively at the time of clinic visit. Urine was obtained, along with spirometry, exhaled nitric oxide, and Asthma Control Questionnaire data. Follow-up phone calls were made 6 weeks after enrollment. Results Fifty-seven participants were enrolled. Urinary bromotyrosine levels tracked significantly with indexes of asthma control as assessed by Asthma Control Questionnaire scores at baseline (R = 0.38, P = .004) and follow-up (R = 0.39, P = .008). Participants with high baseline levels of bromotyrosine were 18.1-fold (95% CI 2.1–153.1, P = .0004) more likely to have inadequately controlled asthma and 4.0-fold more likely (95% CI 1.1–14.7, P = .03) to have an asthma exacerbation (unexpected emergency department visit; doctor’s appointment or phone call; oral or parenteral corticosteroid burst; acute asthma-related respiratory symptoms) over the ensuing 6 weeks. Exhaled nitric oxide levels did not track with Asthma Control Questionnaire data; and immunoglobulin E, eosinophil count, spirometry, and exhaled nitric oxide levels failed to predict asthma exacerbations. Conclusions Urinary bromotyrosine tracks with asthma control and predicts the risk of future asthma exacerbations in children. PMID:21392781

  8. 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

  9. 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

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

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

    PubMed Central

    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. PMID:25379415

  17. Online elicitation of Mamdani-type fuzzy rules via TSK-based generalized predictive control.

    PubMed

    Mahfouf, M; Abbod, M F; Linkens, D A

    2003-01-01

    Many synergies have been proposed between soft-computing techniques, such as neural networks (NNs), fuzzy logic (FL), and genetic algorithms (GAs), which have shown that such hybrid structures can work well and also add more robustness to the control system design. In this paper, a new control architecture is proposed whereby the on-line generated fuzzy rules relating to the self-organizing fuzzy logic controller (SOFLC) are obtained via integration with the popular generalized predictive control (GPC) algorithm using a Takagi-Sugeno-Kang (TSK)-based controlled autoregressive integrated moving average (CARIMA) model structure. In this approach, GPC replaces the performance index (PI) table which, as an incremental model, is traditionally used to discover, amend, and delete the rules. Because the GPC sequence is computed using predicted future outputs, the new hybrid approach rewards the time-delay very well. The new generic approach, named generalized predictive self-organizing fuzzy logic control (GPSOFLC), is simulated on a well-known nonlinear chemical process, the distillation column, and is shown to produce an effective fuzzy rule-base in both qualitative (minimum number of generated rules) and quantitative (good rules) terms. PMID:18238192

  18. Prediction of force and acceleration control spectra for Space Shuttle orbiter sidewall-mounted payloads

    NASA Technical Reports Server (NTRS)

    Hipol, Philip J.

    1990-01-01

    The development of force and acceleration control spectra for vibration testing of Space Shuttle (STS) orbiter sidewall-mounted payloads requiresreliable estimates of the sidewall apparent weight and free (i.e. unloaded) vibration during lift-off. The feasibility of analytically predicting these quantities has been investigated through the development and analysis of a finite element model of the STS cargo bay. Analytical predictions of the sidewall apparent weight were compared with apparent weight measurements made on OV-101, and analytical predictions of the sidewall free vibration response during lift-off were compared with flight measurements obtained from STS-3 and STS-4. These analysis suggest that the cargo bay finite element model has potential application for the estimation of force and acceleration control spectra for STS sidewall-mounted payloads.

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

    PubMed

    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

  20. 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

  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. 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…

  4. Development of advanced stability theory suction prediction techniques for laminar flow control. [on swept wings

    NASA Technical Reports Server (NTRS)

    Srokowski, A. J.

    1978-01-01

    The problem of obtaining accurate estimates of suction requirements on swept laminar flow control wings was discussed. A fast accurate computer code developed to predict suction requirements by integrating disturbance amplification rates was described. Assumptions and approximations used in the present computer code are examined in light of flow conditions on the swept wing which may limit their validity.

  5. 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…

  6. 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…

  7. Demonstration of leapfrogging for implementing nonlinear model predictive control on a heat exchanger.

    PubMed

    Sridhar, Upasana Manimegalai; Govindarajan, Anand; Rhinehart, R Russell

    2016-01-01

    This work reveals the applicability of a relatively new optimization technique, Leapfrogging, for both nonlinear regression modeling and a methodology for nonlinear model-predictive control. Both are relatively simple, yet effective. The application on a nonlinear, pilot-scale, shell-and-tube heat exchanger reveals practicability of the techniques. PMID:26606850

  8. Adaptive cruise control with stop&go function using the state-dependent nonlinear model predictive control approach.

    PubMed

    Shakouri, Payman; Ordys, Andrzej; Askari, Mohamad R

    2012-09-01

    In the design of adaptive cruise control (ACC) system two separate control loops - an outer loop to maintain the safe distance from the vehicle traveling in front and an inner loop to control the brake pedal and throttle opening position - are commonly used. In this paper a different approach is proposed in which a single control loop is utilized. The objective of the distance tracking is incorporated into the single nonlinear model predictive control (NMPC) by extending the original linear time invariant (LTI) models obtained by linearizing the nonlinear dynamic model of the vehicle. This is achieved by introducing the additional states corresponding to the relative distance between leading and following vehicles, and also the velocity of the leading vehicle. Control of the brake and throttle position is implemented by taking the state-dependent approach. The model demonstrates to be more effective in tracking the speed and distance by eliminating the necessity of switching between the two controllers. It also offers smooth variation in brake and throttle controlling signal which subsequently results in a more uniform acceleration of the vehicle. The results of proposed method are compared with other ACC systems using two separate control loops. Furthermore, an ACC simulation results using a stop&go scenario are shown, demonstrating a better fulfillment of the design requirements. PMID:22704362

  9. Mixed H2/H∞ robust model predictive control with saturated inputs

    NASA Astrophysics Data System (ADS)

    Huang, He; Li, Dewei; Xi, Yugeng

    2014-12-01

    In this paper, we investigate the mixed H2/H∞ robust model predictive control (RMPC) for polytopic uncertain systems, which refers to the infinite horizon optimal guaranteed cost control (OGCC). To fully use the capability of actuators, we adopt a saturating feedback control law as the control strategy of RMPC. As the saturating feedback control law can be effectively represented by the convex hull of a group of auxiliary linear feedback laws, the auxiliary feedback laws allow us to design the actual feedback control law without consideration of the input constraints directly to achieve the improved performance. Moreover, we suggest the relative weights on the actual and auxiliary feedback laws to the RMPC, which in turn improves the closed-loop system performance. Furthermore, an off-line design of the proposed RMPC is also developed to make it more practical. Numerical studies demonstrate the effectiveness of the proposed algorithm.

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. The Effects of Predictive Solutions on Training Time and Post-Training Performance for Control Systems with Human Operators.

    ERIC Educational Resources Information Center

    Myers, David Lee

    The effect of predictive solutions on training time (speed) and subsequent performance in a complex manual control system was investigated. A control system with a slow and complex response to the input signals was formulated. Fifty control operators, 25 with the aid of predictive solutions and 25 without, were tested; the mean performances of the…

  15. 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

  16. Prediction of Abstinence, Controlled Drinking, and Heavy Drinking Outcomes Following Behavioral Self-Control Training.

    ERIC Educational Resources Information Center

    Miller, William R.; Joyce, Mark A.

    1979-01-01

    Examined prognostic value of client characteristics for problem drinkers treated with an initial goal of controlled drinking. Clients achieving moderation had less severe symptoms and less family history of problem drinking than abstainers or uncontrolled cases. Females were more successful in attaining moderation. Males were overrepresented among…

  17. 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. PMID:16294779

  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. PMID:18263439

  19. Decision tree-based learning to predict patient controlled analgesia consumption and readjustment

    PubMed Central

    2012-01-01

    Background Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment. Methods The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction. Results The prediction accuracies of total analgesic consumption (continuous dose and PCA dose) and PCA analgesic requirement (PCA dose only) by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naïve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works. Conclusion This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA demands over time. We analyzed

  20. 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

  1. 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

  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. 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.

  4. Model predictive control application to spacecraft rendezvous in mars sample return scenario

    NASA Astrophysics Data System (ADS)

    Saponara, M.; Barrena, V.; Bemporad, A.; Hartley, E. N.; Maciejowski, J.; Richards, A.; Tramutola, A.; Trodden, P.

    2013-12-01

    Model Predictive Control (MPC) is an optimization-based control strategy that is considered extremely attractive in the autonomous space rendezvous scenarios. The Online Recon¦guration Control System and Avionics Architecture (ORCSAT) study addresses its applicability in Mars Sample Return (MSR) mission, including the implementation of the developed solution in a space representative avionic architecture system. With respect to a classical control solution High-integrity Autonomous RendezVous and Docking control system (HARVD), MPC allows a signi¦cant performance improvement both in trajectory and in propellant save. Furthermore, thanks to the online optimization, it allows to identify improvements in other areas (i. e., at mission de¦nition level) that could not be known a priori.

  5. Avionic Architecture for Model Predictive Control Application in Mars Sample & Return Rendezvous Scenario

    NASA Astrophysics Data System (ADS)

    Saponara, M.; Tramutola, A.; Creten, P.; Hardy, J.; Philippe, C.

    2013-08-01

    Optimization-based control techniques such as Model Predictive Control (MPC) are considered extremely attractive for space rendezvous, proximity operations and capture applications that require high level of autonomy, optimal path planning and dynamic safety margins. Such control techniques require high-performance computational needs for solving large optimization problems. The development and implementation in a flight representative avionic architecture of a MPC based Guidance, Navigation and Control system has been investigated in the ESA R&T study “On-line Reconfiguration Control System and Avionics Architecture” (ORCSAT) of the Aurora programme. The paper presents the baseline HW and SW avionic architectures, and verification test results obtained with a customised RASTA spacecraft avionics development platform from Aeroflex Gaisler.

  6. Rumination and self-control interact to predict bulimic symptomatology in college students.

    PubMed

    Breithaupt, Lauren; Rallis, Bethany; Mehlenbeck, Robyn; Kleiman, Evan

    2016-08-01

    Recent studies suggest that a ruminative response style may contribute to the development and maintenance of Bulimia nervosa. However it is not clear what factors may contribute to the relationship between rumination and BN. One factor may be self-control, as studies suggest that BN symptomatology relates to deficits in self-control. In the present study, we hypothesized that the association between rumination and BN symptomatology would be the strongest among individuals with lower self-control relative to those with higher self-control. Participants were 353 students at a large university. Participants completed measures of self-control, rumination, and eating disorder symptomology as part of an online study. A hierarchical regression supported an interaction between rumination and self-control predicting bulimic symptomatology, controlling for BMI. Individuals with higher levels of rumination presented more bulimic symptoms if they also had lower levels of self-control, supporting our hypothesis. Based on these findings, assessing rumination in conjunction with self-control among individuals who present with eating concerns may help to direct treatment. Additionally, clinical interventions increasing self-control may also alleviate some BN symptoms in ruminators. PMID:27033968

  7. 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

  8. Validation of a multigenic model to predict seizure control in newly treated epilepsy.

    PubMed

    Shazadi, Kanvel; Petrovski, Slavé; Roten, Annie; Miller, Hugh; Huggins, Richard M; Brodie, Martin J; Pirmohamed, Munir; Johnson, Michael R; Marson, Anthony G; O'Brien, Terence J; Sills, Graeme J

    2014-12-01

    A multigenic classifier based on five single nucleotide polymorphisms (SNPs) was previously reported to predict treatment response in an Australian newly-diagnosed epilepsy cohort using a k-nearest neighbour (kNN) algorithm. We assessed the validity of this classifier in predicting response to initial antiepileptic drug (AED) treatment in two UK cohorts of newly-diagnosed epilepsy and investigated the utility of these five SNPs in predicting seizure control in general. The original Australian cohort constituted the training set for the classifier and was used to predict response to the first well-tolerated AED monotherapy in independently recruited UK cohorts (Glasgow, n=281; SANAD, n=491). A "leave-one-out" cross-validation was also employed, with training sets derived internally from the UK datasets. The multigenic classifier using the Australian cohort as the training set was unable to predict treatment response in either UK cohort. In the "leave-one-out" analysis, the five SNPs collectively predicted treatment response in both Glasgow and SANAD patients prescribed either carbamazepine or valproate (Glasgow OR=3.1, 95% CI=1.4-6.6, p=0.018; SANAD OR=2.8, 95% CI=1.3-6.1, p=0.048), but not those receiving lamotrigine (Glasgow OR=1.3, 95% CI=0.6-2.8, p=1.0; SANAD OR=2.2, 95% CI=0.9-5.4, p=0.36) or other AEDs (Glasgow OR=0.6, 95% CI=0.2-2.0, p=1.0; SANAD OR=1.9, 95% CI=0.9-4.2, p=0.36). The Australian-based multigenic kNN model is not predictive of initial treatment response in UK cohorts of newly-diagnosed epilepsy. However, the five SNPs identified in the original Australian study appear to collectively have a predictive influence in UK patients prescribed either carbamazepine or valproate. PMID:25282706

  9. Model-based planning and real-time predictive control for laser-induced thermal therapy

    PubMed Central

    Feng, Yusheng; Fuentes, David

    2014-01-01

    In this article, the major idea and mathematical aspects of model-based planning and real-time predictive control for laser-induced thermal therapy (LITT) are presented. In particular, a computational framework and its major components developed by authors in recent years are reviewed. The framework provides the backbone for not only treatment planning but also real-time surgical monitoring and control with a focus on MR thermometry enabled predictive control and applications to image-guided LITT, or MRgLITT. Although this computational framework is designed for LITT in treating prostate cancer, it is further applicable to other thermal therapies in focal lesions induced by radio-frequency (RF), microwave and high-intensity-focused ultrasound (HIFU). Moreover, the model-based dynamic closed-loop predictive control algorithms in the framework, facilitated by the coupling of mathematical modelling and computer simulation with real-time imaging feedback, has great potential to enable a novel methodology in thermal medicine. Such technology could dramatically increase treatment efficacy and reduce morbidity. PMID:22098360

  10. 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

  11. 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

  12. 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

  13. Variability in Phenylalanine Control Predicts IQ and Executive Abilities in Children with Phenylketonuria

    PubMed Central

    Hood, Anna; Grange, Dorothy K.; Christ, Shawn E.; Steiner, Robert; White, Desirée A.

    2014-01-01

    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 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 yrs. 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

  14. 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

  15. 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

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

    NASA Astrophysics Data System (ADS)

    Xavier, Marcelo A.; Trimboli, M. Scott

    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.

  17. 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.

  18. 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

  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. Predicting the flying performance of thermal flying-height control sliders in hard disk drives

    NASA Astrophysics Data System (ADS)

    Liu, Nan; Zheng, Jinglin; Bogy, David B.

    2010-07-01

    Thermal flying-height control (TFC) sliders have been recently used in commercial hard disk drives (HDDs) to increase the HDDs' capacity. The design of this new class of sliders depends on the numerical prediction of their flying performance, which requires a model for heat flux on the surface of the slider facing the disk. The currently widely used heat flux model is based on a first order slip theory and is believed to lack sufficient accuracy due to its limitation of applicability. This paper implements an improved heat flux model and compares numerical predictions of a TFC slider's flying performance based on these two models with experiments. It is found that the numerical prediction based on the currently used model has a relative error less than 10% for a state-of-the-art TFC slider. It is suggested that the currently used model might cause large errors for the sliders which do not have a pressure peak near the transducer.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. A predictive biophysical model of translational coupling to coordinate and control protein expression in bacterial operons

    PubMed Central

    Tian, Tian; Salis, Howard M.

    2015-01-01

    Natural and engineered genetic systems require the coordinated expression of proteins. In bacteria, translational coupling provides a genetically encoded mechanism to control expression level ratios within multi-cistronic operons. We have developed a sequence-to-function biophysical model of translational coupling to predict expression level ratios in natural operons and to design synthetic operons with desired expression level ratios. To quantitatively measure ribosome re-initiation rates, we designed and characterized 22 bi-cistronic operon variants with systematically modified intergenic distances and upstream translation rates. We then derived a thermodynamic free energy model to calculate de novo initiation rates as a result of ribosome-assisted unfolding of intergenic RNA structures. The complete biophysical model has only five free parameters, but was able to accurately predict downstream translation rates for 120 synthetic bi-cistronic and tri-cistronic operons with rationally designed intergenic regions and systematically increased upstream translation rates. The biophysical model also accurately predicted the translation rates of the nine protein atp operon, compared to ribosome profiling measurements. Altogether, the biophysical model quantitatively predicts how translational coupling controls protein expression levels in synthetic and natural bacterial operons, providing a deeper understanding of an important post-transcriptional regulatory mechanism and offering the ability to rationally engineer operons with desired behaviors. PMID:26117546

  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. PMID:26724992

  8. 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. PMID:21963108

  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. Remotely controlled mandibular positioner predicts efficacy of oral appliances in sleep apnea.

    PubMed

    Tsai, Willis H; Vazquez, Juan-Carlos; Oshima, Tsutomu; Dort, Leslie; Roycroft, Brian; Lowe, Alan A; Hajduk, Eric; Remmers, John E

    2004-08-15

    Anterior mandibular positioners (AMPs) have become increasingly popular as alternatives to continuous positive airway pressure for the treatment of obstructive sleep apnea. However, widespread acceptance of AMP is limited by an efficacy rate of 50-80% and an inability to predict which patients will respond to therapy. We evaluated 23 patients with obstructive sleep apnea (respiratory disturbance index [RDI] >/= 15 h(-1)) with a remotely controlled mandibular positioner (RCMP), a temporary oral appliance that can advance or retract the mandible in a process analogous to changing the mask pressure during a continuous positive airway pressure titration study. We hypothesized that the elimination of respiratory events and significant nocturnal oxygen desaturation during an RCMP overnight study would predict AMP efficacy, as defined by an absolute reduction in RDI to less than 15 h(-1), a relative reduction in RDI of more than 30% from baseline, and a subjective improvement in symptoms. AMP compliance was 82%, and therapeutic efficacy was 53%. Among compliant patients, the positive and negative predictive value of an RCMP study in predicting AMP treatment success was 90% and 89%, respectively. An overnight RCMP study is highly predictive of AMP response. PMID:15105166