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Predictive Control (predictive + control)

Distribution by Scientific Domains
Engineering56%
Chemistry34%
3 Other Domains10%

Kinds of Predictive Control

  • generalized predictive control
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  • model predictive control
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  • nonlinear predictive control
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    Terms modified by Predictive Control

  • predictive control approach
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  • predictive control design
    		•
  • predictive control strategy
    		•

    Selected Abstracts

    Achieving state estimation equivalence for misassigned disturbances in offset-free model predictive control

    AICHE JOURNAL, Issue 2 2009
    Murali R. Rajamani
    Abstract Integrated white noise disturbance models are included in advanced control strategies, such as Model Predictive Control, to remove offset when there are unmodeled disturbances or plant/model mismatch. These integrating disturbances are usually modeled to enter either through the plant inputs or the plant outputs or partially through both. There is currently a lack of consensus in the literature on the best choice for the structure of this disturbance model to obtain good feedback control. We show that the choice of the disturbance model does not affect the closed- loop performance if appropriate covariances are used in specifying the state estimator. We also present a data based autocovariance technique to estimate the appropriate covariances regardless of the plant's true unknown disturbance source. The covariances estimated using the autocovariance technique and the resulting estimator gain are shown to compensate for an incorrect choice of the source of the disturbance in the disturbance model. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source]

    Non-Linear Model Predictive Control: A Personal Retrospective,

    THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 4 2007
    B. Wayne Bequette
    Abstract An overview of non-linear model predictive control (NMPC) is presented, with an extreme bias towards the author's experiences and published results. Challenges include multiple solutions (from non-convex optimization problems), and divergence of the model and plant outputs when the constant additive output disturbance (the approach of dynamic matrix control, DMC) is used. Experiences with the use of fundamental models, multiple linear models (MMPC), and neural networks are reviewed. Ongoing work in unmeasured disturbance estimation, prediction and rejection is also discussed. On présente un aperçu général du contrôle prédictif par modèles non linéaires (NMPC), en mettant l'accent en particulier sur les expériences des auteurs et les résultats publiés. Les défis incluent des solutions multiples (à partir des problèmes d'optimisation non convexes), ainsi que la divergence entre les sorties de modèle et d'installation lorsque la perturbation de sortie additive constante (la méthode du contrôle de matrice dynamique, DMC) est utilisée. Les expériences avec les modèles fondamentaux, les modèles linéaires multiples (MMPC) et les réseaux neuronaux sont examinées. Le travail actuellement mené sur l'estimation, la prédiction et le rejet des perturbations non mesurées est également examiné. [source]

    Modeling And Solving An Engine Intake Manifold With Turbo Charger For Predictive Control

    ASIAN JOURNAL OF CONTROL, Issue 3 2006
    Long Xie
    ABSTRACT In this paper, we build the intake manifold model of an engine with a turbo charger and develop a high speed calculation algorithm for model-based predictive control in real time. The model is built according to the analysis of its thermodynamic and hydrodynamic characteristics and the sampled experiment data. The model equations are presented as a set of differential equations with condition selection (bifurcation) on the right hand side. The switching surface is divided into two parts, sliding and crossing. The sliding mode on the switching surface is analyzed in detail, and a calculation algorithm is proposed to remove illegal crossing caused by the numerical errors on this surface. Also, the control formula and the condition guiding the bifurcation between these two parts are demonstrated. Using this method, we can solve this model over the entire region of input throttle angles, the stability is greatly increased, and the calculation time is greatly reduced for real time control systems. [source]

    Simple Recurrent Neural Network-Based Adaptive Predictive Control for Nonlinear Systems

    ASIAN JOURNAL OF CONTROL, Issue 2 2002
    Xiang Li
    ABSTRACT Making use of the neural network universal approximation ability, a nonlinear predictive control scheme is studied in this paper. On the basis of a uniform structure of simple recurrent neural networks, a one-step neural predictive controller (OSNPC) is designed. The whole closed-loop system's asymptotic stability and passivity are discussed, and stable conditions for the learning rate are determined based on the Lyapunov stability theory for the whole neural system. The effectiveness of OSNPC is verified via exhaustive simulations. [source]

    Cross-directional Estimation and Predictive Control of Paper Machines Using DWT

    ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 1-2 2001
    Zhihuan Song
    This paper proposes a novel approach for cross-directional (CD) estimation, modeling and control on paper machines based on discrete wavelet transforms (DWT). The CD basis weight variations are approximated at various resolutions using wavelet multiresolution analysis (WMRA). The controllable component of CD variations can be extracted from the original samples by choosing a suitable threshold resolution. An acceptable response model describing the relationship between the settings of the slice-screws to the basis weight profile is obtained. The controller synthesis, model prediction, optimization and parameter estimation are all performed in the DWT domain. The size of optimization and control problems associated with such large dimensions can be significantly reduced. [source]

    Cybernetic Model Predictive Control of a Continuous Bioreactor with Cell Recycle

    BIOTECHNOLOGY PROGRESS, Issue 5 2003
    Kapil G. Gadkar
    The control of poly-,-hydroxybutyrate (PHB) productivity in a continuous bioreactor with cell recycle is studied by simulation. A cybernetic model of PHB synthesis in Alcaligenes eutrophus is developed. Model parameters are identified using experimental data, and simulation results are presented. The model is interfaced to a multirate model predictive control (MPC) algorithm. PHB productivity and concentration are controlled by manipulating dilution rate and recycle ratio. Unmeasured time varying disturbances are imposed to study regulatory control performance, including unreachable setpoints. With proper controller tuning, the nonlinear MPC algorithm can track productivity and concentration setpoints despite a change in the sign of PHB productivity gain with respect to dilution rate. It is shown that the nonlinear MPC algorithm is able to track the maximum achievable productivity for unreachable setpoints under significant process/model mismatch. The impact of model uncertainty upon controller performance is explored. The multirate MPC algorithm is tested using three controllers employing models that vary in complexity of regulation. It is shown that controller performance deteriorates as a function of decreasing biological complexity. [source]

    Nonlinear Predictive Control of Fed-Batch Cultures of Escherichia coli

    CHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 7 2010
    S. Tebbani
    Abstract A strategy for controlling a fed-batch Escherichia coli culture is described to maintain the culture at the boundary between oxidative and oxido-fermentative regimes. A nonlinear predictive controller is designed to regulate the acetate concentration, constraining the feed rate to follow an optimal reference profile which maximizes the biomass growth. For the sake of simplicity and efficiency, the original problem is converted into an unconstrained nonlinear programming problem, solved by control vector parameterization techniques. The robustness of the structure is further improved by explicitly including the difference between system and model prediction. A robustness study based on a Monte Carlo approach is used to evaluate the performance of the proposed controller. This control law is finally compared to the generic model control strategy. [source]

    Predictive control of parabolic PDEs with state and control constraints

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 16 2006
    Stevan Dubljevic
    Abstract This work focuses on predictive control of linear parabolic partial differential equations (PDEs) with state and control constraints. Initially, the PDE is written as an infinite-dimensional system in an appropriate Hilbert space. Next, modal decomposition techniques are used to derive a finite-dimensional system that captures the dominant dynamics of the infinite-dimensional system, and express the infinite-dimensional state constraints in terms of the finite-dimensional system state constraints. A number of model predictive control (MPC) formulations, designed on the basis of different finite-dimensional approximations, are then presented and compared. The closed-loop stability properties of the infinite-dimensional system under the low order MPC controller designs are analysed, and sufficient conditions that guarantee stabilization and state constraint satisfaction for the infinite-dimensional system under the reduced order MPC formulations are derived. Other formulations are also presented which differ in the way the evolution of the fast eigenmodes is accounted for in the performance objective and state constraints. The impact of these differences on the ability of the predictive controller to enforce closed-loop stability and state constraints satisfaction in the infinite-dimensional system is analysed. Finally, the MPC formulations are applied through simulations to the problem of stabilizing the spatially-uniform unstable steady-state of a linear parabolic PDE subject to state and control constraints. Copyright © 2006 John Wiley & Sons, Ltd. [source]

    Experience with model predictive control in the undergraduate laboratory

    COMPUTER APPLICATIONS IN ENGINEERING EDUCATION, Issue 1 2005
    Kenneth R. Muske
    Abstract A model predictive control experiment for an undergraduate senior laboratory course is outlined in this article. The process under study is a continuous stirred-tank heater and the control objective is to control the water temperature in the tank. A discrete, dynamic, physical model of this process is used in the controller. The model predictive control algorithm is a single-move, analytical controller that matches the model predicted temperature to a reference temperature trajectory at a single time in the future. A series of different control experiments using this algorithm are described and examples of each are presented. © 2005 Wiley Periodicals, Inc. Comput Appl Eng Educ 13: 40,47, 2005; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20028 [source]

    School-aged children after the end of successful treatment of non-central nervous system cancer: longitudinal assessment of health-related quality of life, anxiety and coping

    EUROPEAN JOURNAL OF CANCER CARE, Issue 4 2009
    H. MAURICE-STAM phd
    The aim of the study was to investigate: (1) health-related quality of life (HRQoL) and anxiety in school-aged cancer survivors during the first 4 years of continuous remission after the end of treatment; and (2) correlations of disease-related coping with HRQoL and anxiety. A total of 76 survivors aged 8,15 years completed questionnaires about HRQoL, anxiety and disease-related cognitive coping at one to five measurement occasions. Their HRQoL was compared with norm data, 2 months (n = 49) and 1 year (n = 41), 2 years (n = 41), 3 years (n = 42) and 4 years (n = 27) after treatment. Through longitudinal mixed models analyses it was investigated to what extent disease-related cognitive coping was associated with HRQoL and anxiety over time, independent of the impact of demographic and medical variables. Survivors reported worse Motor Functioning (HRQoL) 2 months after the end of treatment, but from 1 year after treatment they did no longer differ from the norm population. Lower levels of anxiety were associated with male gender, being more optimistic about the further course of the disease (predictive control) and less searching for information about the disease (interpretative control). Stronger reliance on the physician (vicarious control) was associated with better mental HRQoL. As a group, survivors regained good HRQoL from 1 year after treatment. Monitoring and screening survivors are necessary to be able to trace the survivors at risk of worse HRQoL. [source]

    Improvement of tracking performance in designing a GPC-based PID controller using a time-varying proportional gain

    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 4 2006
    Takao Sato
    An effective design method of a proportional-integral-derivative (PID) controller is proposed. The PID parameters of the PID controller are designed on the basis of a generalized predictive control (GPC) law. The PID controller has a time-varying proportional gain, and the PID parameters are designed using the future reference trajectory of the GPC. Finally, numerical examples are shown for illustrating the effectiveness of the proposed method. © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source]

    Application of model-free LQG subspace predictive control to TCP congestion control

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 6 2008
    Belinda A. Chiera
    Abstract We investigate the application of a model-free linear quadratic Gaussian (LQG) subspace-based predictive controller to Internet congestion control. Specifically, we consider a classically designed LQG linear congestion controller with a non-standard performance index and determine whether a model-free controller is a viable alternative in this instance. We employ the model-free subspace predictive controller methodology which we customize for end-to-end transmission control protocol (TCP) congestion control. A series of network simulations support the use of the more easily implementable model-free controller over its classical analogue. We further demonstrate that the model-free controller provides increased stability under transient network conditions when compared with the first feedback congestion controller, TCP Vegas. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Hybrid adaptive predictive control for a dynamic pickup and delivery problem including traffic congestion

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 2 2008
    Cristián E. Cortés
    Abstract This paper presents a hybrid adaptive predictive control approach to incorporate future information regarding unknown demand and expected traffic conditions, in the context of a dynamic pickup and delivery problem with fixed fleet size. As the routing problem is dynamic, several stochastic effects have to be considered within the analytical expression of the dispatcher assignment decision objective function. This paper is focused on two issues: one is the extra cost associated with potential rerouting arising from unknown requests in the future, and the other is the potential uncertainty in travel time coming from non-recurrent traffic congestion from unexpected incidents. These effects are incorporated explicitly in the objective function of the hybrid predictive controller. In fact, the proposed predictive control strategy is based on a multivariable model that includes both discrete/integer and continuous variables. The vehicle load and the sequence of stops correspond to the discrete/integer variable, adding the vehicle position as an indicator of the traffic congestion conditions. The strategy is analyzed under two scenarios. The first one considers a predictable congestion obtained using historical data (off-line method) requiring a predictive model of velocities distributed over zones. The second scenario that accepts unpredictable congestion events generates a more complex problem that is managed by using both fault detection and isolation and fuzzy fault-tolerant control approaches. Results validating these approaches are presented through a simulated numerical example. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Plant-wide control of a hybrid process

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 2 2008
    C. de Prada
    Abstract This paper deals with the model predictive control (MPC) of an industrial hybrid process where continuous and batch units operate jointly: the crystallization section of a sugar factory. The paper describes a plant-wide predictive controller that takes into account, both, continuous objectives and manipulated variables, as well as those related to the discrete operation and logic of the batch units. The MPC is formulated with time events, so that a more efficient NLP optimization technique, instead of MINLP, could be applied. Adaptation is provided by model updating and error estimation. Results of the controller operation in an industrial simulator are provided. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Adaptive model predictive control for co-ordination of compression and friction brakes in heavy duty vehicles

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2006
    Ardalan Vahidi
    Abstract In this paper, an adaptive model predictive control scheme is designed for speed control of heavy vehicles. The controller co-ordinates use of compression brakes and friction brakes on downhill slopes. Moreover, the model predictive controller takes the actuator constraints into account. A recursive least square scheme with forgetting is used in parallel with the controller to update the estimates of vehicle mass and road grade. The adaptation improved the model predictive controller. Also online estimation of the road grade enhanced the closed-loop performance further by contributing through feedforward control. Simulations of realistic driving scenarios with a validated longitudinal vehicle model are used throughout this paper to illustrate the benefits of co-ordinating the two braking mechanisms and influence of unknown vehicle mass and road grade. Copyright © 2006 John Wiley & Sons, Ltd. [source]

    Online trained support vector machines-based generalized predictive control of non-linear systems

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2006
    S. Iplikci
    Abstract In this work, an online support vector machines (SVM) training method (Neural Comput. 2003; 15: 2683,2703), referred to as the accurate online support vector regression (AOSVR) algorithm, is embedded in the previously proposed support vector machines-based generalized predictive control (SVM-Based GPC) architecture (Support vector machines based generalized predictive control, under review), thereby obtaining a powerful scheme for controlling non-linear systems adaptively. Starting with an initially empty SVM model of the unknown plant, the proposed online SVM-based GPC method performs the modelling and control tasks simultaneously. At each iteration, if the SVM model is not accurate enough to represent the plant dynamics at the current operating point, it is updated with the training data formed by persistently exciting random input signal applied to the plant, otherwise, if the model is accepted as accurate, a generalized predictive control signal based on the obtained SVM model is applied to the plant. After a short transient time, the model can satisfactorily reflect the behaviour of the plant in the whole phase space or operation region. The incremental algorithm of AOSVR enables the SVM model to learn the new training data pair, while the decremental algorithm allows the SVM model to forget the oldest training point. Thus, the SVM model can adapt the changes in the plant and also in the operating conditions. The simulation results on non-linear systems have revealed that the proposed method provides an excellent control quality. Furthermore, it maintains its performance when a measurement noise is added to the output of the underlying system. Copyright © 2006 John Wiley & Sons, Ltd. [source]

    Constrained closed-loop control of depth of anaesthesia in the operating theatre during surgery

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 5 2005
    M. Mahfouf
    Abstract The constrained version of generalized predictive control (GPC) which employs the quadratic programming (QP) approach is evaluated for on-line administration of an anaesthetic drug in the operating theatre during surgery. In the first instance, a patient simulator was developed using a physiological model of the patient and the necessary control software was validated via a series of extensive simulation experiments. Such a validated system was then transferred into the operating theatre for a series of clinical evaluation trials. The clinical trials, which were performed with little involvement of the design engineer, led to a good regulation of unconsciousness using fixed-parameters as well the adaptive version of the algorithm. Furthermore, the constrained algorithm displayed good robustness properties against disturbances such as high stimulus levels and allowed for safe and economically effective administration of the anaesthetic agent isoflurane. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    Integrating fault detection and isolation with model predictive control

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2005
    Barry Lennox
    Abstract This paper illustrates how the application of partial least squares (PLS) can be extended to provide an integrated solution to fault detection and isolation, inferential estimation and model predictive control. It is shown that if PLS is used to identify a dynamic model of a plant then the latent variables of the model can identify the suitability of using this model under current conditions. This functionality enables automated model switching in piecewise linear systems. A further advantage of the proposed technique is that the inner structure of the model can be used to provide fault detection and isolation capabilities. By extending the approach to control systems and integrating a dynamic model, identified using the PLS algorithm, within a model predictive controller, similar benefits, such as automatic model selection can be achieved for the control system. The proposed approach is illustrated through its application to the Tennessee Eastman challenge process. Copyright © 2004 John Wiley & Sons, Ltd. [source]

    Nonlinear predictive control of smooth nonlinear systems based on Volterra models.

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 16 2010
    Application to a pilot plant
    Abstract There is a large demand to apply nonlinear algorithms to control nonlinear systems. With algorithms considering the process nonlinearities, better control performance is expected in the whole operating range than with linear control algorithms. Three predictive control algorithms based on a Volterra model are considered. The iterative predictive control algorithm to solve the complete nonlinear problem uses the non-autoregressive Volterra model calculated from the identified autoregressive Volterra model. Two algorithms for a reduced nonlinear optimization problem are considered for the unconstrained case, where an analytic control expression can be given. The performance of the three algorithms is analyzed and compared for reference signal tracking and disturbance rejection. The algorithms are applied and compared in simulation to control a Wiener model, and are used for real-time control of a chemical pilot plant. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    Model predictive control for networked control systems

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 9 2009
    Jing Wu
    Abstract This paper investigates the problem of model predictive control for a class of networked control systems. Both sensor-to-controller and controller-to-actuator delays are considered and described by Markovian chains. The resulting closed-loop systems are written as jump linear systems with two modes. The control scheme is characterized as a constrained delay-dependent optimization problem of the worst-case quadratic cost over an infinite horizon at each sampling instant. A linear matrix inequality approach for the controller synthesis is developed. It is shown that the proposed state feedback model predictive controller guarantees the stochastic stability of the closed-loop system. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Fast implementations and rigorous models: Can both be accommodated in NMPC?

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 8 2008
    Victor M. Zavala
    Abstract In less than two decades, nonlinear model predictive control has evolved from a conceptual framework to an attractive, general approach for the control of constrained nonlinear processes. These advances were realized both through better understanding of stability and robustness properties as well as improved algorithms for dynamic optimization. This study focuses on recent advances in optimization formulations and algorithms, particularly for the simultaneous collocation-based approach. Here, we contrast this approach with competing approaches for online application and discuss further advances to deal with applications of increasing size and complexity. To address these challenges, we adapt the real-time iteration concept, developed in the context of multiple shooting (Real-Time PDE-Constrained Optimization. SIAM: Philadelphia, PA, 2007; 25,52, 3,24), to a collocation-based approach with a full-space nonlinear programming solver. We show that straightforward sensitivity calculations from the Karush,Kuhn,Tucker system also lead to a real-time iteration strategy, with both direct and shifted variants. This approach is demonstrated on a large-scale polymer process, where online calculation effort is reduced by over two orders of magnitude. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    An online active set strategy to overcome the limitations of explicit MPC

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 8 2008
    H. J. Ferreau
    Abstract Nearly all algorithms for linear model predictive control (MPC) either rely on the solution of convex quadratic programs (QPs) in real time, or on an explicit precalculation of this solution for all possible problem instances. In this paper, we present an online active set strategy for the fast solution of parametric QPs arising in MPC. This strategy exploits solution information of the previous QP under the assumption that the active set does not change much from one QP to the next. Furthermore, we present a modification where the CPU time is limited in order to make it suitable for strict real-time applications. Its performance is demonstrated with a challenging test example comprising 240 variables and 1191 inequalities, which depends on 57 parameters and is prohibitive for explicit MPC approaches. In this example, our strategy allows CPU times of well below 100 ms per QP and was about one order of magnitude faster than a standard active set QP solver. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Circadian phase entrainment via nonlinear model predictive control

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 17 2007
    N. Bagheri
    Abstract A nonlinear model predictive control algorithm is developed to investigate the phase-resetting properties of robust nonlinear biological oscillators; in particular, those of the circadian rhythm. This pacemaker is an autonomous biochemical oscillator with a free-running period close to 24 h. Research in chronobiology indicates that light stimuli may delay or advance the phase of the oscillator, allowing it to synchronize physiological processes and entrain to the environment. In this paper, a closed-loop optimal phase tracking control algorithm is developed and applied to a mammalian circadian model. The integration of MPC-based light pulses, coupled with environmental light:dark cycles, allows the circadian system to recover phase differences within 1.5 days,a fraction of the natural open-loop simulated mammalian recovery time. Accelerated phase entrainment may alleviate disorders caused by circadian rhythms that are out of phase with the environment, and improve performance. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    A hybrid model predictive control approach to the direct torque control problem of induction motors

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 17 2007
    Georgios Papafotiou
    Abstract Direct torque control (DTC) is a state-of-the-art control methodology for electric motor drives which features favourable control performance and implementation properties. In DTC, the core of the control system is the inverter switching table, and any efforts to enhance the system's performance aim at improving its design. This issue is addressed in this paper, where we propose a new design procedure for the DTC problem. The DTC drive, comprising a two- or three-level dc-link inverter driving a three-phase induction motor, is modelled in the hybrid mixed logical dynamical (MLD) framework, and a constrained finite-time optimal control problem is set up and solved over a receding horizon using model predictive control (MPC). Simulation results are provided and compared to the current industrial standard demonstrating the potential for notable performance improvements. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Support vector machines-based generalized predictive control

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 17 2006
    S. Iplikci
    Abstract In this study, we propose a novel control methodology that introduces the use of support vector machines (SVMs) in the generalized predictive control (GPC) scheme. The SVM regression algorithms have extensively been used for modelling nonlinear systems due to their assurance of global solution, which is achieved by transforming the regression problem into a convex optimization problem in dual space, and also their higher generalization potential. These key features of the SVM structures lead us to the idea of employing a SVM model of an unknown plant within the GPC context. In particular, the SVM model can be employed to obtain gradient information and also it can predict future trajectory of the plant output, which are needed in the cost function minimization block. Simulations have confirmed that proposed SVM-based GPC scheme can provide a noticeably high control performance, in other words, an unknown nonlinear plant controlled by SVM-based GPC can accurately track the reference inputs with different shapes. Moreover, the proposed SVM-based GPC scheme maintains its control performance under noisy conditions. Copyright © 2006 John Wiley & Sons, Ltd. [source]

    Predictive control of parabolic PDEs with state and control constraints

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 16 2006
    Stevan Dubljevic
    Abstract This work focuses on predictive control of linear parabolic partial differential equations (PDEs) with state and control constraints. Initially, the PDE is written as an infinite-dimensional system in an appropriate Hilbert space. Next, modal decomposition techniques are used to derive a finite-dimensional system that captures the dominant dynamics of the infinite-dimensional system, and express the infinite-dimensional state constraints in terms of the finite-dimensional system state constraints. A number of model predictive control (MPC) formulations, designed on the basis of different finite-dimensional approximations, are then presented and compared. The closed-loop stability properties of the infinite-dimensional system under the low order MPC controller designs are analysed, and sufficient conditions that guarantee stabilization and state constraint satisfaction for the infinite-dimensional system under the reduced order MPC formulations are derived. Other formulations are also presented which differ in the way the evolution of the fast eigenmodes is accounted for in the performance objective and state constraints. The impact of these differences on the ability of the predictive controller to enforce closed-loop stability and state constraints satisfaction in the infinite-dimensional system is analysed. Finally, the MPC formulations are applied through simulations to the problem of stabilizing the spatially-uniform unstable steady-state of a linear parabolic PDE subject to state and control constraints. Copyright © 2006 John Wiley & Sons, Ltd. [source]

    Explicit robust model predictive control using recursive closed-loop prediction

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 11 2006
    Danlei Chu
    Abstract In this paper, we develop an algorithm to compute robust MPC explicit solutions for constrained MIMO systems with internal uncertainties and external disturbances. Our approach is based on a recursive closed-loop prediction strategy to realize a finite horizon robust MPC regulator, which has the feature that only one-step state prediction is sufficient to realize robust MPC with an arbitrary prediction horizon. The paper defines a set of recursive sub-optimization problems as multiple-parametric sub-quadratic programming (mp-SQP), and shows that the optimal solution to the mp-SQP problem is piecewise affine functions of states, associated with piece objectives and state critical regions. Asymptotic closed-loop stability can be guaranteed by a terminal weighting and a terminal feedback gain; also by introducing two tuning variables, the algorithm is capable of adjusting the trade-off between system performance and robustness. The state admissible set, which is not easily derived from physical vision, is constructed by two methods: a piecewise linear norm of signals, and polyhedral Voronoi sets. Finally, two simulation examples demonstrate that the algorithm is efficient, feasible and flexible, and can be applied to both slow and fast industrial MIMO systems. Copyright © 2006 John Wiley & Sons, Ltd. [source]

    Extension of efficient predictive control to the nonlinear case

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 5 2005
    M. Bacic
    Abstract The combined use of the closed-loop paradigm, an augmented autonomous state space formulation, partial invariance, local affine difference inclusion, and polytopic invariance are deployed in this paper to propose an NMPC algorithm which, unlike earlier algorithms that have to tackle online a nonlinear non-convex optimization problem, requires the solution of a simple QP. The proposed algorithm is shown to outperform earlier algorithms in respect of size of region of attraction and online computational load. Conversely, for comparable computational loads, the proposed algorithm outperforms earlier algorithms in terms of optimality of dynamic performance. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    Model predictive control for constrained systems with uncertain state-delays

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 17 2004
    Xiao-Bing Hu
    Abstract This paper presents a model predictive control (MPC) algorithm for a class of constrained linear systems with uncertain state-delays. Based on a novel artificial Lyapunov function, a new stabilizing condition dependent of the upper bound of uncertain state-delays is presented in an LMI (linear matrix inequality) form. The proposed MPC algorithm is developed by following the fashion of stability-enforced scheme. The new algorithm is then extended to linear time varying (LTV) systems with multiple uncertain state-delays. Numerical examples illustrate the effectiveness of the new algorithm. Copyright © 2004 John Wiley & Sons, Ltd. [source]

    Discontinuous feedbacks, discontinuous optimal controls, and continuous-time model predictive control

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 3-4 2003
    Fernando A. C. C. Fontes
    Abstract It is known that there is a class of nonlinear systems that cannot be stabilized by a continuous time-invariant feedback. This class includes systems with interest in practice, such as nonholonomic systems, frequently appearing in robotics and other areas. Yet, most continuous-time model predictive control (MPC) frameworks had to assume continuity of the resulting feedback law, being unable to address an important class of nonlinear systems. It is also known that the open-loop optimal control problems that are solved in MPC algorithms may not have, in general, a continuous solution. Again, most continuous-time MPC frameworks had to artificially assume continuity of the optimal controls or, alternatively, impose some demanding assumptions on the data of the optimal control problem to achieve the desired continuity. In this work we analyse the reasons why traditional MPC approaches had to impose the continuity assumptions, the difficulties in relaxing these assumptions, and how the concept of ,sampling feedbacks' combines naturally with MPC to overcome these difficulties. A continuous-time MPC framework using a strictly positive inter-sampling time is argued to be appropriate to use with discontinuous optimal controls and discontinuous feedbacks. The essential features for the stability of such MPC framework are reviewed. Copyright © 2003 John Wiley & Sons, Ltd. [source]