The Experts below are selected from a list of 72273 Experts worldwide ranked by ideXlab platform
Mark Cannon - One of the best experts on this subject based on the ideXlab platform.
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Model Predictive Control classical robust and stochastic
2015Co-Authors: Basil Kouvaritakis, Mark CannonAbstract:For the first time, a textbook that brings together classical Predictive Control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical Predictive Control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust Predictive Control, the text explains how similar guarantees may be obtained for cases in which the Model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive Model uncertainty. Finally, the tube framework is also applied to Model Predictive Control problems involving hard or probabilistic constraints for the cases of multiplicative and stochastic Model uncertainty. The book provides: extensive use of illustrative examples; sample problems; and discussion of novel Control applications such as resource allocation for sustainable development and turbine-blade Control for maximized power capture with simultaneously reduced risk of turbulence-induced damage. Graduate students pursuing courses in Model Predictive Control or more generally in advanced or process Control and senior undergraduates in need of a specialized treatment will find Model Predictive Control an invaluable guide to the state of the art in this important subject. For the instructor it provides an authoritative resource for the construction of courses
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Homothetic tube Model Predictive Control
Automatica, 2012Co-Authors: Sasa V. Rakovic, Basil Kouvaritakis, Rolf Findeisen, Mark CannonAbstract:The robust Model Predictive Control for constrained linear discrete time systems is solved through the development of a homothetic tube Model Predictive Control synthesis method. The method employs several novel features including a more general parameterization of the state and Control tubes based on homothety and invariance, a more flexible form of the terminal constraint set and a relaxation of the Controlled dynamics of the sets that define the state and Control tubes. Under natural assumptions, the proposed method is computationally efficient and it induces strong system theoretic properties.
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Stochastic Model Predictive Control
Constraints, 2011Co-Authors: Basil Kouvaritakis, Mark CannonAbstract:This paper addresses the issue of output feedback Model Predictive Control for linear systems with input constraints and stochastic disturbances. We show that the optimal policy uses the Kalman Filter for state estimation, but the resultant state estimates are not utilized in a Certainty Equivalence Control law.
Masoud Soroush - One of the best experts on this subject based on the ideXlab platform.
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Tuning Guidelines for Model-Predictive Control
Industrial & Engineering Chemistry Research, 2020Co-Authors: Mohammed Alhajeri, Masoud SoroushAbstract:This paper reviews available tuning guidelines for Model-Predictive Control (MPC) from theoretical and practical perspectives. Its primary focus is on the guidelines introduced since the publication of our previous review of MPC tuning guidelines in this same journal in 2010. Since then, new guidelines based on approaches such as pole placement and multiobjective optimization have been proposed, and more autotuning methods have been introduced. This review covers different implementations of MPC such as dynamic matrix Control, generalized Predictive Control, and state-space-Model Predictive Control that requires Kalman filter tuning. The closed-loop performances of a distillation column and the Shell fractionator under Model-Predictive Controllers tuned using four different tuning guidelines are compared through numerical simulations.
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Model Predictive Control tuning methods a review
Industrial & Engineering Chemistry Research, 2010Co-Authors: Jorge L Garriga, Masoud SoroushAbstract:This paper provides a review of the available tuning guidelines for Model Predictive Control, from theoretical and practical perspectives. It covers both popular dynamic matrix Control and generalized Predictive Control implementations, along with the more general state-space representation of Model Predictive Control and other more specialized types, such as max-plus-linear Model Predictive Control. Additionally, a section on state estimation and Kalman filtering is included along with auto (self) tuning. Tuning methods covered range from equations derived from simulation/approximation of the process dynamics to bounds on the region of acceptable tuning parameter values.
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Short horizon nonlinear Model Predictive Control
Proceedings of International Conference on Control Applications, 1995Co-Authors: Masoud Soroush, Costas KravarisAbstract:This article concerns nonlinear Model Predictive Control of the multivariable, open-loop stable processes whose delay-free part is minimum-phase. The Control law is derived by using a discrete-time state-space formulation and the shortest "useful" prediction horizon for each Controlled output. This derivation allows to establish the theoretical connections between the derived nonlinear Model Predictive Control law and the discrete-time globally linearizing Control, and to deduce the conditions for nominal closed-loop stability under the Model Predictive Control law. Under the nonlinear Model Predictive Controller, the closed-loop system is partially governed by the zero dynamics of the process, which is the nonlinear analog of placing a subset of closed-loop poles at the zeros of a process by a Model algorithmic Controller.
Basil Kouvaritakis - One of the best experts on this subject based on the ideXlab platform.
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Model Predictive Control classical robust and stochastic
2015Co-Authors: Basil Kouvaritakis, Mark CannonAbstract:For the first time, a textbook that brings together classical Predictive Control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical Predictive Control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust Predictive Control, the text explains how similar guarantees may be obtained for cases in which the Model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive Model uncertainty. Finally, the tube framework is also applied to Model Predictive Control problems involving hard or probabilistic constraints for the cases of multiplicative and stochastic Model uncertainty. The book provides: extensive use of illustrative examples; sample problems; and discussion of novel Control applications such as resource allocation for sustainable development and turbine-blade Control for maximized power capture with simultaneously reduced risk of turbulence-induced damage. Graduate students pursuing courses in Model Predictive Control or more generally in advanced or process Control and senior undergraduates in need of a specialized treatment will find Model Predictive Control an invaluable guide to the state of the art in this important subject. For the instructor it provides an authoritative resource for the construction of courses
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Homothetic tube Model Predictive Control
Automatica, 2012Co-Authors: Sasa V. Rakovic, Basil Kouvaritakis, Rolf Findeisen, Mark CannonAbstract:The robust Model Predictive Control for constrained linear discrete time systems is solved through the development of a homothetic tube Model Predictive Control synthesis method. The method employs several novel features including a more general parameterization of the state and Control tubes based on homothety and invariance, a more flexible form of the terminal constraint set and a relaxation of the Controlled dynamics of the sets that define the state and Control tubes. Under natural assumptions, the proposed method is computationally efficient and it induces strong system theoretic properties.
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Stochastic Model Predictive Control
Constraints, 2011Co-Authors: Basil Kouvaritakis, Mark CannonAbstract:This paper addresses the issue of output feedback Model Predictive Control for linear systems with input constraints and stochastic disturbances. We show that the optimal policy uses the Kalman Filter for state estimation, but the resultant state estimates are not utilized in a Certainty Equivalence Control law.
Stefano Di Cairano - One of the best experts on this subject based on the ideXlab platform.
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Automotive Applications of Model Predictive Control
Handbook of Model Predictive Control, 2019Co-Authors: Stefano Di Cairano, Ilya V. KolmanovskyAbstract:Model Predictive Control (MPC) has been investigated for a significant number of potential applications to automotive systems. The treatment of these applications has also stimulated several developments in MPC theory, design methods, and algorithms, in recent years.
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Stochastic Model Predictive Control
Handbook of Model Predictive Control, 2018Co-Authors: Ali Mesbah, Ilya V. Kolmanovsky, Stefano Di CairanoAbstract:Stochastic Model Predictive Control (SMPC) accounts for Model uncertainties and disturbances based on their probabilistic description. This chapter considers several formulations and solutions of SMPC problems and discusses some examples and applications in this diverse, complex, and growing field.
James B. Rawlings - One of the best experts on this subject based on the ideXlab platform.
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Feasible Real-time Nonlinear Model Predictive Control
2020Co-Authors: Matthew J. Tenny, James B. Rawlings, Rahul BindlishAbstract:This paper discusses an algorithm for efficiently calculating the Control moves for constrained nonlinear Model Predictive Control. The approach focuses on real-time optimization strategies that maintain feasibility with respect to the Model and constraints at each iteration, yielding a stable technique suitable for suboptimal Model Predictive Control of nonlinear process. We present a simulation to illustrate the performance of our method.
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cooperative distributed Model Predictive Control
Systems & Control Letters, 2010Co-Authors: Brett T Stewart, James B. Rawlings, Aswin N Venkat, Stephen J Wright, Gabriele PannocchiaAbstract:Abstract In this paper we propose a cooperative distributed linear Model Predictive Control strategy applicable to any finite number of subsystems satisfying a stabilizability condition. The Control strategy has the following features: hard input constraints are satisfied; terminating the iteration of the distributed Controllers prior to convergence retains closed-loop stability; in the limit of iterating to convergence, the Control feedback is plantwide Pareto optimal and equivalent to the centralized Control solution; no coordination layer is employed. We provide guidance in how to partition the subsystems within the plant. We first establish exponential stability of suboptimal Model Predictive Control and show that the proposed cooperative Control strategy is in this class. We also establish that under perturbation from a stable state estimator, the origin remains exponentially stable. For plants with sparsely coupled input constraints, we provide an extension in which the decision variable space of each suboptimization is augmented to achieve Pareto optimality. We conclude with a simple example showing the performance advantage of cooperative Control compared to noncooperative and decentralized Control strategies.
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unreachable setpoints in Model Predictive Control
IEEE Transactions on Automatic Control, 2008Co-Authors: James B. Rawlings, Dennis Bonne, John Bagterp Jorgensen, Aswin N Venkat, Sten Bay JorgensenAbstract:In this work, a new Model Predictive Controller is developed that handles unreachable setpoints better than traditional Model Predictive Control methods. The new Controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal steady state is established for terminal constraint Model Predictive Control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend this analysis to terminal penalty MPC. Two examples are presented that demonstrate the advantages of the proposed setpoint-tracking MPC over the current target-tracking MPC.
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Model-Predictive Control of chemical processes
Chemical Engineering Science, 2001Co-Authors: John W. Eaton, James B. RawlingsAbstract:Abstract This paper discusses Model-Predictive Control, a scheme in which an open-loop performance objective is optimized over a finite moving time horizon. Model-Predictive Control is shown to provide performance superior to conventional feedback Control for nonminimum phase systems or systems with input constraints when future set points are known. Stabilizing unstable linear plants and Controlling nonlinear plants with multiple steady states are also discussed.
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suboptimal Model Predictive Control feasibility implies stability
IEEE Transactions on Automatic Control, 1999Co-Authors: P O M Scokaert, D Q Mayne, James B. RawlingsAbstract:Practical difficulties involved in implementing stabilizing Model Predictive Control laws for nonlinear systems are well known. Stabilizing formulations of the method normally rely on the assumption that global and exact solutions of nonconvex, nonlinear optimization problems are possible in limited computational time. In the paper, we first establish conditions under which suboptimal Model Predictive Control (MPC) Controllers are stabilizing; the conditions are mild holding out the hope that many existing Controllers remain stabilizing even if optimality is lost. Second, we present and analyze two suboptimal MPC schemes that are guaranteed to be stabilizing, provided an initial feasible solution is available and for which the computational requirements are more reasonable.