Minmax feedback model predictive control for constrained. This paper proposes a second order cone formulation of minmax mpc with zone control for lpv systems. This paper deals with the implementation of min max model predictive control for constrained linear systems with bounded additive uncertainties and. Model predictive control for maxminplusscaling systems. This volume provides a definitive survey of the latest model predictive control methods available to engineers and scientists today. Model predictive control for maxminplus systems springerlink. Minmax approaches to robust model predictive control. The second edition of model predictive control provides a thorough introduction to theoretical and practical aspects of the most commonly used mpc strategies. In recent years, much e ort has been spent to incorporate the robustness problem into this framework. The control schemes the authors discuss introduce, in the control optimization, the notion that feedback is present in the recedinghorizon implementation of the control.
Part of the control engineering book series contrengin abstract. Min max model predictive control for uncertain maxminplusscaling systems conference paper pdf available in international journal of control 815. Model predictive control mpc is a widely used control design method in the process industry. For all xt, we find, therefore, that the above set of controls satisfies the stability and state constraints. An algorithm for explicit solution of minmax model. An approximation of the worst case cost is used to obtain the control action. Minmax model predictive control as a quadratic program article pdf available in iet control theory and applications 11. Most physical systems possess parametric uncertainties or unmeasurable parameters and, since parametric uncertainty may degrade the performance of model predictive control mpc, mechanisms to update the unknown or uncertain parameters are desirable in application. From power plants to sugar refining, model predictive control mpc schemes have established themselves as the preferred control strategies for a wide variety of. Recent developments in model predictive control promise remarkable opportunities for designing multiinput, multioutput control systems and improving the control of singleinput, singleoutput systems. Model predictive control advanced textbooks in control and signal processing.
Model predictive control is a form of control scheme in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control. Model predictive control advanced textbooks in control. Bemporad, a decomposition algorithm for feedback minmax model predictive control, in proc. Based on this, we show that the suboptimal predictive. Pdf p the polysolenoid linear motor plm have been playing a crucial role in many industrial aspects because it provides a straight motion directly. Compared to existing approaches, the proposed techniques can be applied to linear systems. An algorithm for explicit solution of minmax model predictive control.
The minmax strategy in model predictive control mpc allows computing the. Min max model predictive control of nonlinear systems using discontinuous feedbacks fernando a. Minmax inventory method predictive optimization software. Minmax model predictive control mpc is one of the few techniques suitable for robust stabilization of uncertain nonlinear systems subject to constraints. Nonlinear model predictive control nmpc has become the accepted methodology to solve complex control problems related to process industries. Camacho minmax mpc 7 why minmax model predictive control.
Multistage suboptimal model predictive control with. This paper gives an overview of robustness in model predictive control mpc. May 15, 2007 from power plants to sugar refining, model predictive control mpc schemes have established themselves as the preferred control strategies for a wide variety of processes. Adaptive minmax model predictive control for field vehicle. In many control problems, disturbances are a fundamental ingredient and in stochastic model predictive control mpc they are accounted for by considering an average cost and probabilistic constraints, where a violation of the constraints is accepted provided that the probability of this to happen is kept below a given threshold. Closed loop response for nominal mpc and the proposed minimax controller. Minmax model predictive control for uncertain maxminplus. This chapter aims to give a concise overview of numerical methods and algorithms for implementing robust. The basic idea in mpc is to repeatedly solve optimization problems online to nd an optimal input to the controlled system.
A min max model predictive control approach to robust power management in ambulatory wireless sensor networks abstract. This paper addresses the problem of transmission power control within a network of resourceconstrained wireless sensors that operate within a particular ambient healthcare environment. This paper proposes a second order cone formulation of min max mpc with zone control for lpv systems. Doi link for robotics and mechatronics for agriculture. Kil to chong,minmax model predictive controller for trajectory tracking of a wheeled mobile robot with slipping. This book was set in lucida using latex, and printed and bound by. Feedback minmax model predictive control based on a. We have studied the robust selftriggered minmax mpc problem for constrained uncertain discretetime nonlinear systems. The second edition of model predictive control provides a thorough introduction to theoretical and practical aspects of the. This paper investigates state space model predictive control ssmpc of an aerothermic process.
This work is devoted to all aspects of model based predictive control, one of the hottest topics in control engineering. Model predictive control college of engineering uc santa barbara. Min max mpc algorithms based on both quadratic and 1norms or infinnorms costs are considered. However, the minmax system is not a blocking issue in itself. Explicit nonlinear model predictive control theory and. Model predictive control is a form of control scheme in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state. Minmax model predictive control of nonlinear systems using.
Minmax model predictive control mpc is one of the few techniques suitable for. The main contribution is an algorithm for solving the min max quadratic mpc problem with an arbitrary degree of approximation. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closedloop stability and performance. Minmax model predictive control of nonlinear systems using discontinuous feedbacks fernando a. Model predictive control camacho eduardo f bordons. The aim of this paper is to provide new techniques for computing a terminal cost and a local statefeedback control law that satisfy recently developed min max mpc inputtostate stabilization conditions. A survey on explicit model predictive control springer for. This approximation can be easily computed yielding a solution of the min max problem very close to the exact one. The aim of this paper is to provide new techniques for computing a terminal cost and a local statefeedback control law that satisfy recently developed minmax mpc inputtostate. Energies free fulltext minmax predictive control of. The starting point is classical predictive control and the. A minmax control framework, based on alternate minimisation and backpropagation through the forward model, is used for the offline computation of the controller and the safe set. Adjustment, reduced to one or two parameters, is particularly easy, by means of charts, thus allowing the operator to choose the horizon according to the desired performances. The min max operator is explored for the first time as an alternative to the traditional loss function.
New methods for computing the terminal cost for minmax. Minmax model predictive control of nonlinear systems. On robustness of suboptimal minmax model predictive control. This text is an introduction to model predictive control, a control methodology which has encountered some success in industry, but which still presents many the book is of interest as an introduction to model predictive control, and a merit is the special presentation, connecting the subject intimately.
The main contribution is an algorithm for solving the minmax quadratic mpc. The book will be of interest to both researchers and designers, and control engineers. Model predictive control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. This text is an introduction to model predictive control, a control methodology which has encountered some success in industry, but which still presents many the book. This book is a comprehensive introduction to model predictive control mpc, including its basic principles and algorithms, system analysis and design methods, strategy developments and practical applications. Pdf min max model predictive control for polysolenoid. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. Robust model predictive control with disturbance invariant sets. The control optimization is therefore feasible for all initial states. Applications in industry is an indispensable resource for plant process engineers and control engineers working in chemical plants, petrochemical companies, and oil refineries in which mpc systems already are operational, or where mpc implementations are being considering. Advances in modelbased predictive control book, 1994. Model predictive control mpc has been applied to many different types of energy systems 1,2. Predictive control with constraints maciejowski pdf download.
Proceeding of the american control conference, pages 62616267, marriott waterfront, baltimore, md, usa, 2010. Feedback min max model predictive control based on a quadratic cost function is addressed in this paper. Pdf minmax model predictive control for uncertain max. Feedback minmax model predictive control based on a quadratic cost function is addressed in this paper. Fontes and lalo magni abstract this paper proposes a model predictive control mpc algorithm for the solution of a robust control problem. Most importantly, mpc provides the flexibility to act while. The book proposes a simple predictive controller where the control laws are given in clear text that requires no calculations. A second order cone formulation of minmax mpc with zone. The main motivation behind explicit nmpc is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real. Stewart g and borrelli f 2008, a model predictive control framework for industrial turbodiesel engine control, decision and control, 2008 47th ieee conference on.
Minmax feedback model predictive control for constrained linear. Constraints are present in all control systems due to physical, environmental and. Modern predictive control explains how mpc differs from other control methods in its implementation of a control action. Lecture 12 model predictive control prediction model control optimization receding horizon update disturbance estimator feedback imc representation of mpc resource. It looks at the theory, current applications, and current areas of research.
A minmax model predictive control approach to robust. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closedloop system analysis, model predictive control optimizationbased pid control, genetic algorithm optimizationbased model predictive control, and. This paper shows the application of a minmax model predictive control mmmpc strategy to a pilot plant in which the temperature of a reactor is controlled. An algorithm for explicit solution of min max model predictive control p. Model predictive control is powerful technique for optimizing the performance of constrained systems. Explicit model predictive control mpc addresses the problem of removing one of the main drawbacks of mpc, namely the need to solve a mathematical program on line to compute the. Pdf minmax approaches to robust model predictive control. Energies free fulltext minmax predictive control of a. Pdf minmax model predictive control as a quadratic program. Most physical systems possess parametric uncertainties or unmeasurable parameters and, since parametric uncertainty may degrade the performance of model predictive control mpc. Robust selftriggered minmax model predictive control for. The idea of inputtostate stability iss is introduced and a lyapunovlike sufficient condition for iss is presented. State space model predictive control of an aerothermic.
Fontes and lalo magni abstract this paper proposes a model predictive. Minmax model predictive control implementation strategies eeci. Robust and adaptive model predictive control of nonlinear. Trajectory tracking control of an autonomous underwater vehicle using lyapunovbased model predictive control. From power plants to sugar refining, model predictive control mpc schemes have established themselves as the preferred control strategies for a wide variety of processes. Minmax model predictive control for uncertain maxmin. Most importantly, mpc provides the flexibility to act while optimizingwhich is essential to the solution of many engineering problems in complex plants, where exact modeling is. Indeed, assuming that another software, dedicated to inventory optimization, is capable of overwriting the minmax values on a daily basis for every sku, then in this case, any inventory control strategy remains possible. Constraints are present in all control systems due to physical, environmental and economic limits on plant operation, and the systematic handling of constraints provided by predictive control strategies allows for significant improvements in performance over conventional control methodologies. Minmax feedback formulations of model predictive control are discussed, both in the fixed and variable horizon contexts. Explicit model predictive control mpc addresses the problem of removing one of the main drawbacks of mpc, namely the need to solve a mathematical program on line to compute the control action. Neural network predictive control of a chemical reactor.
Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. Model predictive control advanced textbooks in control and signal processing camacho, eduardo f. With this proposal, the selection of voltage vectors does not need weighting factors that are normally used within the. A selftriggered control scheduler has been proposed to maximize the intersampling time of feedback minmax mpc, and the algorithm feasibility and closedloop isps at triggering time instants have been proved. This paper shows the application of a min max model predictive control mmmpc strategy to a pilot plant in which the temperature of a reactor is controlled.
In many control problems, disturbances are a fundamental ingredient and in stochastic model predictive control mpc they are accounted for by considering an average. It is a pilot scale heating and ventilation system equipped with a heater grid and a. Multivariable predictive control wiley online books. Approaches based on the extended state space model and extended nonminimal state space model ridong zhang, anke xue, furong gao this monograph. In this paper, a fuzzylogic based operator is used instead of a traditional cost function for the predictive stator current control of a fivephase induction machine im. New methods for computing the terminal cost for minmax model. Indeed, assuming that another software, dedicated to inventory optimization, is capable of overwriting the minmax values on.
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