The book will be of interest to both researchers and designers, and control engineers. The main motivation behind explicit nmpc is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real. Based on this, we show that the suboptimal predictive. Model predictive control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. Robust model predictive control with disturbance invariant sets. 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. In the case of electric machines, the predictive controller can directly command a power converter, typically a voltage source inverter vsi yielding a direct digital control scheme that is often referred to as fsmpc and fcsmpc.
Doi link for robotics and mechatronics for agriculture. Minmax inventory method predictive optimization software. The main contribution is an algorithm for solving the min max quadratic mpc problem with an arbitrary degree of approximation. On robustness of suboptimal minmax model predictive control. A second order cone formulation of minmax mpc with zone. Model predictive control camacho eduardo f bordons. 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. This volume provides a definitive survey of the latest model predictive control methods available to engineers and scientists today. Minmax model predictive control mpc is one of the few techniques suitable for robust stabilization of uncertain nonlinear systems subject to constraints.
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. Model predictive control mpc is a widely used control design method in the process industry. Model predictive control college of engineering uc santa barbara. 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. This approximation can be easily computed yielding a solution of the min max problem very close to the exact one. Explicit nonlinear model predictive control theory and. Model predictive control advanced textbooks in control and signal processing. Feedback min max model predictive control based on a quadratic cost function is addressed in this paper. Minmax model predictive control of nonlinear systems. Feedback minmax model predictive control based on a quadratic cost function is addressed in this paper. Minmax model predictive control implementation strategies eeci.
Trajectory tracking control of an autonomous underwater vehicle using lyapunovbased model predictive control. This paper deals with the implementation of min max model predictive control for constrained linear systems with bounded additive uncertainties and. An algorithm for explicit solution of min max model predictive control p. Pdf minmax model predictive control for uncertain max.
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. The control optimization is therefore feasible for all initial states. Lecture 12 model predictive control prediction model control optimization receding horizon update disturbance estimator feedback imc representation of mpc resource. 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. Minmax model predictive control of nonlinear systems using. Most physical systems possess parametric uncertainties or unmeasurable parameters and, since parametric uncertainty may degrade the performance of model predictive control mpc.
This chapter aims to give a concise overview of numerical methods and algorithms for implementing robust. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. This book was set in lucida using latex, and printed and bound by. Adaptive minmax model predictive control for field vehicle. 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. Minmax feedback formulations of model predictive control are discussed, both in the fixed and variable horizon contexts. 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.
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. The book proposes a simple predictive controller where the control laws are given in clear text that requires no calculations. This work is devoted to all aspects of model based predictive control, one of the hottest topics in control engineering. Model predictive control mpc has been applied to many different types of energy systems 1,2. Bemporad, a decomposition algorithm for feedback minmax model predictive control, in proc. Fontes and lalo magni abstract this paper proposes a model predictive control mpc algorithm for the solution of a robust control problem. Min max mpc algorithms based on both quadratic and 1norms or infinnorms costs are considered. For all xt, we find, therefore, that the above set of controls satisfies the stability and state constraints. Constraints are present in all control systems due to physical, environmental and. 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. State space model predictive control of an aerothermic. Feedback minmax model predictive control based on a.
Most importantly, mpc provides the flexibility to act while. The min max operator is explored for the first time as an alternative to the traditional loss function. Closed loop response for nominal mpc and the proposed minimax controller. Pdf minmax model predictive control as a quadratic program. Minmax model predictive control as a quadratic program article pdf available in iet control theory and applications 11.
We have studied the robust selftriggered minmax mpc problem for constrained uncertain discretetime nonlinear systems. 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. This paper investigates state space model predictive control ssmpc of an aerothermic process. An algorithm for explicit solution of minmax model. Minmax model predictive control for uncertain maxminplus. Indeed, assuming that another software, dedicated to inventory optimization, is capable of overwriting the minmax values on. 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. Pdf p the polysolenoid linear motor plm have been playing a crucial role in many industrial aspects because it provides a straight motion directly. Approaches based on the extended state space model and extended nonminimal state space model ridong zhang, anke xue, furong gao this monograph.
The second edition of model predictive control provides a thorough introduction to theoretical and practical aspects of the most commonly used mpc strategies. Min max model predictive control for uncertain maxminplusscaling systems conference paper pdf available in international journal of control 815. Compared to existing approaches, the proposed techniques can be applied to linear systems. 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. Minmax approaches to robust model predictive control. It looks at the theory, current applications, and current areas of research. Robust and adaptive model predictive control of nonlinear. Nonlinear model predictive control nmpc has become the accepted methodology to solve complex control problems related to process industries. The idea of inputtostate stability iss is introduced and a lyapunovlike sufficient condition for iss is presented. 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.
Pdf min max model predictive control for polysolenoid. Energies free fulltext minmax predictive control of a. Camacho minmax mpc 7 why minmax model predictive control. Model predictive control for maxminplusscaling 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 is of interest as an introduction to model predictive control, and a merit is the special presentation, connecting the subject intimately. Modern predictive control explains how mpc differs from other control methods in its implementation of a control action. Multistage suboptimal model predictive control with.
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. Model predictive control advanced textbooks in control. An approximation of the worst case cost is used to obtain the control action. Kil to chong,minmax model predictive controller for trajectory tracking of a wheeled mobile robot with slipping. In many control problems, disturbances are a fundamental ingredient and in stochastic model predictive control mpc they are accounted for by considering an average.
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. This paper gives an overview of robustness in model predictive control mpc. Minmax model predictive control mpc is one of the few techniques suitable for. Neural network predictive control of a chemical reactor. 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. A survey on explicit model predictive control springer. The minmax strategy in model predictive control mpc allows computing the. Lecture 12 model predictive control prediction model control optimization receding horizon update disturbance estimator feedback. Model predictive control advanced textbooks in control and signal processing camacho, eduardo f.
The second edition of model predictive control provides a thorough introduction to theoretical and practical aspects of the. Pdf minmax approaches to robust model predictive control. The main contribution is an algorithm for solving the minmax quadratic mpc. It is a pilot scale heating and ventilation system equipped with a heater grid and a. Minmax model predictive control of nonlinear systems using discontinuous feedbacks fernando a. With this proposal, the selection of voltage vectors does not need weighting factors that are normally used within the. Minmax feedback model predictive control for constrained. This paper proposes a second order cone formulation of min max mpc with zone control for lpv systems. This paper proposes a multistage suboptimal model predictive control mpc strategy which can reduce the prediction horizon without compromising the stability property. The min max strategy in model predictive control mpc allows computing the optimal control actions where the worstcase performance to the system uncertainties is assumed. Model predictive control is powerful technique for optimizing the performance of constrained systems.
The basic idea in mpc is to repeatedly solve optimization problems online to nd an optimal input to the controlled system. This paper addresses the problem of transmission power control within a network of resourceconstrained wireless sensors that operate within a particular ambient healthcare environment. Recent developments in model predictive control promise remarkable opportunities for designing multiinput, multioutput control systems and improving the control of singleinput, singleoutput systems. An algorithm for explicit solution of minmax model predictive control. However, the minmax system is not a blocking issue in itself. 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. A min max model predictive control approach to robust power management in ambulatory wireless sensor networks abstract. 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. 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. A survey on explicit model predictive control springer for.
From power plants to sugar refining, model predictive control mpc schemes have established themselves as the preferred control strategies for a wide variety of. Stewart g and borrelli f 2008, a model predictive control framework for industrial turbodiesel engine control, decision and control, 2008 47th ieee conference on. Fontes and lalo magni abstract this paper proposes a model predictive. Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. A minmax model predictive control approach to robust. Multivariable predictive control wiley online books. The starting point is classical predictive control and the. 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. Predictive control with constraints maciejowski pdf download. Minmax feedback model predictive control for constrained linear. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closedloop stability and performance.
In recent years, much e ort has been spent to incorporate the robustness problem into this framework. Lee school of chemical and biomolecular engineering center for process systems engineering georgia inst. The control schemes the authors discuss introduce, in the control optimization, the notion that feedback is present in the recedinghorizon implementation of the control. Energies free fulltext minmax predictive control of. New methods for computing the terminal cost for minmax. Advances in modelbased predictive control book, 1994. Model predictive control for maxminplusscaling systems ef. Proceeding of the american control conference, pages 62616267, marriott waterfront, baltimore, md, usa, 2010. This paper proposes a second order cone formulation of minmax mpc with zone control for lpv systems. Model predictive control for maxminplus systems springerlink. Part of the control engineering book series contrengin abstract. New methods for computing the terminal cost for minmax model. Robust selftriggered minmax model predictive control for. 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.
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