Future values of output variables are predicted using a dynamic model of the process and current measurements. May 23, 2019 in this series, youll learn how model predictive control mpc works, and youll discover the benefits of this multivariable control technique. Fully parameterized tube model predictive control article in international journal of robust and nonlinear control 2212. Parameterized tube model predictive control university of oxford. The most relevant novel feature of our proposal is the online use of a single tractable linear program. Parameterized tube model predictive control ieee journals.
The term model predictive control does not designate a specific control strategy but rather an ample range of control methods which make explicit use of a model of the process to obtain the control. N control series includes bibliographical references and index. Model predictive control mpc is one of the most successful control techniques that can be used with hybrid systems. University of alabama at huntsville, huntsville, alabama 35899 jason kyle oregon state university, corvallis, oregon 973314501 and mark costello georgia institute of technology, atlanta, georgia 30332 doi. Model predictive control mpc has been a leading technology in the field of advanced process control for over 30 years. Stabilizing tubebased model predictive control for. Sections 6 discussion and computational aspects, 7 conclusions and future research discuss. Parameterized tube model predictive control request pdf. Model based predictive control mpc, arguably the most effective control methodology for constrained systems, has seen rapid growth over the last few decades. Some simulation abilities were provided to simulate the closed loop performance of the controlled hybrid system.
Xwe introduce a nonempty control constraint set ux. Model predictive control mpc represents a very simple idea for control design, which is intuitively understandable and can be implemented using standard tools. A block diagram of a model predictive control system is shown in fig. Model predictive control is an indispensable part of industrial control engineering and is increasingly the method of choice for advanced control applications. Optimal robust mpc for constrained linear systems that are s ubject to additive. Jan maciejowskis book provides a systematic and comprehensive course on predictive control suitable for senior undergraduate and graduate students and professional engineers. Model predictive control linear convex optimal control. Maciejowski, predictive control with constraints pearson. Lee school of chemical and biomolecular engineering center for process systems engineering georgia inst. Model predictive control college of engineering uc santa barbara. The theory of classical mpc is well established by now, and robust mpc rmpc that deals with uncertainty either in the form of additive disturbance or imprecise andor timevarying.
Tube based model predictive control svr seminar 31012008 problem formulation discrete time, time. Abstractthis paper develops a parameterized tube model predictive control mpc synthesis method. The idea behind this approach can be explained using an example of driving a car. The book is of interest as an introduction to model predictive control, and a merit is the special presentation, connecting the subject intimately with. The method employs several novel features including a more general parameterization of the state and control tubes based on homothety and. The most relevant novel feature of our proposal is the. Homothetic tube model predictive control sciencedirect. This paper presents a heterogeneously parameterized tubebased model predictive control mpc design applicable. Tube model predictive control with an auxiliary sliding mode. An overview of some recent developments in the area is found in the book 10. The term model predictive control does not designate a specific control strategy but rather an ample range of control methods which make explicit use of a model of the process to obtain the control signal by minimizing an objective function. Parameterized tube model predictive control sv rakovic, b kouvaritakis, m cannon, c panos, r findeisen ieee transactions on automatic control 57 11, 27462761, 2012.
Robust model predictive control, prediction structures, parameterized tubes. This book was set in lucida using latex, and printed and bound by. The basic ideaof the method isto considerand optimizetherelevant variables, not. Introduction model predictive controller mpc is traced back to the 1970s. An introduction to modelbased predictive control mpc by stanislaw h. Apr 02, 2015 dynamic control is also known as nonlinear model predictive control nmpc or simply as nonlinear control nlc. Striped parameterized tube model predictive control. Model based predictive and distributed control lab. 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. Model predictive control has had an exceptional history with early intimations in the academic literature coupled with an explosive growth due to its independent adoption by the process. Model predictive control mpc, also known as receding horizon control or moving horizon control, uses the range of control methods, making the use of an explicit dynamic plant model to predict the effect of future reactions of the manipulated variables on the output and the control signal obtained by minimizing the cost function 7. Reaching a sensible compromise between computational tractability and degree of optimality still remains a significant challenge in robust model predictive control.
This information is used to construct state tubes to which the future trajectories of the state are confined. Abstract this workshop introduces its audience to the theory, design and applications of model predictive control mpc under uncertainty. The idea behind mpc is to start with a model of the openloop process that explains the dynamical relations among systems variables command inputs, internal states, and measured outputs. Striped parameterized tube model predictive control diego munozcarpintero. Nonlinear model predictive control technique for unmanned air vehicles nathan slegers. Heterogeneously parameterized tube model predictive control for. R system variables are constrained by the control u. Model predictive control mpc is a widely spread technology in industry for control design of highly complex multivariable processes. The most relevant novel feature of our proposal is the online use of a single tractable linear program that optimizes parameterized, minkowski decomposable, state and control tubes and an associated, fully separable, nonlinear, control policy. Rakovic sv, kouvaritakis b, cannon m, panos c, findeisen r 2012 parameterized tube model predictive control. Htmpc 32,33 employed several novel features including. Pdf homothetic tube model predictive control researchgate. Pdf parameterized tube model predictive control rolf.
Hi, i assume you are a masters student studying control engineering. If its is true, you may mostly refer books by camacho. Sections 6 discussion and computational aspects, 7 conclusions and future research discuss computational issues, provide an illustrative example and draw conclusions. This text is an introduction to model predictive control, a control methodology which has encountered some success in industry, but which still presents many theoretical challenges. Tubebased model predictive control for linear parameter. The objective of this paper is to control the angular speed in a model of a dc motor using different control strategies like model predictive control and linear quadratic regulator for comparison. Model predictive control advanced textbooks in control and. Tutorial overview of model predictive control ieee control. Nlc with predictive models is a dynamic optimization approach that seeks to follow. The residuals, the differences between the actual and predicted outputs, serve as the feedback signal to a.
To this end, we introduce a nonempty state constraint set x. University of alabama at huntsville, huntsville, alabama 35899 jason kyle oregon state university, corvallis. The proposed tube mpc with an auxiliary smc has been applied to the real dc servo system inteco,2011, and the digital simulation and experimental results are given in section5. A complete solution manual more than 300 pages is available for course instructors. The control tubes are sequences of sets of possible controls. This paper develops a parameterized tube model predictive control mpc synthesis method. Mpc differs from other control techniques in its implementation.
Nonlinear model predictive control technique for unmanned. Ee392m spring 2005 gorinevsky control engineering 141 lecture 14 model predictive control part 1. The idea behind mpc is to start with a model of the open. Course on model predictive control part ii linear mpc design. Developments in robust and stochastic predictive control in. Tutorial overview of model predictive control ieee control systems mag azine author. This paper gives an overview of robustness in model predictive control. The proposed tube mpc with an auxiliary smc has been applied to the real.
It has been in use in the process industries in chemical plants and oil refineries since the 1980s. Process control in the chemical industries 115 model predictive control an introduction 1. Introduction to model predictive control springerlink. This paper introduces a tube based model predictive control mpc for linear parameter varying lpv systems which exploits knowledge about bounds on the parameters rate of change to extrapolate its admissible values over the prediction horizon. Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. An introduction to modelbased predictive control mpc. Model predictive control handbook bailey, steve on. The common ground of these algorithms is that they. It has been in use in the process industries in chemical.
Pdf the robust model predictive control for constrained linear discrete time. Model predictive control mpc, also known as receding horizon control or moving horizon control, uses the range of control methods, making the use of an explicit dynamic plant model to predict the effect. Section 5 focuses on the homothetic tube model predictive control and its system theoretic properties. A process model is used to predict the current values of the output variables. What are the best books to learn model predictive control for. Anticipative model predictive control for linear parameter varying. The recently proposed parameterized tube model predictive control mpc exploited linearity to separate the treatment of future disturbances in robust model predictive control, thereby gaining significant computational advantages while superseding the state. Robust model predictive control a story of tube model predictive. These tools originate from di erent elds of research such as system theory, modeling, di erential and di erence equations, simulation, optimization and optimal control. Nonlinear model predictive control technique for unmanned air. Modelbased predictive control mpc, arguably the most effective control methodology for constrained systems, has seen rapid growth over the last few decades. The concept history and industrial application resource. Publishers pdf, also known as version of record includes final page. A modification of the parameterized tube model predictive control ptmpc strategy for linear systems with additive disturbances is proposed, which reduces the dependence of the number of optimization variables on horizon length from quadratic to linear by using a triangular striped prediction structure.
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