The repeated online solution of the finitehorizon problem for an everreceding horizon is called model predictive control. Nmpc is interpreted as an approximation of infinitehorizon optimal control so. The study is focused on the outputtracking problem of systems with stable. Nmpc is interpreted as an approximation of infinite horizon optimal control so that important properties like closedloop stability. An infinite horizon model predictive control for stable. This book offers readers a thorough and rigorous introduction to nonlinear model. A quasiinfinite horizon nonlinear model predictive control. This paper presents a functional model predictive control mpc approach based on an adaptive dynamic programming adp algorithm with the abilities of handling control constraints and disturbances for the optimal control of nonlinear discretetime systems.
Nmpc is interpreted as an approximation of infinitehorizon optimal control so that. It has been in use in the process industries in chemical. Nonlinear model predictive control is primarily aimed at academic researchers and practitioners working in control and optimisation but the text is selfcontained featuring background material on infinite horizon optimal control and lyapunov stability theory which makes the book accessible to graduate students of control engineering and applied. Model predictive control mpc, which involves online optimization of a recedinghorizon objective, has been effectively. The proposed controllers are so designed that the closedloop constrained systems are positive and stable, meanwhile, linear infinite horizon cost functions through their upper. New sufficient conditions are proposed in terms of linearmatrix inequalities. Feb 14, 2009 model predictive control system design and implementation using matlab ebook written by liuping wang. It has an offsetfree control law based on an only optimization problem onelayer control formulation, and the lyapunov stability of the closedloop system is achieved by adopting an infinite prediction horizon. Recommended practice is to choose the control interval duration controller property t s initially, and then hold it constant as you tune other. Dual mode control infinite horizon terminal constraint set.
This paper studies the application of infinite horizon model predictive control mpc and model reduction by means of hankel norm to chemical process of interest in the field of control of large. We predict the behavior of a process state output over a time horizon. Mpc selects the control to be applied to the system by solving an optimization problem over a finite. Nmpc schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different nmpc variants. Model predictive control is a form of control 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. Rhc has several advantages over other types of control. Mpc selects the control to be applied to the system by solving an optimization problem over a finite horizon in realtime. An implementable and stabilizing model predictive control. Here we extend ihmpc to tackle periodic tasks, and demonstrate the power of our approach by synthesizing hopping behavior in a simulated robot. Model predictive control certaintyequivalent control constrained linearquadratic regulator in nite horizon model predictive control mpc with disturbance prediction 1. Finite horizon fh optimal control 3 closedloop solution the optimal solution is given by the statefeedback control law where the gain ki is and pi is the solution of the difference riccati eq uation. Nonlinear model predictive control is primarily aimed at academic researchers and practitioners working in control and optimisation but the text is selfcontained featuring background material on infinite. Earlier work in the literature uses terminal constraints which define sufficient but not necessary conditions for the stability of predicted trajectories. Online optimization is possible because this class of problems is relatively easy to solve, but may.
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. Infinite horizon model predictive control tracking. What are the best books to learn model predictive control. A quasiinfinite horizon nonlinear model predictive control scheme with guaranteed stability.
Mpc model predictive control also known as dmc dynamical matrix control gpc generalized predictive control rhc receding horizon control control algorithms based on numerically solving an optimization problem at each step constrained optimization typically qp or lp receding horizon control. Nmpc is interpreted as an approximation of infinite horizon optimal control so that important properties like closedloop stability, inverse optimality and suboptimality can be derived in a uniform manner. Pdf we present a method that combines offline trajectory optimization and online model predictive control mpc, generating robust. Hi, i assume you are a masters student studying control engineering. A quasi infinite horizon nonlinear model predictive control scheme with guaranteed stability. The bare minimum for discretetime linear mpc, which may be the easiest setting to start learning is some entry level knowledge of these 3 topics.
In model predictive control, the prediction horizon, p is also an important consideration. Toward infinitehorizon optimality in nonlinear model. Infinitehorizon model predictive control for periodic tasks with contacts. What is the difference between finite and infinite horizon. Thus, deeper studies about this problem are necessary in order to promote an efficient. We present in this paper a novel nonlinear model predictive control scheme that guarantees asymptotic closedloop stability. A quasiinfinite horizon nonlinear model predictive control scheme. Pdf in the past decade nonlinear model predictive control nmpc has witnessed.
If one chooses to hold the prediction horizon duration the product pt s constant, p must vary inversely with t s. Good text for model predictive control any recommendation for good books for mpc related ideas. Terminal constraints guarantee the stability of predicted trajectories and form the basis of predictive control algorithms with guaranteed stability. A novel formulation for infinite horizon model predictive control that is general and can accurately approximate and minimize the control cost is proposed. This paper deals with the linear model predictive control mpc with infinite prediction horizon ihmpc that is nominally stable. Nonlinear model predictive control theory and algorithms lars. What are the advantages of model predictive control over. Theory and algorithms communications and control engineering grune, lars, pannek, jurgen on. Infinitehorizon differentiable model predictive control.
More than 25 years after model predictive control mpc or receding horizon. What are the prerequisites for studying model predictive. Here are some examples of good books in model predictive control. Introduction to model predictive control springerlink. A quasiinfinite horizon nonlinear model predictive. Model predictive control mpc, which involves online optimization of a receding horizon objective, has been effectively. Introduction to model predictive control riccardo scattoliniriccardo scattolini. Model predictive control historically 1980s came about as a controller form, from the level of accuracy of mathematical models scientist and engineers have been able to come up with over the years. The control of cyclic processes is an open issue in the literature because of their very peculiar dynamic behavior. Suppose that we wish to control a multipleinput, multipleoutput process while satisfying inequality constraints on the. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. The term model predictive control does not designate a specific control strategy but rather an. Combining shortterm and longterm reservoir operation using. Receding horizon control rhc introduces the essentials of a successful feedback strategy that has emerged in many industrial fields.
Model predictive control in this chapter we consider model predictive control mpc, an important advanced control technique for dif. This work presents a nominally stabilizing mpc controller, also known as infinite horizon model predictive control ihmpc applied in the. Infinite horizon mpc and model reduction applied to large. A model predictive control mpc framework with a fixed maneuver horizon and shrinking prediction and control horizons is presented that, at each time step, minimizes the most accurate. Model predictive control mpc has become a widely used methodology across all engineering disciplines, yet there are few books which study this approach. Nmpc schemes with and without stabilizing terminal constraints are detailed, and intuitive examp. This article discusses the existing linear model predictive control concepts in a unified theoretical framework based on a stabilizing, infinite horizon, linear quadratic regulator. Gives the lecturer a sourcebook for teaching nonlinear model predictive control without needing to work up material from papers and contributed books. Prediction mismatch is expected in the setup of finite horizon model predictive control.
Furthermore, the setup includes a terminal inequality constraint that forces the states at the end of the finite prediction horizon to lie within a prescribed terminal. The book provides an excellent and extensive treatment of nmpc. Sep, 2016 hi, i assume you are a masters student studying control engineering. Offers the student an uptodate account of nonlinear model predictive control written in a textbook style for easier learning. Theory and algorithms communications and control engineering 2nd ed. The horizon finiteness is both the reason for mpcs success and its main limitation. Nonfragile robust model predictive control for uncertain. Nonlinear model predictive control is a thorough and rigorous introduction to. Can anyone suggest me a book or tutorial for understanding.
Shrinking horizon model predictive control method for. Prediction mismatch is expected in the setup of finite horizon model predictive control mpc algorithms such as generalised predictive control gpc and dynamic matrix control dmc and prevents straightforward stability proofs. Model predictive control with linear models muske 1993. This method allows output tracking over a finite horizon when constraints are considered and equilibrium tracking in the linearized dynamics after that for the infinite horizon when. In this paper we deploy conditions which are both necessary and sufficient and hence release more degrees of freedom for optimizing performance andor meeting constraints. It shows how the move to infinite horizon reduces the prediction mismatch and hence facilitates a stability result. Choose sample time and horizons sample time duration.
The basic mpc concept can be summarized as follows. Constrained infinitehorizon model predictive control for. Model predictive control mpc originated in the late seventies and has developed considerably since then. Nmpc is interpreted as an approximation of infinitehorizon optimal control so that important properties like closedloop stability, inverse optimality.
This chapter presents a stabilizing model predictive control mpc strategy for inverted pendulumlike behaved systems. This book offers readers a thorough and rigorous introduction to nonlinear model predictive control nmpc for discretetime and sampleddata systems. Jun 27, 2003 model predictive control mpc has become a widely used methodology across all engineering disciplines, yet there are few books which study this approach. An infinite horizon model predictive control for stable and. Model predictive control control theory mathematical. Until now, no book has addressed in detail all key issues in the field including apriori stability and robust stability results. Model predictive control is a form of control 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. The control of cyclic processes is an open issue in the literature due to their very peculiar dynamic behavior. Model predictive control historically 1980s came about as a controller form, from the level of accuracy of mathematical. Infinite horizon stable predictive control ieee journals. Introduction model predictive control mpc, also known as receding horizon control rhc, is an online control scheme based on the conventional optimal control that is obtained by. Combining shortterm and longterm reservoir operation.
Home browse by title books nonlinear model predictive control. I also called model predictive control mpc for reasons well see later 5. What are the best books to learn model predictive control for. We present in this paper a novel nonlinear model predictive control scheme that guarantees asymptotic. A novel switch stabilizing model predictive control. The problem of constrained infinitehorizon modelpredictive control for fuzzydiscrete systems is considered in this paper.
Download for offline reading, highlight, bookmark or take notes while you read model predictive control system design and implementation using matlab. This article is devoted to the problem of model predictive control mpc design for discretetime and continuoustime positive systems with state and input constraints. Infinite horizon model predictive control robotics. Model predictive control for state models advanced textbooks in control and signal processing kwon, w. Functional nonlinear model predictive control based on. What are the advantages of model predictive control over optimal control. Model predictive control system design and implementation. The quasiinfinite horizon approach to nonlinear model predictive. Nonlinear model predictive control by lars grune overdrive. Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. In optimal control, what is infinite horizon problem. Model predictive control for state models advanced textbooks in control and signal processing. The basic ideaof the method isto considerand optimizetherelevant variables, not.
Part of the lecture notes in control and information sciences book series. Thus, deeper studies about this problem are necessary in order to promote an efficient operation of these processes at the industrial level. Nonlinear model predictive control theory and algorithms. Receding horizon control model predictive control for. If its is true, you may mostly refer books by camacho. The study is focused on the outputtracking problem of systems with stable and integrating modes and unmeasured disturbances. Pdf infinitehorizon model predictive control for periodic tasks. The scheme can be applied to both stable and unstable systems with input constraints. Receding horizon control introduces the essentials of a successful feedback strategy that has emerged in many industrial fields. In order to represent unstable as well as stable multivariable systems, the standard state. Nonlinear model predictive control is a thorough and rigorous introduction to nmpc for discretetime and sampleddata systems. This book offers readers a thorough and rigorous introduction to nonlinear model predictive control nmpc for.
Can anyone suggest me a book or tutorial for understanding model predictive control. 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. Theory and algorithms lars grune, jurgen pannek auth. Infinite horizon ih lq control consider the ih performance index if the pairif the pair a b. It has an offsetfree control law based on an only optimization problem onelayer. If n is large, the part of the problem for t n can be neglected. Infinitehorizon model predictive control for periodic tasks with. Model predictive control mpc can be employed for optimal operation of adjustable hydraulic structures. Nonlinear model predictive control is a thorough and rigorous introduction to nonlinear model predictive control nmpc for discretetime and sampleddata systems. This paper presents a functional model predictive control mpc approach based on an adaptive dynamic programming adp algorithm with the abilities of handling control. Nonlinear model predictive control is a thorough and rigorous introduction to nonlinear model predictive control. Im trying to selflearn some of the underlying concepts behind mpc because it keeps coming up in many seminars ive attended.