We predict the behavior of a process state output over a time horizon. The basic mpc concept can be summarized as follows. A novel switch stabilizing model predictive control. Model predictive control mpc originated in the late seventies and has developed considerably since then. Can anyone suggest me a book or tutorial for understanding model predictive control. Rhc has several advantages over other types of control. Model predictive control control theory mathematical. 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. This book offers readers a thorough and rigorous introduction to nonlinear model.
Nonlinear model predictive control is a thorough and rigorous introduction to nonlinear model predictive control. The proposed controllers are so designed that the closedloop constrained systems are positive and stable, meanwhile, linear infinite horizon cost functions through their upper. Constrained infinitehorizon model predictive control for. 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. If its is true, you may mostly refer books by camacho. Introduction to model predictive control springerlink. 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.
Infinite horizon model predictive control tracking. An infinite horizon model predictive control for stable. 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. Nonlinear model predictive control theory and algorithms. Thus, deeper studies about this problem are necessary in order to promote an efficient operation of these processes at the industrial level.
The study is focused on the outputtracking problem of systems with stable and integrating modes and unmeasured disturbances. 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. Home browse by title books nonlinear model predictive control. Infinite horizon model predictive control robotics. The scheme can be applied to both stable and unstable systems with input constraints. 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. The book provides an excellent and extensive treatment of nmpc. Infinite horizon mpc and model reduction applied to large. What is the difference between finite and infinite horizon. 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. Online optimization is possible because this class of problems is relatively easy to solve, but may. Model predictive control mpc, which involves online optimization of a recedinghorizon objective, has been effectively.
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. 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. Nonlinear model predictive control is a thorough and rigorous introduction to. Nonlinear model predictive control is a thorough and rigorous introduction to nmpc for discretetime and sampleddata systems. The quasiinfinite horizon approach to nonlinear model predictive. Offers the student an uptodate account of nonlinear model predictive control written in a textbook style for easier learning. Pdf we present a method that combines offline trajectory optimization and online model predictive control mpc, generating robust. Terminal constraints guarantee the stability of predicted trajectories and form the basis of predictive control algorithms with guaranteed stability. New sufficient conditions are proposed in terms of linearmatrix inequalities. Im trying to selflearn some of the underlying concepts behind mpc because it keeps coming up in many. Novel switch stabilizing model predictive control strategy.
What are the best books to learn model predictive control for. Mpc selects the control to be applied to the system by solving an optimization problem over a finite. What are the best books to learn model predictive control. Im trying to selflearn some of the underlying concepts behind mpc because it keeps coming up in many seminars ive attended. The basic ideaof the method isto considerand optimizetherelevant variables, not. 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. Feb 14, 2009 model predictive control system design and implementation using matlab ebook written by liuping wang. Here are some examples of good books in model predictive control. This article is devoted to the problem of model predictive control mpc design for discretetime and continuoustime positive systems with state and input constraints.
Theory and algorithms lars grune, jurgen pannek auth. 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. A quasiinfinite horizon nonlinear model predictive control. Receding horizon control introduces the essentials of a successful feedback strategy that has emerged in many industrial fields. Receding horizon control rhc introduces the essentials of a successful feedback strategy that has emerged in many industrial fields. Nmpc is interpreted as an approximation of infinitehorizon optimal control so that important properties like closedloop stability, inverse optimality. 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. Sep, 2016 hi, i assume you are a masters student studying control engineering.
What are the advantages of model predictive control over optimal control. The repeated online solution of the finitehorizon problem for an everreceding horizon is called model predictive control. 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. Nonlinear model predictive control by lars grune overdrive. It has an offsetfree control law based on an only optimization problem onelayer. We present in this paper a novel nonlinear model predictive control scheme that guarantees asymptotic. 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. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. A novel formulation for infinite horizon model predictive control that is general and can accurately approximate and minimize the control cost is proposed. 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 control formulation, and the lyapunov stability of the closedloop system is achieved by adopting an infinite prediction horizon. Good text for model predictive control any recommendation for good books for mpc related ideas. This article discusses the existing linear model predictive control concepts in a unified theoretical framework based on a stabilizing, infinite horizon, linear quadratic regulator. Model predictive control mpc can be employed for optimal operation of adjustable hydraulic structures.
Infinitehorizon model predictive control for periodic tasks with contacts. Nmpc schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different nmpc variants. The study is focused on the outputtracking problem of systems with stable. Can anyone suggest me a book or tutorial for understanding. I also called model predictive control mpc for reasons well see later 5. An infinite horizon model predictive control for stable and.
Earlier work in the literature uses terminal constraints which define sufficient but not necessary conditions for the stability of predicted trajectories. A quasiinfinite horizon nonlinear model predictive control scheme with guaranteed stability. Nmpc is interpreted as an approximation of infinitehorizon optimal control so that. Model predictive control historically 1980s came about as a controller form, from the level of accuracy of mathematical.
It has been in use in the process industries in chemical. Hi, i assume you are a masters student studying control engineering. 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. Download for offline reading, highlight, bookmark or take notes while you read model predictive control system design and implementation using matlab. Receding horizon control model predictive control for. Nmpc is interpreted as an approximation of infinite horizon optimal control so that important properties like closedloop stability. In optimal control, what is infinite horizon problem. Functional nonlinear model predictive control based on.
Infinite horizon stable predictive control ieee journals. What are the prerequisites for studying model predictive. This work presents a nominally stabilizing mpc controller, also known as infinite horizon model predictive control ihmpc applied in the. Infinitehorizon differentiable 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.
Model predictive control with linear models muske 1993. Model predictive control mpc has become a widely used methodology across all engineering disciplines, yet there are few books which study this approach. Theory and algorithms communications and control engineering 2nd ed. Combining shortterm and longterm reservoir operation. The control of cyclic processes is an open issue in the literature due to their very peculiar dynamic behavior. This paper presents a functional model predictive control mpc approach based on an adaptive dynamic programming adp algorithm with the abilities of handling control. 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. Nonlinear model predictive control is a thorough and rigorous introduction to nonlinear model predictive control nmpc for discretetime and sampleddata systems. 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. Nmpc schemes with and without stabilizing terminal constraints are detailed, and intuitive examp. If n is large, the part of the problem for t n can be neglected. Thus, deeper studies about this problem are necessary in order to promote an efficient.
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. A quasiinfinite horizon nonlinear model predictive. Shrinking horizon model predictive control method for. Infinitehorizon model predictive control for periodic tasks with. A quasi infinite horizon nonlinear model predictive control scheme with guaranteed stability. In order to represent unstable as well as stable multivariable systems, the standard state. Theory and algorithms communications and control engineering grune, lars, pannek, jurgen on. The control of cyclic processes is an open issue in the literature because of their very peculiar dynamic behavior. Model predictive control for state models advanced textbooks in control and signal processing kwon, w. Model predictive control mpc, which involves online optimization of a receding horizon objective, has been effectively. We present in this paper a novel nonlinear model predictive control scheme that guarantees asymptotic closedloop stability. Model predictive control certaintyequivalent control constrained linearquadratic regulator in nite horizon model predictive control mpc with disturbance prediction 1. Nonfragile robust model predictive control for uncertain.
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control nmpc for. Combining shortterm and longterm reservoir operation using. Toward infinitehorizon optimality in nonlinear model. Model predictive control system design and implementation. This book offers readers a thorough and rigorous introduction to nonlinear model predictive control nmpc for discretetime and sampleddata systems. Nonlinear model predictive control theory and algorithms lars. Pdf in the past decade nonlinear model predictive control nmpc has witnessed. The term model predictive control does not designate a specific control strategy but rather an. Mpc selects the control to be applied to the system by solving an optimization problem over a finite horizon in realtime. Suppose that we wish to control a multipleinput, multipleoutput process while satisfying inequality constraints on the. The horizon finiteness is both the reason for mpcs success and its main limitation. Recommended practice is to choose the control interval duration controller property t s initially, and then hold it constant as you tune other.
Model predictive control for state models advanced textbooks in control and signal processing. More than 25 years after model predictive control mpc or receding horizon. This paper deals with the linear model predictive control mpc with infinite prediction horizon ihmpc that is nominally stable. A quasiinfinite horizon nonlinear model predictive control scheme. An implementable and stabilizing model predictive control. The problem of constrained infinitehorizon modelpredictive control for fuzzydiscrete systems is considered in this paper.
Choose sample time and horizons sample time duration. In model predictive control, the prediction horizon, p is also an important consideration. If one chooses to hold the prediction horizon duration the product pt s constant, p must vary inversely with t s. Introduction to model predictive control riccardo scattoliniriccardo scattolini. 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. It shows how the move to infinite horizon reduces the prediction mismatch and hence facilitates a stability result. Here we extend ihmpc to tackle periodic tasks, and demonstrate the power of our approach by synthesizing hopping behavior in a simulated robot. What are the advantages of model predictive control over.