Ee392m winter 2003 control engineering 1216 technical detail tuning of mpc feedback control performance is an issue. The ideas of receding horizon control and model predictive control can be traced back to. Design of a model predictive controller to control uavs. Model predictive control stanford engineering everywhere. Theory is well documented, but examples i found are full of gaps, which made them not so usefull.
Solution manual available to course instructors who adopt the text. Abstract we refer to model predictive control mpc as that family of controllers in which there is a direct use of an explicit and separately identifiable model. For the last 30 y ears the theory and technology of model predictive control mpc hav e. Theoretical aspects model predictive control mpc is a powerful control design method for constrained dynam ical systems. It is one of the few areas that has received ongoing interest from researchers in both the industrial and academic communities. In recent years it has also been used in power system balancing models and in power electronics. Theory says a terminal constraint is good mpc stability formulations. Control requirements imposed by design of a networked control strategy appear to be fully met by model predictive control mpc. The fth chapter contains the simulations done to show how the implementation of the model predictive controller is able to control the ariel uav and also how it is able to deal with. Model building, controller design and simulation tasks are carried out on personal computers. Lee school of chemical and biomolecular engineering. It will enable researchers to learn and teach the fundamentals of mpc without continuously searching the diverse control research literature for omitted arguments and requisite background material.
Mayne, 2009 nob hill publishing predictive control with constraints, jan maciejowski, 2000. Switched model predictive control of switched linear systems. A popular model based control design technique is model predictive control mpc, wherein one optimizes the control input over a finite time horizon, applies that input for a single timestep, and. The most important algorithms feature in an accompanying free online matlab toolbox, which allows easy access to sample solutions. Concentration versus time for the ancillary model predictive controller with sample time \delta 12 left and \delta 8 right. The reason for its popularity in industry and academia is its capability of operating without expert intervention for long periods. New trends and tools alberto bemporad abstractmodelbased design is well recognized in industry as a systematic approach to the development, evaluation, and implementation of feedback controllers. Convex optimization, stephen boyd and lieven vandenberghe, 2004 cambridge university press. The controller calculates the optimal quantity of insulin based on measurements of the blood sugar on the subcutaneous layer. Highperformance model predictive control for process industry. Tractable in theory and practice f and c convex excellent reference textbook. Predictive control is a way of thinking not a specific algorithm.
Chapter 3 nonlinear model predictive control in this chapter, we introduce the nonlinear model predictive control algorithm in a rigorous way. Nasa ames research center, moffett field, ca 94035 this paper presents an optimal control method for a class of distributedparameter systems governed by. In this study, an education and application interface was designed for model predictive control mpc. Keerthi and gilbert 1988, mayne and michalska 1990 etc. Model predictive control utcinstitute for advanced. Control design methods based on the mpc concept have found wide acceptance in industrial applications and have been studied by academia. Ece7850 lecture 8 nonlinear model predictive control. Model predictive control mpc is one of the most successful control techniques that can be used with hybrid systems. The general approaches of model predictive control and adaptive control provide benefits of robustness with respect to the model of internal dynamics. With its intrinsic knowledge of the system dynamics and the iterative computation of a fuel optimal control sequence for the entire length of the prediction horizon, 6. Some simulation abilities were provided to simulate the closed loop performance of the controlled hybrid system. 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. I have a few confusions about model predictive control mpc.
Johansen abstract nonlinear model predictive control and moving horizon estimation are related methods since both are based on the concept of solving an optimization problem that involves a. The model predictive control mpc toolbox is a collection of functions commands developed for the analysis and design of model predictive control mpc systems. Its popularity steadily increased throughout the 1980s. Since the beginning of the 1990s, a real boom in the number of industrial. Although a major application of control theory is in control systems engineering, which deals with the design of process control systems for industry, other applications range far beyond this. N control series includes bibliographical references and index. Design of the model predictive control education and. 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 theory computation and design 2nd. Model predictive control mpc is a particular branch of modelbased design.
The emphasis of this tutorial on control theory is on the design of digital controls to achie ve good dynamic response and small errors while using signals that are sampled in time and quantized in amplitude. In this chapter, we introduce the nonlinear model predictive control algorithm in a rigorous way. Neural network based model predictive control 1031 after providing a brief overview of model predictive control in the next section, we present details on the formulation of the nonlinear model. Model predictive control mpc represents a very simple idea for control design, which is intuitively understandable and can be implemented using standard tools.
It presents the basic principles of model predictive control mpc and its application for power converters and drives. After describing the model, an industrial application is presented that validates the usefulness of the nonlinear model in an mpc algorithm. Model predictive control was conceived in the 1970s primarily by industry. Model predictive control may be enhanced by adaptive feedback that modifies the parameters or the form for the model of internal dynamics. Jan maciejowskis book provides a systematic and comprehensive course on predictive control suitable for senior undergraduate and graduate students and professional engineers. Mayne, 2009 nob hill publishing predictive control with constraints, jan maciejowski, 2000 prentice hall optimization. Model predictive control linear convex optimal control. Chapter1 introductiontononlinearmodel predictivecontroland movinghorizon estimation tor a. The reason for such popularity is the ability of mpc designs to yield high performance control.
Unesco eolss sample chapters control systems, robotics and automation vol. Frontiers of model predictive control robust model predictive control nonlinear model predictive control excellent applications guide for researchers and engineers recent achievements of authors over the world theory with practical examples kinds of algorithms for choice. Isbn 9789533071022, pdf isbn 9789535159353, published 20100818. Particle filtering text and exercises from chapter 4, first edition. 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. Half a century after its birth, it has been widely accepted in many engineering fields and has brought much. Over the past few years significant progress has been achieved in the field of nonlinear model predictive control nmpc, also referred to as receding horizon control or moving horizon control. Model predictive control toolboxmodel predictive control toolbox 12 mpc. Chapter1 introductiontononlinearmodel predictivecontroland. Dynamic control is also known as nonlinear model predictive control nmpc or simply as nonlinear control nlc. Rawlings, university of university of california, santa barbara, david q. The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. Developed interface includes model predictive control methods, such as singleinput singleoutput, multiinput multioutput, constrained or unconstrained systems.
Pct predictive control technology, 1984 marketed by profimatics, inc. Model predictive control toolbox provides functions, an app, and simulink blocks for designing and simulating model predictive controllers mpcs. Maciejowski, predictive control with constraints pearson. Tutorial overview of model predictive control ieee control. Nlc with predictive models is a dynamic optimization approach that seeks to. Works in practice, without formal analysis theory requires large infinite prediction horizon terminal constraint additional tricks for a separate static optimization step integrating and. Model predictive control toolbox getting started guide. Feasibility, stability and robustness lixian zhang, songlin zhuang, richard d. Many of the fallacies that econometricians invoked when attempting to apply control theory to economics are explained well by athans and kendrick, 2. Model predictive control toolbox product description design and simulate model predictive controllers model predictive control toolbox provides functions, an app, and simulink blocks for systematically analyzing, designing, and simulating model predictive controllers. Gives the human or philosophical thinking behind predictive control and explains why this is an intuitively obvious approach to control design. Nob hill publishing is pleased to announce the availability of the second edition of the textbook, model predictive control.
Model predictive optimal control of a timedelay distributedparameter system nhan nguyen. Abstract this workshop introduces its audience to the theory, design and applications of model predictive control mpc under uncertainty. Ece7850 wei zhang ece7850 lecture 8 nonlinear model predictive control. Pdf model predictive control status and challenges.
This text provides a comprehensive and foundational treatment of the theory. Combines the aspects of idcom and dmc the optimization problem is solved for only one control move. Model predictive control mpc has a long history in the. Model predictive control predictive control of power. Model predictive control mpc originated in the late seventies and has developed considerably since then.
You can specify plant and disturbance models, horizons, constraints, and. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. Model predictive controllers rely on dynamic models of. Design 2nd edition pdf ebooks read model predictive control theory model predictive. Since they are all minor questions related to the same category, i ask them under one topic.
Introduction to model predictive control springerlink. Tutorial overview of model predictive control ieee control systems mag azine author. Model predictive control theory and design rawlings, james b. Four major aspects of model predictive control make the design methodology attractive to both practitioners and academics. In this article, we apply such a theory to design model predictive tracking controllers for systems whose dynamics evolve on manifolds and illustrate its efficacy with the fully actuated rigid. Distribution is based on 500 trials for each value of \varepsilon. 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. There are various control design methods based on model predictive control concepts. For this design, mpc toolbox and matlab gui in the matlab software were used. Mpc is a controller build on a model for the speci c case. We refer to model predictive control mpc as that family of controllers in which there is a direct use of an explicit and separately identifiable model.
This text provides a comprehensive and foundational treatment of the theory and design of model predictive control. Model predictive control is an indispensable part of industrial control engineering and is increasingly the method of choice for advanced control applications. Model predictive control offers several important advantages. Consider predictive control design simple kinematical model of a car driving at speed v lane direction lateral displacement y x v u.
Model predictive control is the family of controllers, makes the explicit use of model to obtain control signal. Model predictive optimal control of a timedelay distributed. Both transform classical control and statespace modern control methods are described and applied to illustrati ve examples. This project will deal with a control algorithm for an insulin pump, for which linear model predictive control will be used. As the general theory of feedback systems, control theory is useful wherever feedback occurs. Mpc describes a wide family of controllers, not a specific control strategy. Implementation platform is similar to idcom and dmc. Get started with model predictive control toolbox design and simulate model predictive controllers model predictive control toolbox provides functions, an app, and simulink blocks for designing and simulating model predictive controllers mpcs. In this thesis, we explore model predictive control and derive two fast, low complexity algorithms, one for guaranteed stability and feasibility of nonlinear systems and one for reference tracking for linear systems. Predictive control for linear and hybrid systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory andor implementation aspects of predictive control. Model predictive control mpc usually refers to a class of control algorithms in which a dynamic process model is used to predict and optimize process performance, but it is can also be seen as a term denoting a natural control strategy that matches the human thought form most closely. 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. Comparison of standard and tubebased mpc with an aggressive model predictive controller.
This chapter presents an overview of different predictive control methods. Model predictive control status and challenges y ugeng xi 1 dew ei li 1 shu lin 1 abstract. These problems were also pointed out quite vehemently by systems to deal with prediction theory. More than 250 papers have been published in 2006 in isi journals. Model predictive control theory and design control theory wikipedia control theory in control systems engineering is a subfield of mathematics that deals with the control of continuously. Contents list of symbols and abbreviations xxvii 1 discretetime mpc for beginners 1. Model predictive control system design and implementation.