Predictive Control for Linear and Hybrid Systems

by Francesco Borrelli, Alberto Bemporad, Manfred Morari

Overview

Model Predictive Control (MPC), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book. With a simple, unified approach, and with attention to real-time implementation, it covers predictive control theory including the stability, feasibility, and robustness of MPC controllers. The theory of explicit MPC, where the nonlinear optimal feedback controller can be calculated efficiently, is presented in the context of linear systems with linear constraints, switched linear systems, and, more generally, linear hybrid systems. Drawing upon years of practical experience and using numerous examples and illustrative applications, the authors discuss the techniques required to design predictive control laws, including algorithms for polyhedral manipulations, mathematical and multiparametric programming and how to validate the theoretical properties and to implement predictive control policies. The most important algorithms feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.

Product Details

ISBN-13:	9781107652873
Publisher:	Cambridge University Press
Publication date:	06/22/2017
Pages:	440
Product dimensions:	7.48(w) x 9.69(h) x 0.79(d)

About the Author

Francesco Borrelli is a chaired Professor at the Department of Mechanical Engineering of the University of California, Berkeley. Since 2004 he has served as a consultant for major international corporations in the area of real-time predictive control. He was the founder and CTO of BrightBox Technologies Inc., and is the co-director of the Hyundai Center of Excellence in Integrated Vehicle Safety Systems and Control at the University of California, Berkeley. His research interests include constrained optimal control, model predictive control and its application to advanced automotive control, robotics and energy efficient building operation.

Alberto Bemporad is a Professor and former Director of the IMT School for Advanced Studies, Lucca. He has published numerous papers on model predictive control and its application in multiple domains. He has been a consultant for major automotive companies and cofounder of ODYS S.r.l., a company specializing in advanced control and optimization software for industrial production. He is the author or coauthor of various MATLAB® toolboxes for model predictive control design, including the Model Predictive Control Toolbox and the Hybrid Toolbox.

Manfred Morari was a Professor and Head of the Department of Information Technology and Electrical Engineering at the Swiss Federal Institute of Technology (ETH), Zurich. During the last three decades he shaped many of the developments and applications of model predictive control (MPC) through his academic research and interactions with companies from a wide range of sectors. The analysis techniques and software developed in his group are used throughout the world. He has received numerous awards and was elected to the US National Academy of Engineering and is a Fellow of the Royal Academy of Engineering.

Table of Contents

Preface; Acknowledgements; Symbols and acronyms; Part I. Basics of Optimization: 1. Main concepts; 2. Linear and quadratic optimization; 3. Numerical methods for optimization; 4. Polyhedra and p-collections; Part II. Multiparametric Programming: 5. Multiparametric nonlinear programming; 6. Multiparametric programming: a geometric approach; Part III. Optimal Control: 7. General formulation and discussion; 8. Linear quadratic optimal control; 9. Linear 1/∞ norm optimal control; Part IV. Constrained Optimal Control of Linear Systems: 10. Controllability, reachability and invariance; 11. Constrained optimal control; 12. Receding horizon control; 13. Approximate receding horizon control; 14. On-line control computation; 15. Constrained robust optimal control; Part V. Constrained Optimal Control of Hybrid Systems: 16. Models of hybrid systems; 17. Optimal control of hybrid systems; References; Index.