Predictive Control for Linear and Hybrid Systems

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.

With a simple approach that includes real-time applications and algorithms, this book covers the theory of model predictive control (MPC).

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.