Description
Book SynopsisModel 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 algorith
Table of ContentsPreface; 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.
