Predictive Control for Linear and Hybrid Systems
Predictive Control for Linear and Hybrid Systems
Alberto Bemporad , IMT School for Advanced Studies, Lucca
Manfred Morari , ETH Zurich
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.
- Presents the main computational algorithms required to design predictive control algorithms
- Includes examples throughout to illustrate how to use the proposed algorithms and computational tools in order to transfer theory into practice
- Uses simple formalism to break down the main principles of model predictive control (MPC) for students struggling to understand the complex theory
Product details
July 2017Hardback
9781107016880
440 pages
252 × 193 × 24 mm
1.11kg
116 b/w illus. 11 tables
Available
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.
