Francesco Borrelli

Predictive Control for Linear and Hybrid Systems (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 22. Juni 2017
• Englisch
• ISBN-13: 9781108157933
• Artikelnr.: 48678200

Autorenporträt

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.

Inhaltsangabe

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.