With a simple, unified approach, and with consideration of real-time applications, this book covers the theory of stability, feasibility, and robustness of model predictive control (MPC). It is for graduate and postgraduate students, as well as advanced control practitioners interested in the theory and/or implementation of predictive control.
With a simple, unified approach, and with consideration of real-time applications, this book covers the theory of stability, feasibility, and robustness of model predictive control (MPC). It is for graduate and postgraduate students, as well as advanced control practitioners interested in the theory and/or implementation of predictive control.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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