Predictive Control for Linear and Hybrid Systems
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- Francesco Borrelli, University of California, Berkeley
- Alberto Bemporad, IMT School for Advanced Studies, Lucca
- Manfred Morari, ETH Zurich
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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.Read more
- 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
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- Date Published: June 2017
- format: Adobe eBook Reader
- isbn: 9781108158770
- contains: 116 b/w illus. 11 tables
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
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
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