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Predictive Control for Linear and Hybrid Systems


  • Date Published: June 2017
  • availability: Available
  • format: Hardback
  • isbn: 9781107016880

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About the Authors
  • 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
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    Product details

    • Date Published: June 2017
    • format: Hardback
    • isbn: 9781107016880
    • length: 440 pages
    • dimensions: 252 x 193 x 24 mm
    • weight: 1.11kg
    • contains: 116 b/w illus. 11 tables
    • availability: Available
  • 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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    Predictive Control for Linear and Hybrid Systems

    Francesco Borrelli, Alberto Bemporad, Manfred Morari

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  • Authors

    Francesco Borrelli, University of California, Berkeley
    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.

    Alberto Bemporad, IMT School for Advanced Studies, Lucca
    Alberto Bemporad is a Professor and former Director of the IMT School for Advanced Studies, Lucca. He has published numerous papers on model predictive control and its application in multiple domains. He has been a consultant for major automotive companies and cofounder of ODYS S.r.l., a company specializing in advanced control and optimization software for industrial production. He is the author or coauthor of various MATLAB® toolboxes for model predictive control design, including the Model Predictive Control Toolbox and the Hybrid Toolbox.

    Manfred Morari, ETH Zurich
    Manfred Morari was a Professor and Head of the Department of Information Technology and Electrical Engineering at the Swiss Federal Institute of Technology (ETH), Zurich. During the last three decades he shaped many of the developments and applications of model predictive control (MPC) through his academic research and interactions with companies from a wide range of sectors. The analysis techniques and software developed in his group are used throughout the world. He has received numerous awards and was elected to the US National Academy of Engineering and is a Fellow of the Royal Academy of Engineering.

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