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Model Predictive Control

Classical, Robust and Stochastic

  • Basil Kouvaritakis
  • Mark Cannon

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Basil Kouvaritakis, Mark Cannon
    Pages 1-9
  3. Classical MPC

    1. Front Matter
      Pages 11-11
    2. Basil Kouvaritakis, Mark Cannon
      Pages 13-64
  4. Robust MPC

    1. Front Matter
      Pages 65-65
    2. Basil Kouvaritakis, Mark Cannon
      Pages 67-119
    3. Basil Kouvaritakis, Mark Cannon
      Pages 121-174
    4. Basil Kouvaritakis, Mark Cannon
      Pages 175-240
  5. Stochastic MPC

    1. Front Matter
      Pages 241-241
    2. Basil Kouvaritakis, Mark Cannon
      Pages 243-269
    3. Basil Kouvaritakis, Mark Cannon
      Pages 271-301
    4. Basil Kouvaritakis, Mark Cannon
      Pages 303-341
    5. Basil Kouvaritakis, Mark Cannon
      Pages 343-346
  6. Back Matter
    Pages 347-384

About this book

Introduction

For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques.

Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplicative and stochastic model uncertainty. The book provides:

  • extensive use of illustrative examples;
  • sample problems; and
  • discussion of novel control applications such as resource allocation for sustainable development and turbine-blade control for maximized power capture with simultaneously reduced risk of turbulence-induced damage.

Graduate students pursuing courses in model predictive control or more generally in advanced or process control and senior undergraduates in need of a specialized treatment will find Model Predictive Control an invaluable guide to the state of the art in this important subject. For the instructor it provides an authoritative resource for the construction of courses.

Keywords

Constained Systems Controller Parameterization Convex Optimization Model Predictive Control Textbook Probabilistic Constraints Receding-horizon Control Robust Control Robust-control-invariant Sets Stability and Recursive Feasibility Stochastic Control Textbook

Authors and affiliations

  • Basil Kouvaritakis
    • 1
  • Mark Cannon
    • 2
  1. 1.Department of EngineeringUniversity of OxfordOxfordUnited Kingdom
  2. 2.Department of EngineeringUniversity of OxfordOxfordUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-24853-0
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-24851-6
  • Online ISBN 978-3-319-24853-0
  • Series Print ISSN 1439-2232
  • Series Online ISSN 2510-3814
  • Buy this book on publisher's site
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