Model Predictive Control - Classical, Robust and Stochastic

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.

List of contents

From the Contents: Introduction.- Classical Model Predictive Control.- Robust Model Predictive Control with Additive Uncertainty: Open-loop Optimization Strategies.- Robust Model Predictive Control with Additive Uncertainty: Closed-loop Optimization Strategies.

About the author

Both authors have lectured and tutored undergraduate students, and have supervised many final year undergraduate projects and doctoral students in control engineering at the Department of Engineering Science, University of Oxford (Doctor Cannon’s university teaching career spans 20 years whereas Professor Kouvaritakis’ spans more than 40 years). They have also been active in research, publishing hundreds of articles, in prestigious control journals. In addition they have been Investigators and Principal Investigators in several research projects, some of which are connected with industrial partners.

Summary

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.

Additional text

“This book is suitable for advanced undergraduate and graduate students as well as professional researchers and academics. …  The book will also be of interest to the practitioners of advanced process control. … the effort invested in writing this book will certainly be appreciated by its readers. … I am very happy to encourage colleagues active in conventional, robust, and stochastic MPC to acquire this book for their personal collections and make use of it in their research studies.” (Saša V. Raković, IEEE Control Systems Magazine, Vol. 36 (6), December, 2016)
“Model Predictive Control (MPC) is a very popular and successful control technique in both the academic and industrial control communities. … undoubtedly, MPC should be part of any current modern control course. This book collects together the many results of the Oxford University predictive control group which have been carried out over a long period and have been very influential in stimulating interest in both linear and nonlinear systems.” (Rosario Romera, Mathematical Reviews, October, 2016)
“This book manages to provide complete and mathematically rigorous solutions to all the raised problems, under the considered assumptions. In conclusion, the reviewed book is highly recommended to all students (and in particular starting PhD students), researchers and practitioners seeking for a self-standing, clear and mathematically rigorous exposition of the theory and design of classical, robust and stochastic MPC with a linear prediction model structure.” (Octavian Pastravanu, zbMATH 1339.93005, 2016)

Report

"This book is suitable for advanced undergraduate and graduate students as well as professional researchers and academics. ...  The book will also be of interest to the practitioners of advanced process control. ... the effort invested in writing this book will certainly be appreciated by its readers. ... I am very happy to encourage colleagues active in conventional, robust, and stochastic MPC to acquire this book for their personal collections and make use of it in their research studies." (Sasa V. Rakovic, IEEE Control Systems Magazine, Vol. 36 (6), December, 2016)
"Model Predictive Control (MPC) is a very popular and successful control technique in both the academic and industrial control communities. ... undoubtedly, MPC should be part of any current modern control course. This book collects together the many results of the Oxford University predictive control group which have been carried out over a long period and have been very influential in stimulating interest in both linear and nonlinear systems." (Rosario Romera, Mathematical Reviews, October, 2016)
"This book manages to provide complete and mathematically rigorous solutions to all the raised problems, under the considered assumptions. In conclusion, the reviewed book is highly recommended to all students (and in particular starting PhD students), researchers and practitioners seeking for a self-standing, clear and mathematically rigorous exposition of the theory and design of classical, robust and stochastic MPC with a linear prediction model structure." (Octavian Pastravanu, zbMATH 1339.93005, 2016)

Product details

• Authors: Mark Cannon, Basi Kouvaritakis, Basil Kouvaritakis
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Released: 01.11.2015
• EAN: 9783319248516
• ISBN: 978-3-31-924851-6
• No. of pages: 384
• Dimensions: 161 mm x 243 mm x 24 mm
• Weight: 803 g
• Illustrations: XIII, 384 p. 54 illus., 3 illus. in color. With online files/update.
• Series: Advanced Textbooks in Control and Signal Processing; Advanced Textbooks in Control and Signal Processing
• Subject: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering