Overview

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.

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9783319248516

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Reviews

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)

Author Information

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.

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