Model Predictive Control: Classical, Robust and Stochastic

✍ Scribed by Basil Kouvaritakis, Mark Cannon

• Publisher: Springer
• Year: 2015
• Tongue: English
• Leaves: 387
• Series: Advanced Textbooks in Control and Signal Processing
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xiii
Introduction....Pages 1-9
Front Matter....Pages 11-11
MPC with No Model Uncertainty....Pages 13-64
Front Matter....Pages 65-65
Open-Loop Optimization Strategies for Additive Uncertainty....Pages 67-119
Closed-Loop Optimization Strategies for Additive Uncertainty....Pages 121-174
Robust MPC for Multiplicative and Mixed Uncertainty....Pages 175-240
Front Matter....Pages 241-241
Introduction to Stochastic MPC....Pages 243-269
Feasibility, Stability, Convergence and Markov Chains....Pages 271-301
Explicit Use of Probability Distributions in SMPC....Pages 303-341
Conclusions....Pages 343-346
Back Matter....Pages 347-384

✦ Subjects

Control;Systems Theory, Control;Industrial Chemistry/Chemical Engineering;Automotive Engineering;Aerospace Technology and Astronautics