Название: Model Predictive Control, 3rd Edition
Автор: Eduardo F. Camacho, Carlos Bordons, José M. Maestre
Издательство: Springer
Серия: Advanced Textbooks in Control and Signal Processing
Год: 2026
Страниц: 390
Язык: английский
Формат: pdf (true), epub
Размер: 40.9 MB

Model Predictive Control (MPC), the classic textbook for students and practitioners seeking deep understanding of advanced control systems, is now revised, updated and reorganized in a streamlined third edition. The authors, renowned researchers in the field, cover an extensive range of topics that embraces the basic and the advanced, the theoretical and the applied.

The book offers advanced undergraduate and graduate students an accessible, step-by-step approach that enables them progressively to grasp and apply the concepts they are studying. For instructors, this is an invaluable curriculum resource packed with examples and case studies. The text features material on:

- commercial MPC: convolution models, transfer functions, state-space models, and constraints;
- advanced topics: robust and stochastic MPC and MPC for nonlinear, hybrid, large-scale, and distributed systems; and
- applications: a series of case studies in solar energy generation, hospital stock control, a pilot microgrid and copper processing; along with
- exercises to help readers assess their progress, many with full or partial solutions in a solutions manual downloadable by adopting instructors.

Model predictive control has developed considerably over the last 50 years, both within the research control community and in industry. This success can be attributed to the fact that model predictive control is, perhaps, the most general way of posing the process control problem in the time domain. Model predictive control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references when available. Another advantage of model predictive control is that because of the finite control horizon used, constraints and, in general, nonlinear processes which are frequently found in industry, can be handled. Likewise, the previous lack of rigorous theoretical proofs regarding stability and robustness has been solved in the last two decades, promoting the dissemination of model predictive control across different application fields and new research communities. Some new and very promising results in this context allow one to think that this control technique will experience greater expansion, especially as it can be combined with other methods within the broader field of artificial intelligence. In this regard, being a computer-based method, model predictive control benefits directly from continuous developments in information and communication technologies, further enhancing its capabilities.

The implementation of predictive controllers requires some mathematical complexities which can pose challenges for practicing control engineers. One of the goals of this text is to contribute to filling the gap between the empirical way in which practitioners tend to use control algorithms and the powerful but sometimes abstractly formulated techniques developed by control researchers. The book focuses on implementation issues for model predictive controllers and intends to present easy ways of implementing them in industry. The book also aims to serve as a guide to implement model predictive control and as a motivation for doing so by showing that using such a powerful control technique does not require complex control algorithms. For these reasons, the book is aimed mainly at practitioners, although it can be followed by a wide range of readers, as only basic knowledge of control theory and sample data systems is required. A general survey of the field, and guidance in the choice of appropriate implementation techniques, as well as many illustrative examples, are given for practicing engineers and senior undergraduate and graduate students.

Finally, to facilitate hands-on practice, we provide a collection of MATLAB files to run, reproduce, and extend selected examples, complementing the code included in the book.

Model Predictive Control (third edition)’s distinctive strength is its real-world relevance. It is an essential tool for future engineers; its focus on practical implementation, bridging the gap between academic theory and industrial practice and supplemented by exploration of optimization- and algorithm-related aspects of MPC, ensures a holistic treatment of the subject.






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