Model Predictive Control (Hardcover)

Eduardo F. Camacho

ISBN 10: 3031875958 / ISBN 13: 9783031875953

Editore: Springer International Publishing AG, Cham, 2026

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Hardcover. 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; andapplications: a series of case studies in solar energy generation, hospital stock control, a pilot microgrid and copper processing; along withexercises to help readers assess their progress, many with full or partial solutions in a solutions manual downloadable by adopting instructors.MATLAB programs to assist with the design aspects of the book and with reproducing some of the examples are available for readers to download.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. commercial MPC: convolution models, transfer functions, state-space models, and constraints; Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9783031875953

Dati bibliografici

• Titolo: Model Predictive Control (Hardcover)
• Editore: Springer International Publishing AG, Cham
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 10: 3031875958
• ISBN 13: 9783031875953
• Rilegatura: Hardcover
• Edizione: terza edizione
• Condizione: new

Descrizione articolo

Riassunto

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, copper mining and aviation; along with
• exercises to help readers assess their progress, many with full or partial solutions in a solutions manual downloadable by adopting instructors.

MATLAB® programs to assist with the design aspects of the book and with reproducing some of the examples are included.

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.

Informazioni sull?autore

Eduardo F. Camacho received the Ph.D. degree in electrical engineering from the University of Seville, Seville, Spain. He is now an Emeritus Professor with the Department of System and Automation Engineering, University of Seville. He is author of Model Predictive Control in the Process Industry (1995), Advanced Control of Solar Plants (1997), Model Predictive Control (1999), (Springer, 2004, 2nd edition), Control e Instrumentacion de Procesos Quimicos (Ed. Sintesis), Control of Deadtime Processes (Springer, 2007) and Control of Solar Systems (Springer, 2011, translated and printed in China Machine Press, 2014) He has served on various IFAC technical committees and chaired the IFAC publication Committee from 2002–2005. He was the president of the European Control Association (2005–2007) and chaired the IEEE/CSS International Affairs Committee (2003–2006), Chair of the IFAC Policy Committee and a member of the IEEE/CSS Board of Governors and a member of the IFAC Council. He has acted as evaluator of projects at national and European level and was appointed Manager of the Advanced Production Technology Program of the Spanish National R&D Program (1996–2000). He was one of the Spanish representatives on the Program Committee of the Growth Research program and expert for the Program Committee of the NMP research priority of the European Union. He has carried out reviews and editorial work for various technical journals and many conferences. He has been one of the Editors of the IFAC Journal, Control Engineering Practice, Editor-at-Large of the European Journal of Control and Subject Editor of Optimal Control: Methods and Applications. Dr. Camacho is an IEEE and FAC Fellow for contributions in Model Predictive Control of Solar Energy Systems. He was Publication Chair for the IFAC World Congress 2002, General Chair of the joint 44th IEEE CDC–ECC 2005, and co-General Chair of the joint 50th IEEE CDC–ECC 2011. In 2018, he was awarded an Advanced Grant by the European Research Council for a project consisting of integrating solar radiation sensors mounted in drones for controlling solar plants using MPC strategies.

Carlos Bordons received the Ph.D. degree in Electrical Engineering in 1994. He is currently full professor of Systems Engineering and Automatic Control at the University of Seville. His current research interests include advanced process control, especially model predictive control and its application to microgrids. He is co-author of the books Model Predictive Control in the Process Industry, Model Predictive Control (1st and 2nd edition) and Model Predictive Control of Microgrids, published by Springer. He is an Associate Editor of the journal Control Engineering Practice and was European Union Control Association Council Member from 2007 to 2015. From 2008 to 2012, he was the Managing Director of AICIA, which is the main Research and Technology Organization in Andalusia (Southern Spain). He is currently a member of the governing board of Corporación Tecnológica de Andalucía and head of the Laboratory of Engineering for Energy and Environmental Sustainability. He is a visiting professor at the University of Technology Sydney and Universidade Federal de Santa Catarina in Brazil.

José M. Maestre holds a PhD from the University of Seville, where he currently serves as a full professor. He has held positions at universities such as TU Delft, University of Pavia, Kyoto University and Tokyo Institute of Technology. He is author of Service Robotics within the Digital Home (Springer, 2011), A Programar se Aprende Jugando (Paraninfo, 2017),  and Sistemas de Medida y Regulación (Paraninfo, 2018), and also editor of Distributed Model Predictive Control Made Easy (Springer, 2014) and Control Systems Benchmarks (Springer, 2025). His research focuses on the control of distributed cyber-physical systems, with a special emphasis on integrating heterogeneous agents into the control loop. He has published more than 200 journal and conference papers and has led multiple research projects. Finally, his achievements have been recognized with several awards and honors, including the Spanish Royal Academy of Engineering’s medal for his contributions to predictive control in large-scale systems.