Model Predictive Control / Edition 2

by Eduardo F. Camacho, Carlos Bordons Alba

Overview

From power plants to sugar refining, model predictive control (MPC) schemes have established themselves as the preferred control strategies for a wide variety of processes. The second edition of "Model Predictive Control" provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners. "Model Predictive Control" demonstrates that a powerful technique does not always require complex control algorithms. The text features material on the following subjects: general MPC elements and algorithms; commercial MPC schemes; generalized predictive control multivariable, robust, constrained nonlinear and hybrid MPC; fast methods for MPC implementation; applications.

All of the material is thoroughly updated for the second edition with the chapters on nonlinear MPC, MPC and hybrid systems and MPC implementation being entirely new. Many new exercises and examples have also have also been added throughout and MATLAB^® programs to aid in their solution can be downloaded from extras.springer.com. The text is an excellent aid for graduate and advanced undergraduate students and will also be of use to researchers and industrial practitioners wishing to keep abreast of a fast-moving field.

Product Details

ISBN-13:	9781852336943
Publisher:	Springer London
Publication date:	05/13/2004
Series:	Advanced Textbooks in Control and Signal Processing
Edition description:	2nd ed. 2004
Pages:	405
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

About the Author

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.

Table of Contents

1 Introduction to Model Predictive Control.- 1.1 MPC Strategy.- 1.2 Historical Perspective.- 1.3 Industrial Technology.- 1.4 Outline of the Chapters.- 2 Model Predictive Controllers.- 2.1 MPC Elements.- 2.2 Review of Some MPC Algorithms.- 2.3 State Space Formulation.- 3 Commercial Model Predictive Control Schemes.- 3.1 Dynamic Matrix Control.- 3.2 Model Algorithmic Control.- 3.3 Predictive Functional Control.- 3.4 Case Study: A Water Heater.- 3.5 Exercises.- 4 Generalized Predictive Control.- 4.1 Introduction.- 4.2 Formulation of Generalized Predictive Control.- 4.3 The Coloured Noise Case.- 4.4 An Example.- 4.5 Closed-Loop Relationships.- 4.6 The Role of the T Polynomial.- 4.7 The P Polynomial.- 4.8 Consideration of Measurable Disturbances.- 4.9 Use of a Different Predictor in GPC.- 4.10 Constrained Receding Horizon Predictive Control.- 4.11 Stable GPC.- 4.12 Exercises.- 5 Simple Implementation of GPC for Industrial Processes.- 5.1 Plant Model.- 5.2 The Dead Time Multiple of the Sampling Time Case.- 5.3 The Dead Time Nonmultiple of the Sampling Time Case.- 5.4 Integrating Processes.- 5.5 Consideration of Ramp Setpoints.- 5.6 Comparison with Standard GPC.- 5.7 Stability Robustness Analysis.- 5.8 Composition Control in an Evaporator.- 5.9 Exercises.- 6 Multivariable Model Predictive Control.- 6.1 Derivation of Multivariable GPC.- 6.2 Obtaining a Matrix Fraction Description.- 6.3 State Space Formulation.- 6.4 Case Study: Flight Control.- 6.5 Convolution Models Formulation.- 6.6 Case Study: Chemical Reactor.- 6.7 Dead Time Problems.- 6.8 Case Study: Distillation Column.- 6.9 Multivariable MPC and Transmission Zeros.- 6.10 Exercises.- 7 Constrained Model Predictive Control.- 7.1 Constraints and MPC.- 7.2 Constraints and Optimization.- 7.3 Revision of Main Quadratic Programming Algorithms.- 7.4 Constraints Handling.- 7.5 1-norm.- 7.6 Case Study: A Compressor.- 7.7 Constraint Management.- 7.8 Constrained MPC and Stability.- 7.9 Multiobjective MPC.- 7.10 Exercises.- 8 Robust Model Predictive Control.- 8.1 Process Models and Uncertainties.- 8.2 Objective Functions.- 8.3 Robustness by Imposing Constraints.- 8.4 Constraint Handling.- 8.5 Illustrative Examples.- 8.6 Robust MPC and Linear Matrix Inequalities.- 8.7 Closed-Loop Predictions.- 8.8 Exercises.- 9 Nonlinear Model Predictive Control.- 9.1 Nonlinear MPC Versus Linear MPC.- 9.2 Nonlinear Models.- 9.3 Solution of the NMPC Problem.- 9.4 Techniques for Nonlinear Predictive Control.- 9.5 Stability and Nonlinear MPC.- 9.6 Case Study: pH Neutralization Process.- 9.7 Exercises.- 10 Model Predictive Control and Hybrid Systems.- 10.1 Hybrid System Modelling.- 10.2 Example: A Jacket Cooled Batch Reactor.- 10.3 Model Predictive Control of MLD Systems.- 10.4 Piecewise Affine Systems.- 10.5 Exercises.- 11 Fast Methods for Implementing Model Predictive Control.- 11.1 Piecewise Affinity of MPC.- 11.2 MPC and Multiparametric Programming.- 11.3 Piecewise Implementation of MPC.- 11.4 Fast Implementation of MPC forUncertain Systems.- 11.5 Approximated Implementation for MPC.- 11.6 Fast Implementation of MPC and Dead Time Considerations.- 11.7 Exercises.- 12 Applications.- 12.1 Solar Power Plant.- 12.2 Pilot Plant.- 12.3 Model Predictive Control in a Sugar Refinery.- 12.4 Olive Oil Mill.- 12.5 Mobile Robot.- A Revision of the Simplex Method.- A.1 Equality Constraints.- A.2 Finding an Initial Solution.- A.3 Inequality Constraints.- B Dynamic Programming and Linear Quadratic Optimal Control.- B.1 LinearQuadratic Problem.- B.2 InfiniteHorizon.- References.

Editorial Reviews

From the reviews of the second edition:

"This text is an introduction to model predictive control, a control methodology which has encountered some success in industry, but which still presents many theoretical challenges. … The book is of interest as an introduction to model predictive control, and a merit is the special presentation, connecting the subject intimately with industrial situations." (A. Akutowicz, Zentralblatt MATH, Vol. 1080, 2006)

"It is a much more ambitious work, seeking to inform practitioners how to implement MPC while at the same time serving as an advanced student text as well as reference for control researchers. … The authors clearly see the text as a teaching aid since several chapters include exercises. … In summary, a significant contribution to this important field for control academics, and some highly experienced MPC practitioners … ." (Michael Brisk, www.tcetoday.com, February, 2008)

From the Publisher