Model Predictive Control

The second edition of this book provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. All of the material is thoroughly updated and there are entirely new chapters. New exercises and examples have been added.

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. The book demonstrates that a powerful technique does not always require complex control algorithms. All of the material is thoroughly updated for the second edition and there are entirely new chapters. Many new exercises and examples have also been added throughout and Matlab® programs to aid in their solution can be downloaded from the authors' website. This book gives the student access to a widely-used form of control which will be required by many industrial careers.

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)

Inhalt
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.