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)

• 1 Introduction to Model Based Predictive Control.- 1.1 MPC Strategy.- 1.2 Historical Perspective.- 1.3 Industrial Technology.- 1.4 Outline of the Chapters.- 2 Model Based Predictive Controllers.- 2.1 MPC Elements.- 2.2 Review of some MPC Algorithms.- 2.3 Nonlinear Predictive Control.- 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.- 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.- 5 Simple Implementation of GPC for Industrial Processes.- 5.1 Plant Model.- 5.2 The Dead Time Multiple of Sampling Time Case.- 5.3 The Dead Time non Multiple 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.- 6 Multivariable MPC.- 6.1 Derivation of Multivariable GPC.- 6.2 Obtaining a Matrix Fraction Description.- 6.3 State Space Formulation.- 6.4 Dead Time Problems.- 6.5 Example: Distillation Column.- 6.6 Application of DMC to a Chemical Reactor.- 7 Constrained MPC.- 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.- 8 Robust MPC.- 8.1 Process Models and Uncertainties.- 8.2 Objective Functions.- 8.3 Illustrative Examples.- 8.4 Robust MPC and Linear Matrix Inequalities.- 9 Applications.- 9.1 Solar Power Plant.- 9.2 Pilot Plant.- 9.3 Model Predictive Control in a Sugar Refinery.- A Revision of the Simplex method.- A.1 Equality Constraints.- A.2 Finding an Initial Solution.- A.3 Inequality Constraints.- References.