Model Predictive Control

In recent years Model Predictive Control (MPC) schemes have established themselves as the preferred control strategy for a large number of processes. Their ability to handle constraints and multivariable processes and their intuitive way of posing the pro cess control problem in the time domain are two reasons for their popularity. This volume by authors of international repute provides an extensive review concerning the theoretical and practical aspects of predictive controllers. It describes the most commonly used MPC strategies, especially Generalised Predictive Control (GPC), showing both their theoretical properties and their practical implementation issues. Topics such as multivariable MPC, constraint handling, stability and robustness properties are thoroughly analysed in this text.

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, demonstrating that a powerful technique does not always require complex control algorithms. It has been thoroughly updated for the second edition with new chapters on nonlinear MPC and MPC implementation and many new exercises and examples.

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.