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.

An extensive review on the subject by authors who have an international reputation and who have lectured extensively on the subject to students

An invaluable aid to students at all levels

Includes a large number of exercises and real applications

Inhalt
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.- 2.1.1 Prediction Model.- 2.1.2 Objective Function.- 2.1.3 Obtaining the Control Law.- 2.2 Review of some MPC Algorithms.- 2.3 Nonlinear Predictive Control.- 2.3.1 Nonlinear Models.- 2.3.2 Techniques for Nonlinear Predictive Control.- 3 Commercial Model Predictive Control Schemes.- 3.1 Dynamic Matrix Control.- 3.1.1 Prediction.- 3.1.2 Measurable Disturbances.- 3.1.3 Control Algorithm.- 3.2 Model Algorithmic Control.- 3.2.1 Process Model and Prediction.- 3.2.2 Control Law.- 3.2.3 Multivariable Processes.- 3.3 Predictive Functional Control.- 3.3.1 Formulation.- 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.- 4.6.1 Selection of the T Polynomial.- 4.6.2 Relationships with other Formulations.- 4.7 The P Polynomial.- 4.8 Consideration of Measurable Disturbances.- 4.9 Use of a Different Predictor in GPC.- 4.9.1 Equivalent Structure.- 4.9.2 A Comparative Example.- 4.10 Constrained Receding-Horizon Predictive Control.- 4.10.1 Computation of the Control Law.- 4.10.2 Properties.- 4.11 Stable GPC.- 4.11.1 Formulation of the Control Law.- 5 Simple Implementation of GPC for Industrial Processes.- 5.1 Plant Model.- 5.1.1 Plant Identification: The Reaction Curve Method.- 5.2 The Dead Time Multiple of Sampling Time Case.- 5.2.1 Discrete Plant Model.- 5.2.2 Problem Formulation.- 5.2.3 Computation of the Controller Parameters.- 5.2.4 Role of the Control-Weighting Factor.- 5.2.5 Implementation Algorithm.- 5.2.6 An Implementation Example.- 5.3 The Dead Time non Multiple of the Sampling Time Case.- 5.3.1 Discrete Model of the Plant.- 5.3.2 Controller Parameters.- 5.3.3 Example.- 5.4 Integrating Processes.- 5.4.1 Derivation of the Control Law.- 5.4.2 Controller Parameters.- 5.4.3 Example.- 5.5 Consideration of Ramp Setpoints.- 5.5.1 Example.- 5.6 Comparison with Standard GPC.- 5.7 Stability Robustness Analysis.- 5.7.1 Structured Uncertainties.- 5.7.2 Unstructured Uncertainties.- 5.7.3 General Comments.- 5.8 Composition Control in an Evaporator.- 5.8.1 Description of the Process.- 5.8.2 Obtaining the Linear Model.- 5.8.3 Controller Design.- 5.8.4 Results.- 6 Multivariable MPC.- 6.1 Derivation of Multivariable GPC.- 6.1.1 White Noise Case.- 6.1.2 Coloured Noise Case.- 6.1.3 Measurable Disturbances.- 6.2 Obtaining a Matrix Fraction Description.- 6.2.1 Transfer Matrix Representation.- 6.2.2 Parametric Identification.- 6.3 State Space Formulation.- 6.3.1 Matrix Fraction and State Space Equivalences.- 6.4 Dead Time Problems.- 6.5 Example: Distillation Column.- 6.6 Application of DMC to a Chemical Reactor.- 6.6.1 Plant Description.- 6.6.2 Obtaining the Plant Model.- 6.6.3 Control Law.- 6.6.4 Simulation Results.- 7 Constrained MPC.- 7.1 Constraints and MPC.- 7.1.1 Illustrative Examples.- 7.2 Constraints and optimization.- 7.3 Revision of Main Quadratic Programming Algorithms.- 7.3.1 The Active Set Methods.- 7.3.2 Feasible Directions Methods.- 7.3.3 Initial Feasible Point.- 7.3.4 Pivoting Methods.- 7.4 Constraints Handling.- 7.4.1 Slew Rate Constraints.- 7.4.2 Amplitude Constraints.- 7.4.3 Output Constraints.- 7.4.4 Constraints Reduction.- 7.5 1-norm.- 7.6 Case study : a Compressor.- 7.7 Constraint Management.- 7.7.1 Feasibility.- 7.7.2 Techniques for Improving Feasibility.- 7.8 Constrained MPC and Stability.- 7.9 Multiobjective MPC.- 7.9.1 Priorization of Objectives.- 8 Robust MPC.- 8.1 Process Models and Uncertainties.- 8.1.1 Truncated Impulse Response Uncertainties.- 8.1.2 Matrix Fraction Description Uncertainties.- 8.1.3 Global Uncertainties.- 8.2 Objective Functions.- 8.2.1 Quadratic Norm.- 8.2.2 ? ? norm.- 8.2.3 1-norm.- 8.3 Illustrative Examples.- 8.3.1 Bounds on the Output.- 8.3.2 Uncertainties in the Gain.- 8.4 Robust MPC and Linear Matrix Inequalities.- 9 Applications.- 9.1 Solar Power Plant.- 9.1.1 Self tuning GPC Control Strategy.- 9.1.2 Gain scheduling Generalized Predictive Control.- 9.2 Pilot Plant.- 9.2.1 Plant Description.- 9.2.2 Plant Control.- 9.2.3 Flow Control.- 9.2.4 Temperature Control at the Exchanger Output.- 9.2.5 Temperature Control in the Tank.- 9.2.6 Level Control.- 9.2.7 Remarks.- 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.