Model Predictive Control in the Process Industry

Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.

1 Introduction to Model Based Predictive Control. - 1. 1 MPC Strategy. - 1. 2 Historical Perspective. - 1. 3 Outline of the chapters. - 2 Model Based Predictive Controllers. - 2. 1 MPC Elements. - 2. 2 Review of some MPC Algorithms. - 2. 3 MPC Based on the Impulse Response. - 2. 4 Generalized Predictive Control. - 2. 5 Constrained Receding-Horizon Predictive Control. - 2. 6 Stable GPC. - 2. 7 Filter Polynomials for Improving Robustness. - 3 Simple Implementation of GPC for Industrial Processes. - 3. 1 Plant Model. - 3. 2 The Dead Time Multiple of Sampling Time Case. - 3. 3 The Dead Time non Multiple of the Sampling Time Case. - 3. 4 Integrating Processes. - 3. 5 Consideration of Ramp Setpoints. - 4 Robustness Analysis in Precomputed GPC. - 4. 1 Structured Uncertainties. - 4. 2 Stability Limits with Structured Uncertainties. - 4. 3 Unstructured Uncertainties. - 4. 4 Relationship between the two Types of Uncertainties. - 4. 5 General Comments. - 5 Multivariate GPC. - 5. 1 Derivation of Multivariable GPC. - 5. 2 Obtaining a Matrix Fraction Description. - 5. 3 State Space Formulation. - 5. 4 Dead Time Problems. - 5. 5 Example: Distillation Column. - 6 Constrained MPC. - 6. 1 Constraints and GPC. - 6. 2 Revision of Main Quadratic Programming Algorithms. - 6. 3 Constraints Handling. - 6. 4 1-norm. - 6. 5 Constrained MPC and Stability. - 7 Robust MPC. - 7. 1 Process Models and Uncertainties. - 7. 2 Objective Functions. - 7. 3 Illustrative Examples. - 8 Applications. - 8. 1 Solar Power Plant. - 8. 2 Composition Control in an Evaporator. - 8. 3 Pilot Plant. - A Revision of the Simplex method. - A. 1 Equality Constraints. - A. 2 Finding an Initial Solution. - A. 3 Inequality Constraints. - B Model Predictive Control Simulation Program. - References.