Model Predictive Control in the Process Industry

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.