Model Predictive Control

ISBN-13: 9783540762416 / Angielski / Miękka / 1999 / 280 str.

Carlos Bordons Alba

Opis

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.

Recenzje

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)

Spis treści

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.