Predictive Control

This book is a comprehensive introduction to model predictive control (MPC), including its basic principles and algorithms, system analysis and design methods, strategy developments and practical applications. The main contents of the book include an overview of the development trajectory and basic principles of MPC, typical MPC algorithms, quantitative analysis of classical MPC systems, design and tuning methods for MPC parameters, constrained multivariable MPC algorithms and online optimization decomposition methods. Readers will then progress to more advanced topics such as nonlinear MPC and its related algorithms, the diversification development of MPC with respect to control structures and optimization strategies, and robust MPC. Finally, applications of MPC and its generalization to optimization-based dynamic problems other than control will be discussed.

• Systematically introduces fundamental concepts, basic algorithms, and applications of MPC

• Includes a comprehensive overview of MPC development, emphasizing recent advances and modern approaches

• Features numerous MPC models and structures, based on rigorous research

• Based on the best-selling Chinese edition, which is a key text in China

Predictive Control: Fundamentals and Developments is written for advanced undergraduate and graduate students and researchers specializing in control technologies. It is also a useful reference for industry professionals, engineers, and technicians specializing in advanced optimization control technology.

Auteur

Yugeng Xi is a Chair Professor of Shanghai Jiao Tong University (SJTU). He received Dr.-Ing. degree on automatic control from Technical University Munich, Germany in 1984. Since then he has been with the Department of Automation, SJTU. His research interests include predictive control theory and applications, control and optimization of large scale complex systems. He has been working in the area of predictive control for more than 35 years.?? Dewei Li is an Associate Professor of Shanghai Jiao Tong University (SJTU). He received PhD. degree on automatic control from SJTU, China in 2009. From 2011, he has been with the Department of Automation, SJTU. His research interests include predictive control theory and applications, the control of robots, intelligent systems, control and optimization of large scale complex systems. He has been working in the area of predictive control for more than 10 years.

Contenu
Preface xi

1 Brief History and Basic Principles of Predictive Control 1

1.1 Generation and Development of Predictive Control 1

1.2 Basic Methodological Principles of Predictive Control 6

1.2.1 Prediction Model 6

1.2.2 Rolling Optimization 6

1.2.3 Feedback Correction 7

1.3 Contents of this Book 10

References 11

2 Some Basic Predictive Control Algorithms 15

2.1 Dynamic Matrix Control (DMC) Based on the Step Response Model 15

2.1.1 DMC Algorithm and Implementation 15

2.1.2 Description of DMC in the State Space Framework 21

2.2 Generalized Predictive Control (GPC) Based on the Linear Difference Equation Model 25

2.3 Predictive Control Based on the State Space Model 32

2.4 Summary 37

References 39

3 Trend Analysis and Tuning of SISO Unconstrained DMC Systems 41

3.1 The Internal Model Control Structure of the DMC Algorithm 41

3.2 Controller of DMC in the IMC Structure 48

3.2.1 Stability of the Controller 48

3.2.2 Controller with the One-Step Optimization Strategy 53

3.2.3 Controller for Systems with Time Delay 54

3.3 Filter of DMC in the IMC Structure 56

3.3.1 Three Feedback Correction Strategies and Corresponding Filters 56

3.3.2 Influence of the Filter to Robust Stability of the System 60

3.4 DMC Parameter Tuning Based on Trend Analysis 62

3.5 Summary 72

References 73

4 Quantitative Analysis of SISO Unconstrained Predictive Control Systems 75

4.1 Time Domain Analysis Based on the Kleinman Controller 76

4.2 Coefficient Mapping of Predictive Control Systems 81

4.2.1 Controller of GPC in the IMC Structure 81

4.2.2 Minimal Form of the DMC Controller and Uniform Coefficient Mapping 86

4.3 Z Domain Analysis Based on Coefficient Mapping 90

4.3.1 Zero Coefficient Condition and the Deadbeat Property of Predictive Control Systems 90

4.3.2 Reduced Order Property and Stability of Predictive Control Systems 94

4.4 Quantitative Analysis of Predictive Control for Some Typical Systems 98

4.4.1 Quantitative Analysis for First-Order Systems 98

4.4.2 Quantitative Analysis for Second-Order Systems 104

4.5 Summary 112

References 113

5 Predictive Control for MIMO Constrained Systems 115

5.1 Unconstrained DMC for Multivariable Systems 115

5.2 Constrained DMC for Multivariable Systems 123

5.2.1 Formulation of the Constrained Optimization Problem in Multivariable DMC 123

5.2.2 Constrained Optimization Algorithm Based on the Matrix Tearing Technique 125

5.2.3 Constrained Optimization Algorithm Based on QP 128

5.3 Decomposition of Online Optimization for Multivariable Predictive Control 132

5.3.1 Hierarchical Predictive Control Based on DecompositionCoordination 133

5.3.2 Distributed Predictive Control 137

5.3.3 Decentralized Predictive Control 140

5.3.4 Comparison of Three Decomposition Algorithms 143

5.4 Summary 146

References 147

6 Synthesis of Stable Predictive Controllers 149

6.1 Fundamental Philosophy of the Qualitative Synthesis Theory of Predictive Control 150

6.1.1 Relationships between MPC and Optimal Control 150

6.1.2 Infinite Horizon Approximation of Online Open-Loop Finite Horizon Optimization 152

6.1.3 Recursive Feasibility in Rolling Optimization 155

6.1.4 Preliminary Knowledge 157

6.2 Synthesis of Stable Predictive Controllers 163

6.2.1 Predictive Control with Zero Terminal Constraints 163

6.2.2 Predictive Control with Terminal Cost Functions 165

6.2.3 Predictive Control with Terminal Set Constraints 170

6.3 General Stability Conditions of Predictive Control and Suboptimality Analysis 174

6.3.1 General Stability Conditions of Predictive Control 174

6.3.2 Suboptimality Analysis of Predictive Control 177 6.4 Summa...