Predictive Control
Description
* 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.
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Persons
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.
Content
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 Decomposition-Coordination 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 Summary 179
References 179
7 Synthesis of Robust Model Predictive Control 181
7.1 Robust Predictive Control for Systems with Polytopic Uncertainties 181
7.1.1 Synthesis of RMPC Based on Ellipsoidal Invariant Sets 181
7.1.2 Improved RMPC with Parameter-Dependent Lyapunov Functions 187
7.1.3 Synthesis of RMPC with Dual-Mode Control 191
7.1.4 Synthesis of RMPC with Multistep Control Sets 199
7.2 Robust Predictive Control for Systems with Disturbances 205
7.2.1 Synthesis with Disturbance Invariant Sets 205
7.2.2 Synthesis with Mixed H2/H8 Performances 209
7.3 Strategies for Improving Robust Predictive Controller Design 214
7.3.1 Difficulties for Robust Predictive Controller Synthesis 214
7.3.2 Efficient Robust Predictive Controller 216
7.3.3 Off-Line Design and Online Synthesis 220
7.3.4 Synthesis of the Robust Predictive Controller by QP 223
7.4 Summary 227
References 228
8 Predictive Control for Nonlinear Systems 231
8.1 General Description of Predictive Control for Nonlinear Systems 231
8.2 Predictive Control for Nonlinear Systems Based on Input-Output Linearization 235
8.3 Multiple Model Predictive Control Based on Fuzzy Clustering 241
8.4 Neural Network Predictive Control 248
8.5 Predictive Control for Hammerstein Systems 253
8.6 Summary 256
References 257
9 Comprehensive Development of Predictive Control Algorithms and Strategies 259
9.1 Predictive Control Combined with Advanced Structures 259
9.1.1 Predictive Control with a Feedforward-Feedback Structure 259
9.1.2 Cascade Predictive Control 262
9.2 Alternative Optimization Formulation in Predictive Control 267
9.2.1 Predictive Control with Infinite Norm Optimization 267
9.2.2 Constrained Multiobjective Multidegree of Freedom Optimization and Satisfactory Control 270
9.3 Input Parametrization of Predictive Control 277
9.3.1 Blocking Strategy of Optimization Variables 277
9.3.2 Predictive Functional Control 279
9.4 Aggregation of the Online Optimization Variables in Predictive Control 281
9.4.1 General Framework of Optimization Variable Aggregation in Predictive Control 282
9.4.2 Online Optimization Variable Aggregation with Guaranteed Performances 284
9.5 Summary 294
References 294
10 Applications of Predictive Control 297
10.1 Applications of Predictive Control in Industrial Processes 297
10.1.1 Industrial Application and Software Development of Predictive Control 297
10.1.2 The Role of Predictive Control in Industrial Process Optimization 300
10.1.3 Key Technologies of Predictive Control Implementation 302
10.1.4 QDMC for a Refinery Hydrocracking Unit 308
10.1.4.1 Process Description and Control System Configuration 309
10.1.4.2 Problem Formulation and Variable Selection 310
10.1.4.3 Plant Testing and Model Identification 310
10.1.4.4 Off-Line Simulation and Design 311
10.1.4.5 Online Implementation and Results 312
10.2 Applications of Predictive Control in Other Fields 313
10.2.1 Brief Description of Extension of Predictive Control Applications 313
10.2.2 Online Optimization of a Gas Transportation Network 318
10.2.2.1 Problem Description for Gas Transportation Network Optimization 318
10.2.2.2 Black Box Technique and Online Optimization 320
10.2.2.3 Application Example 321
10.2.2.4 Hierarchical Decomposition for a Large-Scale Network 323
10.2.3 Application of Predictive Control in an Automatic Train Operation System 323
10.2.4 Hierarchical Predictive Control of Urban Traffic Networks 328
10.2.4.1 Two-Level Hierarchical Control Framework 328
10.2.4.2 Upper Level Design 329
10.2.4.3 Lower Level Design 331
10.2.4.4 Example and Scenarios Setting 331
10.2.4.5 Results and Analysis 332
10.3 Embedded Implementation of Predictive Controller with Applications 335
10.3.1 QP Implementation in FPGA with Applications 337
10.3.2 Neural Network QP Implementation in DSP with Applications 343
10.4 Summary 347
References 351
11 Generalization of Predictive Control Principles 353
11.1 Interpretation of Methodological Principles of Predictive Control 353
11.2 Generalization of Predictive Control Principles to General Control Problems 355
11.2.1 Description of Predictive Control Principles in Generalized Form 355
11.2.2 Rolling Job Shop Scheduling in Flexible Manufacturing Systems 358
11.2.3 Robot Rolling Path Planning in an Unknown Environment 363
11.3 Summary 367
References 367
Index 369
