DISTRIBUTED MODEL PREDICTIVE CONTROL FOR PLANT-WIDE SYSTEMS
DISTRIBUTED MODEL PREDICTIVE CONTROL FOR PLANT-WIDE SYSTEMS
In this book, experienced researchers gave a thorough explanation of distributed model predictive control (DMPC): its basic concepts, technologies, and implementation in plant-wide systems. Known for its error tolerance, high flexibility, and good dynamic performance, DMPC is a popular topic in the control field and is widely applied in many industries.
To efficiently design DMPC systems, readers will be introduced to several categories of coordinated DMPCs, which are suitable for different control requirements, such as network connectivity, error tolerance, performance of entire closed-loop systems, and calculation of speed. Various real-life industrial applications, theoretical results, and algorithms are provided to illustrate key concepts and methods, as well as to provide solutions to optimize the global performance of plant-wide systems.
* Features system partition methods, coordination strategies, performance analysis, and how to design stabilized DMPC under different coordination strategies.
* Presents useful theories and technologies that can be used in many different industrial fields, examples include metallurgical processes and high-speed transport.
* Reflects the authors' extensive research in the area, providing a wealth of current and contextual information.
Distributed Model Predictive Control for Plant-Wide Systems is an excellent resource for researchers in control theory for large-scale industrial processes. Advanced students of DMPC and control engineers will also find this as a comprehensive reference text.
Autorentext
SHAOYUAN LI Shanghai Jiao Tong University, China
YI ZHENG Shanghai Jiao Tong University, China
Zusammenfassung
DISTRIBUTED MODEL PREDICTIVE CONTROL FOR PLANT-WIDE SYSTEMS
DISTRIBUTED MODEL PREDICTIVE CONTROL FOR PLANT-WIDE SYSTEMS
In this book, experienced researchers gave a thorough explanation of distributed model predictive control (DMPC): its basic concepts, technologies, and implementation in plant-wide systems. Known for its error tolerance, high flexibility, and good dynamic performance, DMPC is a popular topic in the control field and is widely applied in many industries.
To efficiently design DMPC systems, readers will be introduced to several categories of coordinated DMPCs, which are suitable for different control requirements, such as network connectivity, error tolerance, performance of entire closed-loop systems, and calculation of speed. Various real-life industrial applications, theoretical results, and algorithms are provided to illustrate key concepts and methods, as well as to provide solutions to optimize the global performance of plant-wide systems.
- Features system partition methods, coordination strategies, performance analysis, and how to design stabilized DMPC under different coordination strategies.
- Presents useful theories and technologies that can be used in many different industrial fields, examples include metallurgical processes and high-speed transport.
- Reflects the authors' extensive research in the area, providing a wealth of current and contextual information.
Distributed Model Predictive Control for Plant-Wide Systems is an excellent resource for researchers in control theory for large-scale industrial processes. Advanced students of DMPC and control engineers will also find this as a comprehensive reference text.
Inhalt
Preface xi
About the Authors xv
Acknowledgement xvii
List of Figures xix
List of Tables xxiii
1 Introduction 1
1.1 Plant-Wide System 1
1.2 Control System Structure of the Plant-Wide System 3
1.2.1 Centralized Control 4
1.2.2 Decentralized Control and Hierarchical Coordinated Decentralized Control 5
1.2.3 Distributed Control 6
1.3 Predictive Control 8
1.3.1 What is Predictive Control 8
1.3.2 Advantage of Predictive Control 9
1.4 Distributed Predictive Control 9
1.4.1 Why Distributed Predictive Control 9
1.4.2 What is Distributed Predictive Control 10
1.4.3 Advantage of Distributed Predictive Control 10
1.4.4 Classification of DMPC 11
1.5 About this Book 13
Part I FOUNDATION
2 Model Predictive Control 19
2.1 Introduction 19
2.2 Dynamic Matrix Control 20
2.2.1 Step Response Model 20
2.2.2 Prediction 21
2.2.3 Optimization 22
2.2.4 Feedback Correction 23
2.2.5 DMC with Constraint 24
2.3 Predictive Control with the State Space Model 26
2.3.1 System Model 27
2.3.2 Performance Index 28
2.3.3 Prediction 28
2.3.4 Closed-Loop Solution 30
2.3.5 State Space MPC with Constraint 31
2.4 Dual Mode Predictive Control 33
2.4.1 Invariant Region 33
2.4.2 MPC Formulation 34
2.4.3 Algorithms 35
2.4.4 Feasibility and Stability 36
2.5 Conclusion 37
3 Control Structure of Distributed MPC 39
3.1 Introduction 39
3.2 Centralized MPC 40
3.3 Single-Layer Distributed MPC 41
3.4 Hierarchical Distributed MPC 42
3.5 Example of the Hierarchical DMPC Structure 43
3.6 Conclusion 45
4 Structure Model and System Decomposition 47
4.1 Introduction 47
4.2 System Mathematic Model 48
4.3 Structure Model and Structure Controllability 50
4.3.1 Structure Model 50
4.3.2 Function of the Structure Model in System Decomposition 51
4.3.3 InputOutput Accessibility 53
4.3.4 General Rank of the Structure Matrix 56
4.3.5 Structure Controllability 56
4.4 Related Gain Array Decomposition 58
4.4.1 RGA Definition 59
4.4.2 RGA Interpretation 60
4.4.3 Pairing Rules 61
4.5 Conclusion 63
Part II UNCONSTRAINED DISTRIBUTED PREDICTIVE CONTROL
5 Local Cost Optimization-based Distributed Model Predictive Control 67
5.1 Introduction 67
5.2 Local Cost Optimization-based Distributed Predictive Control 68
5.2.1 Problem Description 68
5.2.2 DMPC Formulation 69
5.2.3 Closed-loop Solution 72
5.2.4 Stability Analysis 79
5.2.5 Simulation Results 79
5.3 Distributed MPC Strategy Based on Nash Optimality 82
5.3.1 Formulation 83
5.3.2 Algorithm 86
5.3.3 Computational Convergence for Linear Systems 86
5.3.4 Nominal Stability of Distributed Model Predictive Control System 88
5.3.5 Performance Analysis with Single-step Horizon Control Under Communication Failure 89
5.3.6 Simulation Results 94
5.4 Conclusion 99
Appendix 99
Appendix A. QP problem transformation 99
Appendix B. Proof of Theorem 5.1 100
6 Cooperative Distributed Predictive Control 103
6.1 Introduction 103
6.2 Noniterative Cooperative DMPC 104
6.2.1 System Description 104
6.2.2 Formulation 104
6.2.3 Closed-Form Solution 107
6.2.4 Stability and Performance Analysis 109
6.2.5 Example 113
6.3 Distributed Predictive Control based on Pareto Optimality 114
6.3.1 Formulation 118
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