Chapter 1 Introduction 1
1.1 Networked Control System 1
1.2 Model Predictive Control 5
1.3 Distributed Model Predictive Control 8
1.4 Event Triggered Control 13
1.5 Outline of This Book 16
References 18
Chapter 2 DMPC of Networked Systems with Event-Triggered Computation 27
2.1 Introduction 27
2.2 DMPC with a Cooperative Event-Triggered Computation Mechanism 29
2.2.1 Optimization Problem Formulation 29
2.2.2 Cooperative Event-Triggering Condition 33
2.2.3 Cooperative Event-Triggered DMPC Algorithm 36
2.2.4 Feasibility and Stability Analysis of the Overall Closed-Loop System 37
2.2.5 Example 41
2.3 DMPC with a Decentralized Event-Triggered Computation Mechanism 44
2.3.1 Optimization Problem Formulation 44
2.3.2 Decentralized Event-Triggering Condition 47
2.3.3 Event-Triggered Dual-Mode DMPC Algorithm 50
2.3.4 Feasibility and Stability Analysis of the Overall Closed-Loop System 50
2.3.5 Example 52
2.4 Discussion 55
References 56
Chapter 3 DMPC of Networked Systems with Event-Triggered Communication 57
3.1 Introduction 57
3.2 Optimization Problem Formulation 58
3.3 DMPC with Event-Triggered Communication 60
3.3.1 Event-Triggered Condition 60
3.3.2 DMPC Algorithm with Event-Triggered Communication 67
3.4 Feasibility and Stability Analysis 68
3.5 Example 70
3.6 Conclusion 75
References 75
Chapter 4 Dynamic Event-Triggered DMPC of Networked Systems 77
4.1 Introduction 77
4.2 Optimization Problem Formulation 79
4.3 Event-Triggering Condition 80
4.4 Dynamic Event-Triggering Condition 84
4.5 Dynamic Event-Triggered DMPC Algorithm 86
4.6 Performance Analysis 87
4.7 Example 90
4.8 Conclusion 93
References 93
Chapter 5 Mixed Time/Event-Triggered DMPC of Networked Systems 95
5.1 Introduction 95
5.2 Problem Formulation 96
5.2.1 Event-Triggered DMPC Optimization Problem 98
5.2.2 Time-Triggered DMPC Optimization Problem 99
5.3 Mixed Time/Event-Triggered Dual-Mode DMPC 101
5.3.1 Event-Triggering Condition 101
5.3.2 Mixed Time/Event-Triggered Dual-Mode DMPC Algorithm 105
5.4 Feasibility and Stability Analysis 107
5.5 Example 113
5.6 Conclusion 118
References 118
Chapter 6 Self-Triggered DMPC of Networked Systems 121
6.1 Introduction 121
6.2 Problem Formulation 123
6.3 Co-Design of Self-Triggered Mechanism and DMPC Strategy 124
6.3.1 Self-Triggered DMPC Optimization Control Problem 124
6.3.2 Explicit Solution of the Self-Triggered MPC 127
6.3.3 Self-Triggered DMPC Algorithm 130
6.4 Stability Analysis 131
6.5 Example 133
6.6 Conclusion 140
References 141
Chapter 7 Event-Triggered Distributed Model Predictive Control for Interconnected Networked Systems 143
7.1 Introduction 143
7.2 Optimization Problem Formulation 145
7.3 Event-Triggering Conditions for NCSs with Dynamic Coupling 151
7.4 Event-Triggered DMPC Algorithm for Interconnected NCSs 156
7.5 Feasibility and Stability Analysis 158
7.6 Example 165
7.7 Conclusion 173
References 173
內容試閱：
Networked Control Systems (NCSs) are spatially distributed systems in which the communication between sensors，actuators，and controllers is carried through a shared band-limited communication network. Examples of such systems include cyber-enabled manufacturing，smart grids，and water and sewage networks.Assisted by the highly efficient communication technology in networks， NCSs have the potential to achieve a consistent behavior among multiple distributed subsystems through the coordination and optimization of local controllers.
Distributed Model Predictive Control (DMPC) for such a complex system has been attracted much attention during the past twenty years，and most literatures on DMPC require these subsystems to communicate with each other in a synchronous manner.However，due to constrained network resources，the controller cannot exchange information as frequently and massively as they could in theory. The design of DMPC should consider not only the control requirements but also communication resources.This book is inspired by the development of DMPC of networked systems to save computation and communication resources.
The major new contribution is to show how to design efficient DMPCs that can be coordinated asynchronously with the increasing effectiveness of event-triggering mechanism，and how to improve the event-triggered DMPC for different requirements，namely improvement of control performance，extension to interconnected networked systems，etc. In Chap. 1，we recall the main concepts and some fundamental results of model predictive control and event-trig gered/self-triggered control for NCSs. Some existing results on the event-triggered/self-triggered mechanisms，stability of the overall closed-loop system under DMPC strategies are provided. The DMPC approaches for of NCSs are presented in Chaps.2-4.In Chap. 2，the DMPC of NCSs with event-triggered computation has been studied.The triggering conditions are obtained to decrease the solving frequency of optimization problems for each subsystem and a decentralized event-triggered dual-mode DMPC strategy is proposed to reduce information exchanges with neighboring subsystems. To further reduce the energy consumption in communication，the DMPC approaches of NCSs with event-triggered communication are introduced in Chap. 3.In Chap.4，a dynamic event-triggering condition is presented，and we show that a larger inter-execution time can be obtained and the trade-off between resource usage and control performance is achieved. More complex scenarios are considered in Chaps.5-7.A mixed time/event-triggered DMPC algorithm for the wired/wireless NCSs is introduced to improve system control performance in Chap. 5，and its feasibility and stability are also detailed. To avoid predefined triggering conditions continuously checked，the self-triggered DMPC is considered in Chap. 6，where the next triggering instant is predetermined based on the past information available at a triggering instant. At last，the event-triggered DMPC for large-scale NCSs with dynamic coupling is introduced in Chap. 7， two event-triggering conditions are established and comparability constraints on the predictive states at the triggering instants are imposed into the optimization problem，then feasibility and stability of the overall system are analyzed，respectively.
This book tries to make the readers understand the asynchronous distributed MPC，and to give a guidance to readers for designing distributed MPC and applying it to different areas. This book would be useful for the graduated students who are interest in the distributed MPC，and the persons who are engaged in researching control theory in academic institutes，university，and control engineering fields.
Yuanyuan Zou
Shaoyuan Li
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