A comprehensive review of the state of the art in the control of multi-agent systems theory and applications
The superiority of multi-agent systems over single agents for the control of unmanned air, water and ground vehicles has been clearly demonstrated in a wide range of application areas. Their large-scale spatial distribution, robustness, high scalability and low cost enable multi-agent systems to achieve tasks that could not successfully be performed by even the most sophisticated single agent systems.
Cooperative Control of Multi-Agent Systems: Theory and Applications provides a wide-ranging review of the latest developments in the cooperative control of multi-agent systems theory and applications. The applications described are mainly in the areas of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). Throughout, the authors link basic theory to multi-agent cooperative control practice — illustrated within the context of highly-realistic scenarios of high-level missions — without losing site of the mathematical background needed to provide performance guarantees under general working conditions. Many of the problems and solutions considered involve combinations of both types of vehicles. Topics explored include target assignment, target tracking, consensus, stochastic game theory-based framework, event-triggered control, topology design and identification, coordination under uncertainty and coverage control.
- Establishes a bridge between fundamental cooperative control theory and specific problems of interest in a wide range of applications areas
- Includes example applications from the fields of space exploration, radiation shielding, site clearance, tracking/classification, surveillance, search-and-rescue and more
- Features detailed presentations of specific algorithms and application frameworks with relevant commercial and military applications
- Provides a comprehensive look at the latest developments in this rapidly evolving field, while offering informed speculation on future directions for collective control systems
The use of multi-agent system technologies in both everyday commercial use and national defense is certain to increase tremendously in the years ahead, making this book a valuable resource for researchers, engineers, and applied mathematicians working in systems and controls, as well as advanced undergraduates and graduate students interested in those areas.
Inhaltsverzeichnis
List of Contributors xiii
Preface xvii
Acknowledgment xix
1 Introduction 1
Yue Wang, Eloy Garcia, David Casbeer and Fumin Zhang
1.1 Introduction 1
1.2 Chapter Summary and Contributions 11
References 17
2 Sensor Placement Algorithms for a Path Covering Problem 31
Sivakumar Rathinamand Rajnikant Sharma
2.1 Problem Statement 34
2.2 Algorithm Approx 1 35
2.2.1 Algorithm for Targets That Lie Within a Strip 36
2.2.2 Algorithm for a General Set of Points 37
2.2.3 Proof of the Approximation Ratio 38
2.3 Algorithm Approx 2 42
2.4 Numerical Results 46
2.5 Conclusions 48
References 48
3 Robust Coordination of Small UAVs for Vision-Based Target Tracking Using Output-Feedback MPC with MHE 51
Steven A. P. Quintero, David A. Copp, and João P. Hespanha
3.1 Vision-Based Target Tracking 53
3.2 Problem Formulation 58
3.2.1 UAV Dynamics 58
3.2.2 Target Dynamics and Overall State Space 61
3.2.3 Measurement Error Models 62
3.3 Robust Output-Feedback MPC/MHE 64
3.4 Simulation Results 67
3.4.1 Constant-Velocity Target 70
3.4.2 Evasive Target 73
3.4.3 Experimental Target Log 76
3.5 Conclusion and Future Work 79
References 80
4 Projection-Based Consensus for Time-Critical Coordination of Unmanned Aerial Vehicles under Velocity Constraints 85
Xiaofeng Wang, Eloy Garcia, Zheqing Zhou, Derek Kingstonand David Casbeer
4.1 Introduction 85
4.2 Problem Statement 87
4.2.1 Notations 87
4.2.2 Problem Formulation 88
4.3 Projection-Based Consensus Algorithm 89
4.4 Convergence Analysis 91
4.5 Convergence Time 96
4.6 Feasibility 101
4.7 Simulation 104
4.8 Summary 110
References 111
5 Greedy Maximization for Asset-Based Weapon–Target Assignment with Time-Dependent Rewards 115
Doo-Hyun Choand Han-Lim Choi
5.1 Introduction 115
5.2 Problem Formulation 117
5.2.1 Problem Variables 119
5.2.2 Constraints 119
5.2.3 Objective Function 120
5.3 Properties of the Objective Function 120
5.3.1 Preliminary—Greedy Algorithm 121
5.3.2 Preliminary—Maximization of Set Function 121
5.3.3 Weapon Target Assignment—Lower Bound with Greedy Algorithm 122
5.4 Algorithmic Details 126
5.4.1 Time Slot Generation 126
5.4.2 Greedy Maximization 127
5.5 Numerical Case Studies 128
5.5.1 Simple TSWTA Example 128
5.5.2 Realistic Interceptor-Ballistic Target Assignment 134
5.6 Conclusion 136
Acknowledgment 136
References 137
6 Coordinated Threat Assignments and Mission Management of Unmanned Aerial Vehicles 141
Eloy Garciaand David Casbeer
6.1 Introduction 141
6.2 Problem Statement 144
6.2.1 Preliminaries 144
6.2.2 Mission Description 144
6.3 Decentralized Assignment of Threats 148
6.3.1 Optimal Individual Paths and Selections 148
6.3.2 Decentralized Assignment Algorithm 150
6.4 Assignment Constraints 153
6.4.1 Timing Constraints 154
6.4.2 Coupled Decision Making 158
6.5 Multiple Main Targets 163
6.6 Conclusions 172
References 172
7 Event-Triggered Communication and Control for Multi-Agent Average Consensus 177
Cameron Nowzari, Jorge Cortesand George J. Pappas
7.1 Introduction 177
7.1.1 Organization 178
7.2 Preliminaries 181
7.2.1 Event-Triggered Control of Linear Systems 182
7.3 Problem Statement 185
7.4 Centralized Event-Triggered Control 186
7.5 Decentralized Event-Triggered Control 188
7.6 Decentralized Event-Triggered Communication and Control 192
7.6.1 Directed Graphs 196
7.7 Periodic Event-Triggered Coordination 199
7.8 Conclusions and Future Outlook 201
References 202
Appendix 205
8 Topology Design and Identification for Dynamic Networks 209
Chuangchuang Sunand Ran Dai
8.1 Introduction 209
8.2 Network Topology Design Problems 212
8.2.1 Network Design for Fast Convergence of Consensus Protocol 213
8.2.2 Network Design for Minimum Total Effective Resistance 215
8.2.3 Equivalent Conversion from Cardinality-Constrained Optimization Problems to RCOPs 216
8.3 Network Topology Identification Problems 216
8.3.1 LTI System Identification 216
8.3.2 Formulation of NTIs as QCQPs 219
8.3.3 Equivalent Conversion from QCQPs to RCOPs 220
8.4 Iterative Rank Minimization Approach 221
8.5 Simulation Examples 224
8.5.1 Example for Designing Fast Converging Consensus-based Network 225
8.5.2 Example for Designing Minimum Total Effective Resistance Network 226
8.5.3 Example of NTI with Agent Dynamics Driven by Consensus Protocol 227
8.6 Conclusions 231
References 232
9 Distributed Multi-Agent Coordination with Uncertain Interactions: A Probabilistic Perspective 237
Yongcan Cao, David Casbeer, Eloy Garciaand Corey Schumacher
9.1 Introduction 237
9.2 Preliminaries 239
9.2.1 Graph Theory Notions 239
9.2.2 Problem Statement 240
9.3 Fixed Interaction Graph 241
9.3.1 Equal Possibility 242
9.3.2 Unequal Possibility 249
9.4 Switching Interaction Graph 253
9.5 Conclusion 262
References 262
10 Awareness Coverage Control in Unknown Environments Using Heterogeneous Multi-Robot Systems 265
Yue Wangand Li Wang
10.1 Introduction 265
10.2 Problem Formulation 268
10.2.1 Robot Models 268
10.2.2 Sensor Models 270
10.2.3 Communication Strategies 272
10.2.4 State of Awareness Dynamics 273
10.3 Cooperative Control of Heterogeneous Multi-Robot Systems 275
10.3.1 Motion Control for Boundary-Tracking UAVs 275
10.3.2 Awareness Coverage Control for Coverage Robots 275
10.3.2.1 Awareness Metric 275
10.3.2.2 Domain Coverage Algorithm 276
10.4 Simulation Results 284
10.5 Conclusion 287
References 287
Index 291
Über den Autor
Yue Wang, Ph D is the Warren H. Owen – Duke Energy Assistant Professor of Engineering in the Department of Mechanical Engineering, Clemson University, USA. Her research interests include cooperative control and decision-making for multi-agent systems and human-robot interaction.
Eloy Garcia is a scientist with Info Sci Tex Corp and the Control Science Center of Excellence, USAF Research Laboratory, USA.
David Casbeer is a research engineer with the Aerospace Systems Directorate, USAF Research Laboratory, USA.
Fumin Zhang, Ph D is Associate Professor at the School of Electrical and Computer Engineering, Georgia Institute of Technology, USA.
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