Cooperative Control of Multi-Agent Systems Theory and Applications

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.

Yue Wang, PhD 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 InfoSciTex 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, PhD is Associate Professor at the School of Electrical and Computer Engineering, Georgia Institute of Technology, USA.

List of Contributors xiii

Preface xvii

Acknowledgment xix

1 Introduction 1
YueWang, Eloy Garcia, David Casbeer and Fumin Zhang

1.1 Introduction 1

1.2 Chapter Summary and Contributions 11

2 Sensor Placement Algorithms for a Path Covering Problem 31
Sivakumar Rathinam and Rajnikant Sharma

2.1 Problem Statement 34

2.2 Algorithm Approx1 35

2.3 Algorithm Approx2 42

2.4 Numerical Results 46

2.5 Conclusions 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

3.3 Robust Output-Feedback MPC/MHE 64

3.4 Simulation Results 67

3.5 Conclusion and FutureWork 79

4 Projection-Based Consensus for Time-Critical Coordination of Unmanned Aerial Vehicles under Velocity Constraints 85
XiaofengWang, Eloy Garcia, Zheqing Zhou, Derek Kingston and David Casbeer

4.1 Introduction 85

4.2 Problem Statement 87

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

5 GreedyMaximization for Asset-Based Weapon–Target Assignment with Time-Dependent Rewards 115
Doo-Hyun Cho and Han-Lim Choi

5.1 Introduction 115

5.2 Problem Formulation 117

5.3 Properties of the Objective Function 120

5.4 Algorithmic Details 126

5.5 Numerical Case Studies 128

6 Coordinated Threat Assignments and Mission Management of Unmanned Aerial Vehicles 141
Eloy Garcia and David Casbeer

6.1 Introduction 141

6.2 Problem Statement 144

6.3 Decentralized Assignment of Threats 148

6.4 Assignment Constraints 153

6.5 Multiple Main Targets 163

6.6 Conclusions 172

7 Event-Triggered Communication and Control for Multi-Agent Average Consensus 177
Cameron Nowzari, Jorge Cortes and George J. Pappas

7.1 Introduction 177

7.2 Preliminaries 181

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

7.7 Periodic Event-Triggered Coordination 199

7.8 Conclusions and Future Outlook 201

8 Topology Design and Identification for Dynamic Networks 209
Chuangchuang Sun and Ran Dai

8.1 Introduction 209

8.2 Network Topology Design Problems 212

8.3 Network Topology Identification Problems 216

8.4 Iterative Rank Minimization Approach 221

8.5 Simulation Examples 224

8.6 Conclusions 231

9 Distributed Multi-Agent Coordination with Uncertain Interactions: A Probabilistic Perspective 237
Yongcan Cao, David Casbeer, Eloy Garcia and Corey Schumacher

9.1 Introduction 237

9.2 Preliminaries 239

9.3 Fixed Interaction Graph 241

9.4 Switching Interaction Graph 253

10 Awareness Coverage Control in Unknown Environments Using Heterogeneous Multi-Robot Systems 265
YueWang and LiWang

10.1 Introduction 265

10.2 Problem Formulation 268

10.3 Cooperative Control of Heterogeneous Multi-Robot Systems 275

10.4 Simulation Results 284

Index 291

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