Distributed Cooperative Control

Yi Guo, PhD, is an Associate Professor of Electrical and Computer Engineering at the Stevens Institute of Technology. She has more than 15 years of research experience in controls and robotics, and has taught robotics and controls courses for the past 10 years at the Stevens Institute of Technology. Dr. Guo has authored/coauthored over 100 peer-reviewed journals and conference papers. She is currently the Associate Editor of the IEEE Robotics and Automation Magazine. Dr. Guo frequently presents at international conferences, and gives invited talks for students and other professionals.

• Preface xiiAbout the Companion Website xiv1 Introduction 11.1 Motivation and Challenges 11.1.1 From Collective Behaviors to Cooperative Control 11.1.2 Challenges 21.2 Background and Related Work 41.2.1 Networked Communication Systems 41.2.2 Cooperating Autonomous Mobile Robots 51.2.3 Nanoscale Systems and Laser Synchronization 71.3 Overview of the Book 9References 122 Distributed Consensus and Consensus Filters 192.1 Introduction and Literature Review 192.2 Preliminaries on Graph Theory 222.3 Distributed Consensus 262.3.1 The Continuous-Time Consensus Protocol 262.3.2 The Discrete-Time Consensus Protocol 282.4 Distributed Consensus Filter 292.4.1 PI Average Consensus Filter: Continuous-Time 302.4.2 PI Average Consensus Filter: Discrete-Time 30References 31Part I Distributed Consensus for Networked Communication Systems 373 Average Consensus for Quantized Communication 393.1 Introduction 393.2 Problem Formulation 413.2.1 Average Consensus Protocol with Quantization 413.2.2 Problem Statement 423.3 Weighting Matrix Design for Average Consensus with Quantization 423.3.1 State Transformation 433.3.2 Design for Fixed and Directed Graphs 443.3.3 Design for Switching and Directed Graphs 523.4 Simulations and Performance Evaluation 543.4.1 Fixed and Directed Graphs 543.4.2 Switching and Directed Graphs 553.4.3 Fixed and Directed Graphs 563.4.4 Performance Comparison 573.5 Conclusion 61Notes 61References 624 Weighted Average Consensus for Cooperative Spectrum Sensing 644.1 Introduction 644.2 Problem Statement 674.3 Cooperative Spectrum Sensing Using Weighted Average Consensus 714.3.1 Weighted Average Consensus Algorithm 714.3.2 Fusion Convergence Performance in Terms of Detection Probability 724.3.3 Optimal Weight Design under AWGN Measurement Channels 734.3.4 Heuristic Weight Design under Rayleigh Fading Channels 754.4 Convergence Analysis 764.4.1 Fixed Communication Channels 764.4.2 Dynamic Communication Channels 794.4.3 Convergence Rate with Random Link Failures 834.5 Simulations and Performance Evaluation 874.5.1 SU Network Setup 874.5.2 Convergence of Weighted Average Consensus 884.5.3 Metrics and Methodologies 904.5.4 Performance Evaluation 914.6 Conclusion 97Notes 97References 975 Distributed Consensus Filter for Radio Environment Mapping 1015.1 Introduction 1015.2 Problem Formulation 1035.2.1 System Configuration and Distributed Sensor Placement 1035.2.2 The Model and Problem Statement 1055.3 Distributed REM Tracking 1065.3.1 System Matrix Estimation 1075.3.2 Kalman–EM Filter 1085.3.3 PI Consensus Filter for Distributed Estimation and Tracking 1095.4 Communication and Computation Complexity 1105.4.1 Communication Complexity 1125.4.2 Computation Complexity 1125.5 Simulations and Performance Evaluation 1135.5.1 Dynamic Radio Transmitter 1135.5.2 Stationary Radio Transmitter 1165.5.3 Comparison with Existing Centralized Methods 1165.6 Conclusion 118Notes 119References 119Part II Distributed Cooperative Control for Multirobotic Systems 1236 Distributed Source Seeking by Cooperative Robots 1256.1 Introduction 1256.2 Problem Formulation 1266.3 Source Seeking with All-to-All Communications 1276.3.1 Cooperative Estimation of Gradients 1276.3.2 Control Law Design 1286.4 Distributed Source Seeking with Limited Communications 1336.5 Simulations 1356.6 Experimental Validation 1386.6.1 The Robot 1386.6.2 The Experiment Setup 1406.6.3 Experimental Results 1416.7 Conclusion 144Notes 144References 1447 Distributed Plume Front Tracking by Cooperative Robots 1467.1 Introduction 1467.2 Problem Statement 1487.3 Plume Front Estimation and Tracking by Single Robot 1507.3.1 State Equation of the Plume Front Dynamics 1517.3.2 Measurement Equation and Observer Design 1527.3.3 Estimation-Based Tracking Control 1537.3.4 Convergence Analysis 1557.4 Multirobot Cooperative Tracking of Plume Front 1567.4.1 Boundary Robots 1577.4.2 Follower Robots 1577.4.3 Convergence Analysis 1587.5 Simulations 1607.5.1 Simulation Environment 1607.5.2 Single-Robot Plume Front Tracking 1617.5.3 Multirobot Cooperative Plume Front Tracking 1617.6 Conclusion 164Notes 165References 165Part III Distributed Cooperative Control for Multiagent Physics Systems 1678 Friction Control of Nano-particle Array 1698.1 Introduction 1698.2 The Frenkel–Kontorova Model 1708.3 Open-Loop Stability Analysis 1728.3.1 Linear Particle Interactions 1728.3.2 Nonlinear Particle Interactions 1768.4 Control Problem Formulation 1778.5 Tracking Control Design 1788.5.1 Tracking Control of the Average System 1788.5.2 Stability of Single Particles in the Closed-Loop System 1818.6 Simulation Results 1868.7 Conclusion 191Notes 194References 1959 Synchronizing Coupled Semiconductor Lasers 1979.1 Introduction 1979.2 The Model of Coupled Semiconductor Lasers 1989.3 Stability Properties of Decoupled Semiconductor Laser 2009.4 Synchronization of Coupled Semiconductor Lasers 2039.5 Simulation Examples 2079.6 Conclusion 209Notes 209References 210Appendix A Notation and Symbols 212Appendix B Kronecker Product and Properties 213Appendix C Quantization Schemes 214Appendix D Finite L2 Gain 215Appendix E Radio Signal Propagation Model 216Index 218