Examines new cooperative control methodologies tailored to real–world applications in various domains such as in communication systems, physics systems, and multi–robotic systems Provides the fundamental mechanism for solving collective behaviors in naturally–occurring systems as well as cooperative behaviors in man–made systems Discusses cooperative control methodologies using real–world applications, including semi–conductor laser arrays, mobile sensor networks, and multi–robotic systems Includes results from the research group at the Stevens Institute of Technology to show how advanced control technologies can impact challenging issues, such as high energy systems and oil spill monitoring INDICE: 1 Introduction 1 .1.1 Motivation and Challenges 1 .1.1.1 From Collective Behaviors to Cooperative Control 1 .1.1.2 Challenges 2 .1.2 Background and Related Work 3 .1.2.1 Networked Communication Systems 4 .1.2.2 Cooperating Autonomous Mobile Robots 5 .1.2.3 Nanoscale Systems and Laser Synchronization 7 .1.3 Overview of the Book 9 .2 Distributed Consensus and Consensus Filters 19 .2.1 Introduction and Literature Review 19 .2.2 Preliminaries on Graph Theory 22 .2.3 Distributed Consensus 25 .2.3.1 The Continuous–Time Consensus Protocol 25 .2.3.2 The Discrete–Time Consensus Protocol 27 .2.4 Distributed Consensus Filter 29 .2.4.1 PI Average Consensus Filter: Continuous–time 29 .2.4.2 PI Average Consensus Filter: Discrete–time 30 .Part I Distributed Consensus for Networked Communication Systems 39 .3 Average Consensus for Quantized Communication 41 .3.1 Introduction 41 .3.2 Problem Formulation 43 .3.2.1 Average Consensus Protocol with Quantization 43 .3.2.2 Problem Statement 43 .3.3 Weighting Matrix Design for Average Consensus with Quantization 44 .3.3.1 State Transformation 44 .3.3.2 Design for Fixed Directed Graphs 46 .3.3.3 Design for Switching Directed Graphs 55 .3.4 Simulations and Performance Evaluation 56 .3.4.1 Fixed Directed Graphs 56 .3.4.2 Switching Directed Graphs 58 .3.4.3 Fixed Undirected Graphs 58 .3.4.4 Performance Comparison 61 .3.5 Conclusion 63 .4 Weighted Average Consensus for Cooperative Spectrum Sensing 67 .4.1 Introduction 67 .4.2 Problem Statement 70 .4.3 Cooperative Spectrum Sensing Using Weighted Average Consensus 73 .4.3.1 Weighted Average Consensus Algorithm 74 .4.3.2 Fusion Convergence Performance in terms of Detection Probability 75 .4.3.3 Optimal Weight Design under AWGN measurement Channels 76 .4.3.4 Heuristic Weight Design under Rayleigh Fading Channels 78 .4.4 Convergence Analysis 79 .4.4.1 Fixed Communication Channels 79 .4.4.2 Dynamic Communication Channels 82 .4.4.3 Convergence Rate with Random Link Failures 85 .4.5 Simulations and Performance Evaluation 89 .4.5.1 Secondary User Network Setup 90 .4.5.2 Convergence of Weighted Average Consensus 90 .4.5.3 Metrics and Methodologies 92 .4.5.4 Performance Evaluation 92 .4.6 Conclusion 96 .5 Distributed Consensus Filter for Radio Environment Mapping 103 .5.1 Introduction 103 .5.2 Problem Formulation 105 .5.2.1 System Configuration and Distributed Sensor Placement 105 .5.2.2 The Model and Problem Statement 106 .5.3 Distributed Radio Environment Map Tracking 108 .5.3.1 System Matrix Estimation 108 .5.3.2 Kalman–EM Filter 110 .5.3.3 PI Consensus Filter for Distributed Estimation and Tracking 111 .5.4 Communication and Computation Complexity 112 .5.4.1 Communication Complexity 114 .5.4.2 Computation Complexity 114 .5.5 Simulations and Performance Evaluation 115 .5.5.1 Dynamic Radio Transmitter 115 .5.5.2 Stationary Radio Transmitter 118 .5.5.3 Comparison with Existing Centralized Methods 119 .5.6 Conclusion 120 .Part II Distributed Cooperative Control for Multi–robotic Systems 123 .6 Distributed Source Seeking by Cooperative Robots 125 .6.1 Introduction 125 .6.2 Problem Formulation 126 .6.3 Source Seeking with All–to–All Communications 127 .6.3.1 Cooperative Estimation of Gradients 127 .6.3.2 Control Law Design 128 .6.4 Distributed Source Seeking with Limited Communications 133 .6.5 Simulations 135 .6.6 Experimental Validation 138 .6.6.1 The Robot 138 .6.6.2 The Experiment Setup 139 .6.6.3 Experimental Results 140 .6.7 Conclusion 143 .7 Distributed Plume Front Tracking by Cooperative Robots 147 .7.1 Introduction 147 .7.2 Problem Statement 149 .7.3 Plume Front Estimation and Tracking by Single Robot 151 .7.3.1 State Equation of the Plume Front Dynamics 151 .7.3.2 Measurement Equation and Observer Design 153 .7.3.3 Estimation–based Tracking Control 154 .7.3.4 Convergence Analysis 155 .7.4 Multi–robot Cooperative Tracking of Plume Front 157 .7.4.1 Boundary Robots 157 .7.4.2 Follower Robots 158 .7.4.3 Convergence Analysis 160 .7.5 Simulations 161 .7.5.1 Simulation Environment 161 .7.5.2 Single Robot Plume Front Tracking 161 .7.5.3 Multi–robot Cooperative Plume Front Tracking 163 .7.6 Conclusion 165 .Part III Distributed Cooperative Control for Multi–agent Physics Systems 169 .8 Friction Control of Nano–Particle Array 171 .8.1 Introduction 171 .8.2 The Frenkel–Kontorova Model 172 .8.3 Open–Loop Stability Analysis 174 .8.3.1 Linear Particle Interactions 174 .8.3.2 Nonlinear Particle Interactions 178 .8.4 Control Problem Formulation 180 .8.5 Tracking Control Design 180 .8.5.1 Tracking Control of the Average System 181 .8.5.2 Stability of Single Particles in the Closed–Loop System 183 .8.6 Simulation Results 188 .8.7 Conclusion 193 .9 Synchronizing Coupled Semiconductor Lasers 199 .9.1 Introduction 199 .9.2 The Model of Coupled Semiconductor Lasers 200 .9.3 Stability Properties of Decoupled Semiconductor Laser 202 .9.4 Synchronization of Coupled Semiconductor Lasers 204 .9.5 Simulation Examples 208 .9.6 Conclusion 209 .A Notation and Symbols 217 .B Kronecker Product and Properties 219 .C Quantization Schemes 221 .D Finite L2 Gain 223 .E Radio Signal Propagation Model 225 .Appendices
- ISBN: 978-1-119-21609-4
- Editorial: Wiley–Blackwell
- Encuadernacion: Cartoné
- Páginas: 240
- Fecha Publicación: 02/05/2017
- Nº Volúmenes: 1
- Idioma: Inglés