A comprehensive guide to formation control of multi-agent systems using rigid graph theory
This book is the first to provide a comprehensive and unified treatment of the subject of graph rigidity-based formation control of multi-agent systems. Such systems are relevant to a variety of emerging engineering applications, including unmanned robotic vehicles and mobile sensor networks. Graph theory, and rigid graphs in particular, provides a natural tool for describing the multi-agent formation shape as well as the inter-agent sensing, communication, and control topology.
Beginning with an introduction to rigid graph theory, the contents of the book are organized by the agent dynamic model (single integrator, double integrator, and mechanical dynamics) and by the type of formation problem (formation acquisition, formation manoeuvring, and target interception). The book presents the material in ascending level of difficulty and in a self-contained manner; thus, facilitating reader understanding.
Key features:
Uses the concept of graph rigidity as the basis for describing the multi-agent formation geometry and solving formation control problems. Considers different agent models and formation control problems. Control designs throughout the book progressively build upon each other. Provides a primer on rigid graph theory. Combines theory, computer simulations, and experimental results.
Formation Control of Multi-Agent Systems: A Graph Rigidity Approach is targeted at researchers and graduate students in the areas of control systems and robotics. Prerequisite knowledge includes linear algebra, matrix theory, control systems, and nonlinear systems.
| Preface |
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xi | |
| About the Companion Website |
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xiii | |
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1 | (28) |
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1 | (5) |
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6 | (1) |
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7 | (16) |
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7 | (2) |
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9 | (2) |
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11 | (3) |
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1.3.4 Infinitesimal Rigidity |
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14 | (5) |
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19 | (1) |
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1.3.6 Framework Ambiguities |
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20 | (2) |
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22 | (1) |
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1.4 Formation Control Problems |
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23 | (3) |
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1.5 Book Overview and Organization |
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26 | (2) |
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28 | (1) |
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2 Single-Integrator Model |
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29 | (42) |
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2.1 Formation Acquisition |
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29 | (6) |
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2.2 Formation Maneuvering |
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35 | (1) |
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36 | (4) |
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2.3.1 Constant Flocking Velocity |
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37 | (1) |
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2.3.2 Time-Varying Flocking Velocity |
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38 | (2) |
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2.4 Target Interception with Unknown Target Velocity |
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40 | (3) |
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2.5 Dynamic Formation Acquisition |
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43 | (2) |
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45 | (21) |
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2.6.1 Formation Acquisition |
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45 | (6) |
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2.6.2 Formation Maneuvering |
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51 | (5) |
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56 | (2) |
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2.6.4 Target Interception |
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58 | (5) |
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63 | (3) |
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66 | (5) |
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3 Double-Integrator Model |
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71 | (20) |
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73 | (2) |
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3.2 Formation Acquisition |
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75 | (1) |
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3.3 Formation Maneuvering |
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76 | (1) |
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3.4 Target Interception with Unknown Target Acceleration |
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77 | (2) |
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3.5 Dynamic Formation Acquisition |
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79 | (1) |
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80 | (7) |
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3.6.1 Formation Acquisition |
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80 | (1) |
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3.6.2 Dynamic Formation Acquisition with Maneuvering |
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81 | (3) |
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3.6.3 Target Interception |
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84 | (3) |
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87 | (4) |
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91 | (16) |
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91 | (2) |
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4.2 Nonholonomic Kinematics |
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93 | (4) |
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93 | (1) |
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94 | (3) |
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97 | (605) |
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4.3.1 Model-Based Control |
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98 | (2) |
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100 | (2) |
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102 | (600) |
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702 | (5) |
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707 | (52) |
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5.1 Experimental Platform |
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107 | (3) |
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5.2 Vehicle Equations of Motion |
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110 | (3) |
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5.3 Low-Level Control Design |
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113 | (1) |
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114 | (51) |
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5.4.1 Single Integrator: Formation Acquisition |
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117 | (1) |
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5.4.2 Single Integrator: Formation Maneuvering |
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118 | (8) |
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5.4.3 Single Integrator: Target Interception |
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126 | (2) |
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5.4.4 Single Integrator: Dynamic Formation |
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128 | (4) |
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5.4.5 Double Integrator: Formation Acquisition |
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132 | (4) |
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5.4.6 Double Integrator: Formation Maneuvering |
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136 | (2) |
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5.4.7 Double Integrator: Target Interception |
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138 | (10) |
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5.4.8 Double Integrator: Dynamic Formation |
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148 | (1) |
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5.4.9 Holonomic Dynamics: Formation Acquisition |
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149 | (4) |
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153 | (12) |
| A Matrix Theory and Linear Algebra |
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159 | (4) |
| B Functions and Signals |
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163 | (2) |
| C Systems Theory |
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165 | (10) |
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165 | (1) |
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166 | (2) |
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168 | (2) |
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C.4 Input-to-State Stability |
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170 | (1) |
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171 | (1) |
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C.6 Integrator Backstepping |
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172 | (3) |
| D Dynamic Model Terms |
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175 | (2) |
| References |
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177 | (10) |
| Index |
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187 | |
MARCIO DE QUEIROZ joined the Department of Mechanical and Industrial Engineering at Louisiana State University in 2000, where he is currently the Roy O. Martin Lumber Company Professor. In 2005, he was the recipient of the NSF CAREER award. He has served as an Associate Editor for the IEEE Transactions on Automatic Control, the IEEE/ASME Transactions on Mechatronics, the ASME Journal of Dynamic Systems, Measurement, and Control, and the IEEE Transactions on Systems, Man, and Cybernetics Part B. His research interests include nonlinear control, multi-agent systems, robotics, active magnetic and mechanical bearings, and biological/biomedical system modelling and control.
XIAOYU CAI joined the job search group in LinkedIn in 2018, where he is currently a software engineer. He received the 2013 Outstanding Research Assistant Award from the Department of Mechanical and Industrial Engineering at LSU for his doctoral research on formation control of multi-agent systems. His research interests include computer vision, reinforcement learning, nonlinear control theory and applications, multi-agent systems, robotics, process control, control of high-precision servo systems.
MATTHEW FEEMSTER joined the Weapons, Robotics, and Controls Engineering Department of the U.S. Naval Academy in Annapolis, MD, in 2002 and where he is currently an Associate Professor. His research interests are in the utilization of nonlinear control theory to promote mission capabilities in such fielded applications as autonomous air, ground, and marine vehicles.
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