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E-raamat: Distributed Cooperative Control: Emerging Applications

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  • Ilmumisaeg: 03-Mar-2017
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119216100
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Distributed Cooperative Control: Emerging ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 03-Mar-2017
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119216100
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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
Preface xii
About the Companion Website xiv
1 Introduction
1(18)
1.1 Motivation and Challenges
1(3)
1.1.1 From Collective Behaviors to Cooperative Control
1(1)
1.1.2 Challenges
2(2)
1.2 Background and Related Work
4(5)
1.2.1 Networked Communication Systems
4(1)
1.2.2 Cooperating Autonomous Mobile Robots
5(2)
1.2.3 Nanoscale Systems and Laser Synchronization
7(2)
1.3 Overview of the Book
9(10)
References
12(7)
2 Distributed Consensus and Consensus Filters
19(18)
2.1 Introduction and Literature Review
19(3)
2.2 Preliminaries on Graph Theory
22(4)
2.3 Distributed Consensus
26(3)
2.3.1 The Continuous-Time Consensus Protocol
26(2)
2.3.2 The Discrete-Time Consensus Protocol
28(1)
2.4 Distributed Consensus Filter
29(8)
2.4.1 PI Average Consensus Filter: Continuous-Time
30(1)
2.4.2 PI Average Consensus Filter: Discrete-Time
30(1)
References
31(6)
Part I Distributed Consensus for Networked Communication Systems
37(86)
3 Average Consensus for Quantized Communication
39(25)
3.1 Introduction
39(2)
3.2 Problem Formulation
41(1)
3.2.1 Average Consensus Protocol with Quantization
41(1)
3.2.2 Problem Statement
42(1)
3.3 Weighting Matrix Design for Average Consensus with Quantization
42(12)
3.3.1 State Transformation
43(1)
3.3.2 Design for Fixed and Directed Graphs
44(8)
3.3.3 Design for Switching and Directed Graphs
52(2)
3.4 Simulations and Performance Evaluation
54(7)
3.4.1 Fixed and Directed Graphs
54(1)
3.4.2 Switching and Directed Graphs
55(1)
3.4.3 Fixed and Directed Graphs
56(1)
3.4.4 Performance Comparison
57(4)
3.5 Conclusion
61(3)
Notes
61(1)
References
62(2)
4 Weighted Average Consensus for Cooperative Spectrum Sensing
64(37)
4.1 Introduction
64(3)
4.2 Problem Statement
67(4)
4.3 Cooperative Spectrum Sensing Using Weighted Average Consensus
71(5)
4.3.1 Weighted Average Consensus Algorithm
71(1)
4.3.2 Fusion Convergence Performance in Terms of Detection Probability
72(1)
4.3.3 Optimal Weight Design under AWGN Measurement Channels
73(2)
4.3.4 Heuristic Weight Design under Rayleigh Fading Channels
75(1)
4.4 Convergence Analysis
76(11)
4.4.1 Fixed Communication Channels
76(3)
4.4.2 Dynamic Communication Channels
79(4)
4.4.3 Convergence Rate with Random Link Failures
83(4)
4.5 Simulations and Performance Evaluation
87(10)
4.5.1 SU Network Setup
87(1)
4.5.2 Convergence of Weighted Average Consensus
88(2)
4.5.3 Metrics and Methodologies
90(1)
4.5.4 Performance Evaluation
91(6)
4.6 Conclusion
97(4)
Notes
97(1)
References
97(4)
5 Distributed Consensus Filter for Radio Environment Mapping
101(22)
5.1 Introduction
101(2)
5.2 Problem Formulation
103(3)
5.2.1 System Configuration and Distributed Sensor Placement
103(2)
5.2.2 The Model and Problem Statement
105(1)
5.3 Distributed REM Tracking
106(4)
5.3.1 System Matrix Estimation
107(1)
5.3.2 Kalman--EM Filter
108(1)
5.3.3 PI Consensus Filter for Distributed Estimation and Tracking
109(1)
5.4 Communication and Computation Complexity
110(3)
5.4.1 Communication Complexity
112(1)
5.4.2 Computation Complexity
112(1)
5.5 Simulations and Performance Evaluation
113(5)
5.5.1 Dynamic Radio Transmitter
113(3)
5.5.2 Stationary Radio Transmitter
116(1)
5.5.3 Comparison with Existing Centralized Methods
116(2)
5.6 Conclusion
118(5)
Notes
119(1)
References
119(4)
Part II Distributed Cooperative Control for Multirobotic Systems
123(44)
6 Distributed Source Seeking by Cooperative Robots
125(21)
6.1 Introduction
125(1)
6.2 Problem Formulation
126(1)
6.3 Source Seeking with All-to-All Communications
127(6)
6.3.1 Cooperative Estimation of Gradients
127(1)
6.3.2 Control Law Design
128(5)
6.4 Distributed Source Seeking with Limited Communications
133(2)
6.5 Simulations
135(3)
6.6 Experimental Validation
138(6)
6.6.1 The Robot
138(2)
6.6.2 The Experiment Setup
140(1)
6.6.3 Experimental Results
141(3)
6.7 Conclusion
144(2)
Notes
144(1)
References
144(2)
7 Distributed Plume Front Tracking by Cooperative Robots
146(21)
7.1 Introduction
146(2)
7.2 Problem Statement
148(2)
7.3 Plume Front Estimation and Tracking by Single Robot
150(6)
7.3.1 State Equation of the Plume Front Dynamics
151(1)
7.3.2 Measurement Equation and Observer Design
152(1)
7.3.3 Estimation-Based Tracking Control
153(2)
7.3.4 Convergence Analysis
155(1)
7.4 Multirobot Cooperative Tracking of Plume Front
156(4)
7.4.1 Boundary Robots
157(1)
7.4.2 Follower Robots
157(1)
7.4.3 Convergence Analysis
158(2)
7.5 Simulations
160(4)
7.5.1 Simulation Environment
160(1)
7.5.2 Single-Robot Plume Front Tracking
161(1)
7.5.3 Multirobot Cooperative Plume Front Tracking
161(3)
7.6 Conclusion
164(3)
Notes
165(1)
References
165(2)
Part III Distributed Cooperative Control for Multiagent Physics Systems
167(45)
8 Friction Control of Nano-particle Array
169(28)
8.1 Introduction
169(1)
8.2 The Frenkel--Kontorova Model
170(2)
8.3 Open-Loop Stability Analysis
172(5)
8.3.1 Linear Particle Interactions
172(4)
8.3.2 Nonlinear Particle Interactions
176(1)
8.4 Control Problem Formulation
177(1)
8.5 Tracking Control Design
178(8)
8.5.1 Tracking Control of the Average System
178(3)
8.5.2 Stability of Single Particles in the Closed-Loop System
181(5)
8.6 Simulation Results
186(5)
8.7 Conclusion
191
Notes
194(1)
References
195(2)
9 Synchronizing Coupled Semiconductor Lasers
197(15)
9.1 Introduction
197(1)
9.2 The Model of Coupled Semiconductor Lasers
198(2)
9.3 Stability Properties of Decoupled Semiconductor Laser
200(3)
9.4 Synchronization of Coupled Semiconductor Lasers
203(4)
9.5 Simulation Examples
207(2)
9.6 Conclusion
209(3)
Notes
209(1)
References
210(2)
Appendix A Notation and Symbols 212(1)
Appendix B Kronecker Product and Properties 213(1)
Appendix C Quantization Schemes 214(1)
Appendix D Finite L2 Gain 215(1)
Appendix E Radio Signal Propagation Model 216(2)
Index 218
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.
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