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Cooperative Control of Multi-Agent Systems: Optimal and Adaptive Design Approaches 2014 ed. [Hardback]

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  • Formāts: Hardback, 307 pages, height x width: 235x155 mm, weight: 6746 g, 59 Illustrations, color; 21 Illustrations, black and white; XX, 307 p. 80 illus., 59 illus. in color., 1 Hardback
  • Sērija : Communications and Control Engineering
  • Izdošanas datums: 21-Jan-2014
  • Izdevniecība: Springer London Ltd
  • ISBN-10: 1447155734
  • ISBN-13: 9781447155737
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Cooperative Control of Multi-Agent Systems: Optimal and Adaptive Design Approaches 2014 ed.Palielināt
  • Formāts: Hardback, 307 pages, height x width: 235x155 mm, weight: 6746 g, 59 Illustrations, color; 21 Illustrations, black and white; XX, 307 p. 80 illus., 59 illus. in color., 1 Hardback
  • Sērija : Communications and Control Engineering
  • Izdošanas datums: 21-Jan-2014
  • Izdevniecība: Springer London Ltd
  • ISBN-10: 1447155734
  • ISBN-13: 9781447155737
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781447155737.html
Keywords:

Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control design methods to multi-agent systems on communication graphs. It develops Riccati design techniques for general linear dynamics for cooperative state feedback design, cooperative observer design, and cooperative dynamic output feedback design. Both continuous-time and discrete-time dynamical multi-agent systems are treated. Optimal cooperative control is introduced and neural adaptive design techniques for multi-agent nonlinear systems with unknown dynamics, which are rarely treated in literature are developed. Results spanning systems with first-, second- and on up to general high-order nonlinear dynamics are presented.

Each control methodology proposed is developed by rigorous proofs. All algorithms are justified by simulation examples. The text is self-contained and will serve as an excellent comprehensive source of information for researchers and graduate students working with multi-agent systems.



Offering readers a wealth of cutting-edge, Riccati-based design techniques for various forms of control, this self-contained text stress-tests the reliability of the methods outlined with rigorous stability analyses and detailed control design algorithms.

1 Introduction to Synchronization in Nature and Physics and Cooperative Control for Multi-Agent Systems on Graphs
1(22)
1.1 Synchronization in Nature and Social Systems
1(9)
1.1.1 Animal Motion in Collective Groups
3(1)
1.1.2 Communication Graphs and Implementing Reynolds' Rules
4(4)
1.1.3 Leadership in Animal Groups on the Move
8(2)
1.2 Networks of Coupled Dynamical Systems in Nature and Science
10(9)
1.2.1 Collective Motion in Biological and Social Systems, Physics and Chemistry, and Engineered Systems
10(2)
1.2.2 Graph Topologies and Structured Information Flow in Collective Groups
12(7)
1.3 Cooperative Control of Multi-Agent Systems on Communication Graphs
19(4)
References
19(4)
2 Algebraic Graph Theory and Cooperative Control Consensus
23(52)
2.1 Algebraic Graph Theory
24(10)
2.1.1 Graph Theory Basics
25(1)
2.1.2 Graph Matrices
26(2)
2.1.3 Eigenstructure of Graph Laplacian Matrix
28(5)
2.1.4 The Fiedler Eigenvalue
33(1)
2.2 Systems on Communication Graphs and Consensus
34(1)
2.3 Consensus with Single-Integrator Dynamics
35(10)
2.3.1 Distributed Control Protocols for Consensus
35(6)
2.3.2 Consensus Value for Balanced Graphs---Average Consensus
41(2)
2.3.3 Consensus Leaders
43(1)
2.3.4 Non-Scalar Node States
44(1)
2.4 Motion Invariants for First-Order Consensus
45(2)
2.4.1 Graph Motion Invariant and `Internal Forces'
45(1)
2.4.2 Center of Gravity Dynamics and Shape Dynamics
46(1)
2.5 Consensus with First-Order Discrete-Time Dynamics
47(6)
2.5.1 Perron Discrete-Time Systems
47(2)
2.5.2 Normalized Protocol Discrete-Time Systems
49(2)
2.5.3 Average Consensus Using Two Parallel Protocols at Each Node
51(2)
2.6 Higher-Order Consensus: Linear Systems on Graphs
53(2)
2.7 Second-Order Consensus T
55(7)
2.7.1 Analysis of Second-Order Consensus Using Position/Velocity Local Node States
55(4)
2.7.2 Analysis of Second-Order Consensus Using Position/Velocity Global State
59(1)
2.7.3 Formation Control Second-Order Protocol
60(2)
2.8 Matrix Analysis of Graphs
62(5)
2.8.1 Irreducible Matrices and Frobenius Form
63(2)
2.8.2 Stochastic Matrices
65(1)
2.8.3 M-Matrices
66(1)
2.9 Lyapunov Functions for Cooperative Control on Graphs
67(3)
2.10 Conclusions and Setting for the Subsequent
Chapters
70(5)
References
70(5)
Part I Local Optimal Design for Cooperative Control in Multi-Agent Systems on Graphs
3 Riccati Design for Synchronization of Continuous-Time Systems
75(32)
3.1 Duality, Stability, and Optimality for Cooperative Control
75(1)
3.2 State Feedback Design of Cooperative Control Protocols
76(8)
3.2.1 Synchronization of Multi-Agent Systems on Graphs
77(2)
3.2.2 Cooperative SVFB Control
79(4)
3.2.3 Local Riccati Design of Synchronizing Protocols
83(1)
3.3 Region of Synchronization
84(2)
3.4 Cooperative Observer Design
86(3)
3.5 Duality for Cooperative Systems on Graphs
89(2)
3.6 Cooperative Dynamic Regulators for Synchronization
91(5)
3.6.1 Neighborhood Controller and Neighborhood Observer
91(2)
3.6.2 Neighborhood Controller and Local Observer
93(2)
3.6.3 Local Controller and Neighborhood Observer
95(1)
3.7 Simulation Examples
96(11)
References
104(3)
4 Riccati Design for Synchronization of Discrete-Time Systems
107(34)
4.1 Graph Properties
108(1)
4.2 State Feedback Design of Discrete-Time Cooperative Controls
108(4)
4.2.1 Synchronization of Discrete-Time Multi-Agent Systems on Graphs
109(3)
4.3 Synchronization Region
112(1)
4.4 Local Riccati Design of Synchronizing Protocols
112(4)
4.5 Robustness Property of Local Riccati Design
116(2)
4.6 Application to Real Graph Matrix Eigenvalues and Single-Input Systems
118(3)
4.6.1 Case of Real Graph Eigenvalues
118(1)
4.6.2 Case of Single-Input Agent Dynamics
119(1)
4.6.3 Mahler Measure, Graph Condition Number, and Graph Channel Capacity
120(1)
4.7 Cooperative Observer Design
121(7)
4.7.1 Distributed Neighborhood Observer Dynamics
122(2)
4.7.2 Convergence Region
124(1)
4.7.3 Local Riccati Cooperative Observer Design
124(3)
4.7.4 Duality on Graphs for Discrete-Time Cooperative Systems
127(1)
4.8 Cooperative Dynamic Regulators for Synchronization
128(4)
4.8.1 Neighborhood Controller and Neighborhood Observer
128(1)
4.8.2 Neighborhood Controller and Local Observer
129(1)
4.8.3 Local Controller and Neighborhood Observer
130(2)
4.9 Simulation Examples
132(4)
4.9.1 Example 4.1. Importance of Weighting in Control and Observer Protocols
132(2)
4.9.2 Example 4.2. Three Cooperative Dynamic Regulator Designs
134(2)
4.10 Conclusion
136(5)
References
140(1)
5 Cooperative Globally Optimal Control for Multi-Agent Systems on Directed Graph Topologies
141(40)
5.1 Stability, Local Optimality, and Global Optimality for Synchronization Control on Graphs
142(1)
5.2 Graph Definitions
143(1)
5.3 Partial Asymptotic Stability
144(3)
5.4 Inverse Optimal Control
147(6)
5.4.1 Optimality
148(1)
5.4.2 Inverse Optimality
149(4)
5.5 Optimal Cooperative Control for Quadratic Performance Index and Single-Integrator Agent Dynamics
153(5)
5.5.1 Optimal Cooperative Regulator
153(3)
5.5.2 Optimal Cooperative Tracker
156(2)
5.6 Optimal Cooperative Control for Quadratic Performance Index and Linear Time-Invariant Agent Dynamics
158(6)
5.6.1 Optimal Cooperative Regulator
158(3)
5.6.2 Optimal Cooperative Tracker
161(3)
5.7 Constraints on Graph Topology
164(3)
5.7.1 Undirected Graphs
165(1)
5.7.2 Detail Balanced Graphs
165(1)
5.7.3 Directed Graphs with Simple Laplacian
166(1)
5.8 Optimal Cooperative Control for General Digraphs: Performance Index with Cross-Weighting Terms
167(11)
5.8.1 Optimal Cooperative Regulator---Single-Integrator Agent Dynamics
168(2)
5.8.2 Optimal Cooperative Tracker---Single-Integrator Agent Dynamics
170(1)
5.8.3 Condition for Existence of Global Optimal Control with Cross-weighting Terms in the Performance Index
171(1)
5.8.4 General Linear Time-Invariant Systems---Cooperative Regulator
172(3)
5.8.5 General Linear Time-Invariant Systems---Cooperative Tracker
175(3)
5.9 Conclusion
178(3)
References
179(2)
6 Graphical Games: Distributed Multiplayer Games on Graphs
181(40)
6.1 Introduction: Games, RL, and PI
182(1)
6.2 Synchronization and Node Error Dynamics
183(3)
6.2.1 Graphs
183(1)
6.2.2 Synchronization and Node Error Dynamics
184(2)
6.3 Cooperative Multiplayer Games on Graphs
186(6)
6.3.1 Cooperative Performance Index
186(2)
6.3.2 Global and Local Performance Objectives: Cooperation and Competition
188(1)
6.3.3 Graphical Games
188(4)
6.4 Interactive Nash Equilibrium
192(3)
6.5 Stability and Solution of Graphical Games
195(5)
6.5.1 Coupled Riccati Equations
196(2)
6.5.2 Stability and Solution for Cooperative Nash Equilibrium
198(2)
6.6 PI Algorithms for Cooperative Multiplayer Games
200(11)
6.6.1 Best Response
200(1)
6.6.2 PI Solution for Graphical Games
201(4)
6.6.3 PI Solution for Graphical Games
205(1)
6.6.4 Critic NN
206(1)
6.6.5 Action NN and Online Learning
207(4)
6.7 Simulation Results
211(4)
6.7.1 Position and Velocity Regulated to Zero
213(1)
6.7.2 All the Nodes Synchronize to the Curve Behavior of the Leader Node
213(2)
6.8 Conclusion
215(6)
References
215(6)
Part II Distributed Adaptive Control for Multi-Agent Cooperative Systems
7 Graph Laplacian Potential and Lyapunov Functions for Multi-Agent Systems
221(14)
7.1 Graph Laplacian Potential
222(1)
7.2 Laplacian Potential for Undirected Graphs
223(5)
7.2.1 Laplacian Potential for Directed Graphs
225(3)
7.3 Lyapunov Analysis for Cooperative Regulator Problems
228(7)
7.3.1 Consensus of Single Integrator Cooperative Systems
229(2)
7.3.2 Synchronization of Passive Nonlinear Systems
231(3)
References
234(1)
8 Cooperative Adaptive Control for Systems with First-Order Nonlinear Dynamics
235(24)
8.1 Synchronization Control Formulation and Error Dynamics
236(5)
8.1.1 Graph Theory Basics
236(1)
8.1.2 Synchronization Control Problem
237(1)
8.1.3 Synchronization Error Dynamics
238(1)
8.1.4 Synchronization Control Design
239(2)
8.2 Adaptive Design and Distributed Tuning Law
241(8)
8.3 Relation of Error Bounds to Graph Structural Properties
249(1)
8.4 Simulation Examples
250(9)
References
256(3)
9 Cooperative Adaptive Control for Systems with Second-Order Nonlinear Dynamics
259(20)
9.1 Sliding Variable Cooperative Control Formulation and Error Dynamics
260(5)
9.1.1 Cooperative Tracking Problem for Synchronization of Multi-Agent Systems
260(2)
9.1.2 Sliding Mode Tracking Error
262(1)
9.1.3 Synchronization Control Design and Error Dynamics
263(2)
9.2 Cooperative Adaptive Design and Distributed Tuning Laws
265(8)
9.3 Simulation Example
273(6)
References
278(1)
10 Cooperative Adaptive Control for Higher-Order Nonlinear Systems
279(26)
10.1 Sliding Variable Control Formulation and Error Dynamics
280(5)
10.1.1 Synchronization for Nonlinear Higher-Order Cooperative Systems
280(3)
10.1.2 Local Neighborhood Error Dynamics
283(1)
10.1.3 Communication Graph Structure and the Graph Lyapunov Equation
284(1)
10.2 Distributed Control Structure
285(6)
10.2.1 Sliding Mode Error Variables and Performance Lyapunov Equation
285(2)
10.2.2 Local NN Approximators for Unknown Nonlinearities
287(3)
10.2.3 Distributed Control Law Structure
290(1)
10.3 Distributed Tuning Laws for Cooperative Adaptive Control
291(6)
10.4 Simulation Example
297(8)
References
303(2)
Index 305
Frank L. Lewis (S78-M81-SM86-F94), Fellow IEEE, Fellow IFAC, Fellow UK Institute of Measurement and Control, Professional Engineer Texas, UK Chartered Engineer, is Distinguished Scholar Professor and Moncrief-ODonnell Chair at University of Texas at Arlingtons Automation & Robotics Research Institute. He obtained his PhD at Georgia Tech. He received the Fulbright Research Award, NSF Research Initiation Grant, ASEE Terman Award, Int. Neural Network Soc. Gabor Award 2009, UK Inst Measurement & Control Honeywell Field Engineering Medal 2009. Received Outstanding Service Award from Dallas IEEE Section, selected as Engineer of the year by Ft. Worth IEEE Section. Received the 2010 IEEE Region 5 Outstanding Engineering Educator Award and the 2010 UTA Graduate Deans Excellence in Doctoral Mentoring Award. He served on the NAE Committee on Space Station in 1995. He is an elected Guest Consulting Professor at South China University of Tech. and Shanghai Jiao Tong University. Founder Member of the Board of Governors of the Mediterranean Control Assoc. Helped win the IEEE CSS Best Chapter Award (as Founding Chairman of DFW Chapter), the National Sigma Xi Award for Outstanding Chapter (as President of UTA Chapter), and the US SBA Tibbets Award in 1996 (as Director of ARRIs SBIR Program). He is author of 6 US patents, 222 journal papers, 47 chapters and encyclopedia articles, 333 refereed conference papers, and 14 books. His current research interests include distributed control on graphs, neural and fuzzy systems, intelligent control, wireless sensor networks, nonlinear systems, robotics, condition-based maintenance, microelectro-mechanical systems (MEMS) control, and manufacturing process control. Hongwei Zhang (S10-M11) received his PhD from the Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong in 2010. From July 2009 to December 2010, he was a visiting scholar and subsequently a postdoctoralresearcher at the Automation and Robotics Research Institute of the University of Texas at Arlington, Texas, USA. He is now with the Department of Electronic Engineering, the City University of Hong Kong, as a postdoctoral researcher. He is the author of 1 book (in Chinese), 1 book chapter and several refereed journal papers. He is a regular reviewer for several refereed journals and conferences, including Automatica, Systems & Control Letters, IEEE Trans. Neural Netw., IEEE Trans. Syst. Man Cybern. B, Cybern., IEEE Trans. Ind. Electron., IEEE Conf. Decision Control and Int. Joint Conf. Neural Netw., among others. His current research interests includes cooperative control of multi-agent systems, neural adaptive control, receding horizon control, optimal control and approximate dynamic programming (ADP). Abhijit Das received his PhD degree from The University of Texas at Arlington in 2010, all in Electrical Engineering. From 2003 to 2006 he was involved with several projects with Defense Research and Development Organization (DRDO), India. In 2007, he joined Automation and Robotics Research Institute as a Research Assistant. His Ph.D. dissertation won Dean Dissertation Fellowship award in 2010. He is the author of 1 book, 3 book chapters and several journal and conference articles. He is life member of Systems Soc. of India, student member of AIAA, IEEE, SIAM. His profile is also appeared in Marquis Whos Who in America. His research interests are cooperative control of multi-agent systems and neural network for control. Kristian Hengster-Movric received his MS degree from the Faculty of Electrical Engineering and Computing, University of Zagreb, (Zagreb, Croatia) in 2009. He was awarded Rector's Prize for work related to his master thesis. From 2009 he is a PhD student at the University of Texas at Arlington, and is associated with the Automation and Robotics Research Institute (ARRI). In 2010 he became a member of Golden Key International Honour Societyfor his academic achievements.