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E-raamat: Iterative Learning Control for Multi-agent Systems Coordination

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  • Formaat: PDF+DRM
  • Sari: IEEE Press
  • Ilmumisaeg: 03-Mar-2017
  • Kirjastus: Wiley-IEEE Press
  • Keel: eng
  • ISBN-13: 9781119189060
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Iterative Learning Control for Multi-agent Systems CoordinationSuurem pilt
  • Formaat: PDF+DRM
  • Sari: IEEE Press
  • Ilmumisaeg: 03-Mar-2017
  • Kirjastus: Wiley-IEEE Press
  • Keel: eng
  • ISBN-13: 9781119189060
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A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, showcasing recent advances and industrially relevant applications

  • Explores the synergy between the important topics of iterative learning control (ILC) and multi-agent systems (MAS)
  • Concisely summarizes recent advances and significant applications in ILC methods for power grids, sensor networks and control processes
  • Covers basic theory, rigorous mathematics as well as engineering practice
Preface ix
1 Introduction
1(10)
1.1 Introduction to Iterative Learning Control
1(4)
1.1.1 Contraction-Mapping Approach
3(1)
1.1.2 Composite Energy Function Approach
4(1)
1.2 Introduction to MAS Coordination
5(2)
1.3 Motivation and Overview
7(2)
1.4 Common Notations in This Book
9(2)
2 Optimal Iterative Learning Control for Multi-agent Consensus Tracking
11(16)
2.1 Introduction
11(1)
2.2 Preliminaries and Problem Description
12(3)
2.2.1 Preliminaries
12(1)
2.2.2 Problem Description
13(2)
2.3 Main Results
15(6)
2.3.1 Controller Design for Homogeneous Agents
15(5)
2.3.2 Controller Design for Heterogeneous Agents
20(1)
2.4 Optimal Learning Gain Design
21(2)
2.5 Illustrative Example
23(3)
2.6 Conclusion
26(1)
3 Iterative Learning Control for Multi-agent Coordination Under Iteration-Varying Graph
27(14)
3.1 Introduction
27(1)
3.2 Problem Description
28(1)
3.3 Main Results
29(9)
3.3.1 Fixed Strongly Connected Graph
29(3)
3.3.2 Iteration-Varying Strongly Connected Graph
32(5)
3.3.3 Uniformly Strongly Connected Graph
37(1)
3.4 Illustrative Example
38(2)
3.5 Conclusion
40(1)
4 Iterative Learning Control for Multi-agent Coordination with Initial State Error
41(12)
4.1 Introduction
41(1)
4.2 Problem Description
42(1)
4.3 Main Results
43(6)
4.3.1 Distributed D-type Updating Rule
43(5)
4.3.2 Distributed PD-type Updating Rule
48(1)
4.4 Illustrative Examples
49(1)
4.5 Conclusion
50(3)
5 Multi-agent Consensus Tracking with Input Sharing by Iterative Learning Control
53(16)
5.1 Introduction
53(1)
5.2 Problem Formulation
54(1)
5.3 Controller Design and Convergence Analysis
54(6)
5.3.1 Controller Design Without Leader's Input Sharing
55(3)
5.3.2 Optimal Design Without Leader's Input Sharing
58(1)
5.3.3 Controller Design with Leader's Input Sharing
59(1)
5.4 Extension to Iteration-Varying Graph
60(3)
5.4.1 Iteration-Varying Graph with Spanning Trees
60(1)
5.4.2 Iteration-Varying Strongly Connected Graph
60(2)
5.4.3 Uniformly Strongly Connected Graph
62(1)
5.5 Illustrative Examples
63(5)
5.5.1 Example 1: Iteration-Invariant Communication Graph
63(1)
5.5.2 Example 2: Iteration-Varying Communication Graph
64(2)
5.5.3 Example 3: Uniformly Strongly Connected Graph
66(2)
5.6 Conclusion
68(1)
6 A HOIM-Based Iterative Learning Control Scheme for Multi-agent Formation
69(12)
6.1 Introduction
69(1)
6.2 Kinematic Model Formulation
70(1)
6.3 HOIM-Based ILC for Multi-agent Formation
71(7)
6.3.1 Control Law for Agent 1
72(2)
6.3.2 Control Law for Agent 2
74(1)
6.3.3 Control Law for Agent 3
75(3)
6.3.4 Switching Between Two Structures
78(1)
6.4 Illustrative Example
78(2)
6.5 Conclusion
80(1)
7 P-type Iterative Learning for Non-parameterized Systems with Uncertain Local Lipschitz Terms
81(20)
7.1 Introduction
81(1)
7.2 Motivation and Problem Description
82(2)
7.2.1 Motivation
82(1)
7.2.2 Problem Description
83(1)
7.3 Convergence Properties with Lyapunov Stability Conditions
84(8)
7.3.1 Preliminary Results
84(2)
7.3.2 Lyapunov Stable Systems
86(4)
7.3.3 Systems with Stable Local Lipschitz Terms but Unstable Global Lipschitz Factors
90(2)
7.4 Convergence Properties in the Presence of Bounding Conditions
92(5)
7.4.1 Systems with Bounded Drift Term
92(2)
7.4.2 Systems with Bounded Control Input
94(3)
7.5 Application of P-type Rule in MAS with Local Lipschitz Uncertainties
97(2)
7.6 Conclusion
99(2)
8 Synchronization for Nonlinear Multi-agent Systems by Adaptive Iterative Learning Control
101(22)
8.1 Introduction
101(1)
8.2 Preliminaries and Problem Description
102(3)
8.2.1 Preliminaries
102(1)
8.2.2 Problem Description for First-Order Systems
102(3)
8.3 Controller Design for First-Order Multi-agent Systems
105(3)
8.3.1 Main Results
105(2)
8.3.2 Extension to Alignment Condition
107(1)
8.4 Extension to High-Order Systems
108(5)
8.5 Illustrative Example
113(5)
8.5.1 First-Order Agents
114(1)
8.5.2 High-Order Agents
115(3)
8.6 Conclusion
118(5)
9 Distributed Adaptive Iterative Learning Control for Nonlinear Multi-agent Systems with State Constraints
123(52)
9.1 Introduction
123(1)
9.2 Problem Formulation
124(3)
9.3 Main Results
127(20)
9.3.1 Original Algorithms
127(8)
9.3.2 Projection Based Algorithms
135(3)
9.3.3 Smooth Function Based Algorithms
138(3)
9.3.4 Alternative Smooth Function Based Algorithms
141(4)
9.3.5 Practical Dead-Zone Based Algorithms
145(2)
9.4 Illustrative Example
147(27)
9.5 Conclusion
174(1)
10 Synchronization for Networked Lagrangian Systems under Directed Graphs
175(14)
10.1 Introduction
175(1)
10.2 Problem Description
176(1)
10.3 Controller Design and Performance Analysis
177(6)
10.4 Extension to Alignment Condition
183(1)
10.5 Illustrative Example
184(4)
10.6 Conclusion
188(1)
11 Generalized Iterative Learning for Economic Dispatch Problem in a Smart Grid
189(20)
11.1 Introduction
189(1)
11.2 Preliminaries
190(3)
11.2.1 In-Neighbor and Out-Neighbor
190(1)
11.2.2 Discrete-Time Consensus Algorithm
191(1)
11.2.3 Analytic Solution to EDP with Loss Calculation
192(1)
11.3 Main Results
193(5)
11.3.1 Upper Level: Estimating the Power Loss
194(1)
11.3.2 Lower Level: Solving Economic Dispatch Distributively
194(3)
11.3.3 Generalization to the Constrained Case
197(1)
11.4 Learning Gain Design
198(2)
11.5 Application Examples
200(8)
11.5.1 Case Study 1: Convergence Test
201(1)
11.5.2 Case Study 2: Robustness of Command Node Connections
202(1)
11.5.3 Case Study 3: Plug and Play Test
203(2)
11.5.4 Case Study 4: Time-Varying Demand
205(2)
11.5.5 Case Study 5: Application in Large Networks
207(1)
11.5.6 Case Study 6: Relation Between Convergence Speed and Learning Gain
207(1)
11.6 Conclusion
208(1)
12 Summary and Future Research Directions
209(14)
12.1 Summary
209(1)
12.2 Future Research Directions
210(13)
12.2.1 Open Issues in MAS Control
210(4)
12.2.2 Applications
214(9)
Appendix A Graph Theory Revisit
223(2)
Appendix B Detailed Proofs
225(8)
B.1 HOIM Constraints Derivation
225(1)
B.2 Proof of Proposition 2.1
226(1)
B.3 Proof of Lemma 2.1
227(2)
B.4 Proof of Theorem 8.1
229(1)
B.5 Proof of Corollary 8.1
230(3)
Bibliography 233(12)
Index 245
Shiping Yang, Jian-Xin Xu, and Xuefang Li National University of Singapore

Dong Shen Beijing University of Chemical Technology, P.R. China
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