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