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MARC Lista (tabellare)
Iterative learning control for multi-agent systems coordination / / by Shiping Yang, Jian-Xin Xu, Xuefang Li, Dong Shen
Iterative learning control for multi-agent systems coordination / / by Shiping Yang, Jian-Xin Xu, Xuefang Li, Dong Shen
Autore Yang Shiping <1987->
Pubbl/distr/stampa Singapore : , : John Wiley & Sons, Inc., , 2017
Descrizione fisica 1 online resource (259 pages)
Disciplina 629.8/9
Collana Wiley - IEEE
Soggetto topico Intelligent control systems
Multiagent systems
Machine learning
Iterative methods (Mathematics)
ISBN 1-119-18905-5
1-119-18907-1
1-119-18906-3
Classificazione TEC037000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- 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 Leader's Input Sharing 55 -- 5.3.2 Optimal Design Without Leader's Input Sharing 58 -- 5.3.3 Controller Design with Leader's 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 156 -- 9.4 Illustrative Example 163 -- 9.5 Conclusion 171 -- 10 Synchronization for Networked Lagrangian Systems under Directed Graphs 173 -- 10.1 Introduction 173 -- 10.2 Problem Description 174 -- 10.3 Controller Design and Performance Analysis 175 -- 10.4 Extension to Alignment Condition 181 -- 10.5 Illustrative Example 182 -- 10.6 Conclusion 186 -- 11 Generalized Iterative Learning for Economic Dispatch Problem in a Smart Grid 187 -- 11.1 Introduction 187 -- 11.2 Preliminaries 188 -- 11.2.1 In-Neighbor and Out-Neighbor 188 -- 11.2.2 Discrete-Time Consensus Algorithm 189 -- 11.2.3 Analytic Solution to EDP with Loss Calculation 190 -- 11.3 Main Results 191 -- 11.3.1 Upper Level: Estimating the Power Loss 192 -- 11.3.2 Lower Level: Solving Economic Dispatch Distributively 192 -- 11.3.3 Generalization to the Constrained Case 195 -- 11.4 Learning Gain Design 196 -- 11.5 Application Examples 198 -- 11.5.1 Case Study 1: Convergence Test 199 -- 11.5.2 Case Study 2: Robustness of Command Node Connections 200 -- 11.5.3 Case Study 3: Plug and Play Test 201 -- 11.5.4 Case Study 4: Time-Varying Demand 203 -- 11.5.5 Case Study 5: Application in Large Networks 205 -- 11.5.6 Case Study 6: Relation Between Convergence Speed and Learning Gain 205 -- 11.6 Conclusion 206 -- 12 Summary and Future Research Directions 207 -- 12.1 Summary 207 -- 12.2 Future Research Directions 208 -- 12.2.1 Open Issues in MAS Control 208 -- 12.2.2 Applications 212 -- Appendix A Graph Theory Revisit 221 -- Appendix B Detailed Proofs 223 -- B.1 HOIM Constraints Derivation 223 -- B.2 Proof of Proposition 2.1 224 -- B.3 Proof of Lemma 2.1 225 -- B.4 Proof of Theorem 8.1 227 -- B.5 Proof of Corollary 8.1 228 -- Bibliography 231 -- Index 000.
Record Nr. UNINA-9910270928503321
Yang Shiping <1987->  
Singapore : , : John Wiley & Sons, Inc., , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Iterative learning control for multi-agent systems coordination / / by Shiping Yang, Jian-Xin Xu, Xuefang Li, Dong Shen
Iterative learning control for multi-agent systems coordination / / by Shiping Yang, Jian-Xin Xu, Xuefang Li, Dong Shen
Autore Yang Shiping <1987->
Pubbl/distr/stampa Singapore : , : John Wiley & Sons, Inc., , 2017
Descrizione fisica 1 online resource (259 pages)
Disciplina 629.8/9
Collana Wiley - IEEE
Soggetto topico Intelligent control systems
Multiagent systems
Machine learning
Iterative methods (Mathematics)
ISBN 1-119-18905-5
1-119-18907-1
1-119-18906-3
Classificazione TEC037000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- 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 Leader's Input Sharing 55 -- 5.3.2 Optimal Design Without Leader's Input Sharing 58 -- 5.3.3 Controller Design with Leader's 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 156 -- 9.4 Illustrative Example 163 -- 9.5 Conclusion 171 -- 10 Synchronization for Networked Lagrangian Systems under Directed Graphs 173 -- 10.1 Introduction 173 -- 10.2 Problem Description 174 -- 10.3 Controller Design and Performance Analysis 175 -- 10.4 Extension to Alignment Condition 181 -- 10.5 Illustrative Example 182 -- 10.6 Conclusion 186 -- 11 Generalized Iterative Learning for Economic Dispatch Problem in a Smart Grid 187 -- 11.1 Introduction 187 -- 11.2 Preliminaries 188 -- 11.2.1 In-Neighbor and Out-Neighbor 188 -- 11.2.2 Discrete-Time Consensus Algorithm 189 -- 11.2.3 Analytic Solution to EDP with Loss Calculation 190 -- 11.3 Main Results 191 -- 11.3.1 Upper Level: Estimating the Power Loss 192 -- 11.3.2 Lower Level: Solving Economic Dispatch Distributively 192 -- 11.3.3 Generalization to the Constrained Case 195 -- 11.4 Learning Gain Design 196 -- 11.5 Application Examples 198 -- 11.5.1 Case Study 1: Convergence Test 199 -- 11.5.2 Case Study 2: Robustness of Command Node Connections 200 -- 11.5.3 Case Study 3: Plug and Play Test 201 -- 11.5.4 Case Study 4: Time-Varying Demand 203 -- 11.5.5 Case Study 5: Application in Large Networks 205 -- 11.5.6 Case Study 6: Relation Between Convergence Speed and Learning Gain 205 -- 11.6 Conclusion 206 -- 12 Summary and Future Research Directions 207 -- 12.1 Summary 207 -- 12.2 Future Research Directions 208 -- 12.2.1 Open Issues in MAS Control 208 -- 12.2.2 Applications 212 -- Appendix A Graph Theory Revisit 221 -- Appendix B Detailed Proofs 223 -- B.1 HOIM Constraints Derivation 223 -- B.2 Proof of Proposition 2.1 224 -- B.3 Proof of Lemma 2.1 225 -- B.4 Proof of Theorem 8.1 227 -- B.5 Proof of Corollary 8.1 228 -- Bibliography 231 -- Index 000.
Record Nr. UNINA-9910815011103321
Yang Shiping <1987->  
Singapore : , : John Wiley & Sons, Inc., , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui