{"product_id":"iterative-learning-control-for-multiagent-systems-coordination-9781119189046","title":"Iterative Learning Control for Multiagent Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003eA timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, showcasing recent advances and industrially relevant applications\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExplores the synergy between the important topics of iterative learning control (ILC) and multi-agent systems (MAS)\u003c\/li\u003e \u003cli\u003eConcisely summarizes recent advances and significant applications in ILC methods for power grids, sensor networks and control processes\u003c\/li\u003e \u003cli\u003eCovers basic theory, rigorous mathematics as well as engineering practice\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003ePreface \u003c\/b\u003eix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction \u003c\/b\u003e1\u003c\/p\u003e \u003cp\u003e1.1 Introduction to Iterative Learning Control 1\u003c\/p\u003e \u003cp\u003e1.1.1 Contraction-Mapping Approach 3\u003c\/p\u003e \u003cp\u003e1.1.2 Composite Energy Function Approach 4\u003c\/p\u003e \u003cp\u003e1.2 Introduction to MAS Coordination 5\u003c\/p\u003e \u003cp\u003e1.3 Motivation and Overview 7\u003c\/p\u003e \u003cp\u003e1.4 Common Notations in This Book 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Optimal Iterative Learning Control for Multi-agent Consensus Tracking \u003c\/b\u003e11\u003c\/p\u003e \u003cp\u003e2.1 Introduction 11\u003c\/p\u003e \u003cp\u003e2.2 Preliminaries and Problem Description 12\u003c\/p\u003e \u003cp\u003e2.2.1 Preliminaries 12\u003c\/p\u003e \u003cp\u003e2.2.2 Problem Description 13\u003c\/p\u003e \u003cp\u003e2.3 Main Results 15\u003c\/p\u003e \u003cp\u003e2.3.1 Controller Design for Homogeneous Agents 15\u003c\/p\u003e \u003cp\u003e2.3.2 Controller Design for Heterogeneous Agents 20\u003c\/p\u003e \u003cp\u003e2.4 Optimal Learning Gain Design 21\u003c\/p\u003e \u003cp\u003e2.5 Illustrative Example 23\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Iterative Learning Control for Multi-agent Coordination Under Iteration-Varying Graph \u003c\/b\u003e27\u003c\/p\u003e \u003cp\u003e3.1 Introduction 27\u003c\/p\u003e \u003cp\u003e3.2 Problem Description 28\u003c\/p\u003e \u003cp\u003e3.3 Main Results 29\u003c\/p\u003e \u003cp\u003e3.3.1 Fixed Strongly Connected Graph 29\u003c\/p\u003e \u003cp\u003e3.3.2 Iteration-Varying Strongly Connected Graph 32\u003c\/p\u003e \u003cp\u003e3.3.3 Uniformly Strongly Connected Graph 37\u003c\/p\u003e \u003cp\u003e3.4 Illustrative Example 38\u003c\/p\u003e \u003cp\u003e3.5 Conclusion 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Iterative Learning Control for Multi-agent Coordination with Initial State Error \u003c\/b\u003e41\u003c\/p\u003e \u003cp\u003e4.1 Introduction 41\u003c\/p\u003e \u003cp\u003e4.2 Problem Description 42\u003c\/p\u003e \u003cp\u003e4.3 Main Results 43\u003c\/p\u003e \u003cp\u003e4.3.1 Distributed D-type Updating Rule 43\u003c\/p\u003e \u003cp\u003e4.3.2 Distributed PD-type Updating Rule 48\u003c\/p\u003e \u003cp\u003e4.4 Illustrative Examples 49\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Multi-agent Consensus Tracking with Input Sharing by Iterative Learning Control \u003c\/b\u003e53\u003c\/p\u003e \u003cp\u003e5.1 Introduction 53\u003c\/p\u003e \u003cp\u003e5.2 Problem Formulation 54\u003c\/p\u003e \u003cp\u003e5.3 Controller Design and Convergence Analysis 54\u003c\/p\u003e \u003cp\u003e5.3.1 Controller Design Without Leader’s Input Sharing 55\u003c\/p\u003e \u003cp\u003e5.3.2 Optimal Design Without Leader’s Input Sharing 58\u003c\/p\u003e \u003cp\u003e5.3.3 Controller Design with Leader’s Input Sharing 59\u003c\/p\u003e \u003cp\u003e5.4 Extension to Iteration-Varying Graph 60\u003c\/p\u003e \u003cp\u003e5.4.1 Iteration-Varying Graph with Spanning Trees 60\u003c\/p\u003e \u003cp\u003e5.4.2 Iteration-Varying Strongly Connected Graph 60\u003c\/p\u003e \u003cp\u003e5.4.3 Uniformly Strongly Connected Graph 62\u003c\/p\u003e \u003cp\u003e5.5 Illustrative Examples 63\u003c\/p\u003e \u003cp\u003e5.5.1 Example 1: Iteration-Invariant Communication Graph 63\u003c\/p\u003e \u003cp\u003e5.5.2 Example 2: Iteration-Varying Communication Graph 64\u003c\/p\u003e \u003cp\u003e5.5.3 Example 3: Uniformly Strongly Connected Graph 66\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 68\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 A HOIM-Based Iterative Learning Control Scheme for Multi-agent Formation \u003c\/b\u003e69\u003c\/p\u003e \u003cp\u003e6.1 Introduction 69\u003c\/p\u003e \u003cp\u003e6.2 Kinematic Model Formulation 70\u003c\/p\u003e \u003cp\u003e6.3 HOIM-Based ILC for Multi-agent Formation 71\u003c\/p\u003e \u003cp\u003e6.3.1 Control Law for Agent 1 72\u003c\/p\u003e \u003cp\u003e6.3.2 Control Law for Agent 2 74\u003c\/p\u003e \u003cp\u003e6.3.3 Control Law for Agent 3 75\u003c\/p\u003e \u003cp\u003e6.3.4 Switching Between Two Structures 78\u003c\/p\u003e \u003cp\u003e6.4 Illustrative Example 78\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 P-type Iterative Learning for Non-parameterized Systems with Uncertain Local Lipschitz Terms \u003c\/b\u003e81\u003c\/p\u003e \u003cp\u003e7.1 Introduction 81\u003c\/p\u003e \u003cp\u003e7.2 Motivation and Problem Description 82\u003c\/p\u003e \u003cp\u003e7.2.1 Motivation 82\u003c\/p\u003e \u003cp\u003e7.2.2 Problem Description 83\u003c\/p\u003e \u003cp\u003e7.3 Convergence Properties with Lyapunov Stability Conditions 84\u003c\/p\u003e \u003cp\u003e7.3.1 Preliminary Results 84\u003c\/p\u003e \u003cp\u003e7.3.2 Lyapunov Stable Systems 86\u003c\/p\u003e \u003cp\u003e7.3.3 Systems with Stable Local Lipschitz Terms but Unstable Global Lipschitz Factors 90\u003c\/p\u003e \u003cp\u003e7.4 Convergence Properties in the Presence of Bounding Conditions 92\u003c\/p\u003e \u003cp\u003e7.4.1 Systems with Bounded Drift Term 92\u003c\/p\u003e \u003cp\u003e7.4.2 Systems with Bounded Control Input 94\u003c\/p\u003e \u003cp\u003e7.5 Application of P-type Rule in MAS with Local Lipschitz Uncertainties 97\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Synchronization for Nonlinear Multi-agent Systems by Adaptive Iterative Learning Control \u003c\/b\u003e101\u003c\/p\u003e \u003cp\u003e8.1 Introduction 101\u003c\/p\u003e \u003cp\u003e8.2 Preliminaries and Problem Description 102\u003c\/p\u003e \u003cp\u003e8.2.1 Preliminaries 102\u003c\/p\u003e \u003cp\u003e8.2.2 Problem Description for First-Order Systems 102\u003c\/p\u003e \u003cp\u003e8.3 Controller Design for First-Order Multi-agent Systems 105\u003c\/p\u003e \u003cp\u003e8.3.1 Main Results 105\u003c\/p\u003e \u003cp\u003e8.3.2 Extension to Alignment Condition 107\u003c\/p\u003e \u003cp\u003e8.4 Extension to High-Order Systems 108\u003c\/p\u003e \u003cp\u003e8.5 Illustrative Example 113\u003c\/p\u003e \u003cp\u003e8.5.1 First-Order Agents 114\u003c\/p\u003e \u003cp\u003e8.5.2 High-Order Agents 115\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Distributed Adaptive Iterative Learning Control for Nonlinear Multi-agent Systems with State Constraints \u003c\/b\u003e123\u003c\/p\u003e \u003cp\u003e9.1 Introduction 123\u003c\/p\u003e \u003cp\u003e9.2 Problem Formulation 124\u003c\/p\u003e \u003cp\u003e9.3 Main Results 127\u003c\/p\u003e \u003cp\u003e9.3.1 Original Algorithms 127\u003c\/p\u003e \u003cp\u003e9.3.2 Projection Based Algorithms 135\u003c\/p\u003e \u003cp\u003e9.3.3 Smooth Function Based Algorithms 138\u003c\/p\u003e \u003cp\u003e9.3.4 Alternative Smooth Function Based Algorithms 141\u003c\/p\u003e \u003cp\u003e9.3.5 Practical Dead-Zone Based Algorithms 156\u003c\/p\u003e \u003cp\u003e9.4 Illustrative Example 163\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Synchronization for Networked Lagrangian Systems under Directed Graphs \u003c\/b\u003e173\u003c\/p\u003e \u003cp\u003e10.1 Introduction 173\u003c\/p\u003e \u003cp\u003e10.2 Problem Description 174\u003c\/p\u003e \u003cp\u003e10.3 Controller Design and Performance Analysis 175\u003c\/p\u003e \u003cp\u003e10.4 Extension to Alignment Condition 181\u003c\/p\u003e \u003cp\u003e10.5 Illustrative Example 182\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Generalized Iterative Learning for Economic Dispatch Problem in a Smart Grid \u003c\/b\u003e187\u003c\/p\u003e \u003cp\u003e11.1 Introduction 187\u003c\/p\u003e \u003cp\u003e11.2 Preliminaries 188\u003c\/p\u003e \u003cp\u003e11.2.1 In-Neighbor and Out-Neighbor 188\u003c\/p\u003e \u003cp\u003e11.2.2 Discrete-Time Consensus Algorithm 189\u003c\/p\u003e \u003cp\u003e11.2.3 Analytic Solution to EDP with Loss Calculation 190\u003c\/p\u003e \u003cp\u003e11.3 Main Results 191\u003c\/p\u003e \u003cp\u003e11.3.1 Upper Level: Estimating the Power Loss 192\u003c\/p\u003e \u003cp\u003e11.3.2 Lower Level: Solving Economic Dispatch Distributively 192\u003c\/p\u003e \u003cp\u003e11.3.3 Generalization to the Constrained Case 195\u003c\/p\u003e \u003cp\u003e11.4 Learning Gain Design 196\u003c\/p\u003e \u003cp\u003e11.5 Application Examples 198\u003c\/p\u003e \u003cp\u003e11.5.1 Case Study 1: Convergence Test 199\u003c\/p\u003e \u003cp\u003e11.5.2 Case Study 2: Robustness of Command Node Connections 200\u003c\/p\u003e \u003cp\u003e11.5.3 Case Study 3: Plug and Play Test 201\u003c\/p\u003e \u003cp\u003e11.5.4 Case Study 4: Time-Varying Demand 203\u003c\/p\u003e \u003cp\u003e11.5.5 Case Study 5: Application in Large Networks 205\u003c\/p\u003e \u003cp\u003e11.5.6 Case Study 6: Relation Between Convergence Speed and Learning Gain 205\u003c\/p\u003e \u003cp\u003e11.6 Conclusion 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Summary and Future Research Directions \u003c\/b\u003e207\u003c\/p\u003e \u003cp\u003e12.1 Summary 207\u003c\/p\u003e \u003cp\u003e12.2 Future Research Directions 208\u003c\/p\u003e \u003cp\u003e12.2.1 Open Issues in MAS Control 208\u003c\/p\u003e \u003cp\u003e12.2.2 Applications 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Graph Theory Revisit \u003c\/b\u003e221\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Detailed Proofs \u003c\/b\u003e223\u003c\/p\u003e \u003cp\u003eB.1 HOIM Constraints Derivation 223\u003c\/p\u003e \u003cp\u003eB.2 Proof of Proposition 2.1 224\u003c\/p\u003e \u003cp\u003eB.3 Proof of Lemma 2.1 225\u003c\/p\u003e \u003cp\u003eB.4 Proof of Theorem 8.1 227\u003c\/p\u003e \u003cp\u003eB.5 Proof of Corollary 8.1 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography \u003c\/b\u003e231\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex \u003c\/b\u003e000\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407006015831,"sku":"9781119189046","price":104.45,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119189046.jpg?v=1730497858","url":"https:\/\/bookcurl.com\/products\/iterative-learning-control-for-multiagent-systems-coordination-9781119189046","provider":"Book Curl","version":"1.0","type":"link"}