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Hybrid control and motion planning of dynamical legged locomotion / / Nasser Sadati ... [et al.]
| Hybrid control and motion planning of dynamical legged locomotion / / Nasser Sadati ... [et al.] |
| Pubbl/distr/stampa | Hoboken, N.J. : , : Wiley, , 2012 |
| Descrizione fisica | 1 online resource (286 p.) |
| Disciplina |
629.8/932
629.8932 |
| Altri autori (Persone) | SadatiNasser |
| Collana | IEEE press series on systems science and engineering |
| Soggetto topico |
Mobile robots
Robots - Motion Walking |
| ISBN |
1-118-39372-4
1-118-39374-0 1-283-59324-6 9786613905697 1-118-39370-8 |
| Classificazione | TEC037000 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface ix -- 1. Introduction 1 -- 1.1 Objectives of Legged Locomotion and Challenges in Controlling Dynamic Walking and Running 1 -- 1.2 Literature Overview 4 -- 1.2.1 Tracking of Time Trajectories 4 -- 1.2.2 Poincar'e Return Map and Hybrid Zero Dynamics 5 -- 1.3 The Objective of the Book 7 -- 1.3.1 Hybrid Zero Dynamics in Walking with Double Support Phase 7 -- 1.3.2 Hybrid Zero Dynamics in Running with an Online Motion Planning Algorithm 8 -- 1.3.3 Online Motion Planning Algorithms for Flight Phases of Running 9 -- 1.3.4 Hybrid Zero Dynamics in 3D Running 10 -- 1.3.5 Hybrid Zero Dynamics in Walking with Passive Knees 11 -- 1.3.6 Hybrid Zero Dynamics with Continuous-Time Update Laws 12 -- 2. Preliminaries in Hybrid Systems 13 -- 2.1 Basic Definitions 13 -- 2.2 Poincar'e Return Map for Hybrid Systems 16 -- 2.3 Low-Dimensional Stability Analysis 23 -- 2.4 Stabilization Problem 28 -- 3. Asymptotic Stabilization of Periodic Orbits forWalking with Double Support Phase 35 -- 3.1 Introduction 35 -- 3.2 Mechanical Model of a Biped Walker 37 -- 3.2.1 The Biped Robot 37 -- 3.2.2 Dynamics of the Flight Phase 37 -- 3.2.3 Dynamics of the Single Support Phase 39 -- 3.2.4 Dynamics of the Double Support Phase 40 -- 3.2.5 Impact Model 43 -- 3.2.6 Transition from the Double Support Phase to the Single Support Phase 45 -- 3.2.7 Hybrid Model of Walking 45 -- 3.3 Control Laws for the Single and Double Support Phases 46 -- 3.3.1 Single Support Phase Control Law 46 -- 3.3.2 Double Support Phase Control Law 49 -- 3.4 Hybrid Zero Dynamics (HZD) 54 -- 3.4.1 Analysis of HZD in the Single Support Phase 55 -- 3.4.2 Analysis of HZD in the Double Support Phase 57 -- 3.4.3 Restricted Poincar'e Return Map 58 -- 3.5 Design of an HZD Containing a Prespecified Periodic Solution 60 -- 3.5.1 Design of the Output Functions 60 -- 3.5.2 Design of u1d and u2d 62 -- 3.6 Stabilization of the Periodic Orbit 67 -- 3.7 Motion Planning Algorithm 71 -- 3.7.1 Motion Planning Algorithm for the Single Support Phase 72.
3.7.2 Motion Planning Algorithm for the Double Support Phase 73 -- 3.7.3 Constructing a Period-One Orbit for the Open-Loop Hybrid Model of Walking 76 -- 3.8 Numerical Example for the Motion Planning Algorithm 77 -- 3.9 Simulation Results of the Closed-Loop Hybrid System 82 -- 3.9.1 Effect of Double Support Phase on Angular Momentum Transfer and Stabilization 82 -- 3.9.2 Effect of Event-Based Update Laws on Momentum Transfer and Stabilization 92 -- 4. Asymptotic Stabilization of Periodic Orbits for Planar Monopedal Running 95 -- 4.1 Introduction 95 -- 4.2 Mechanical Model of a Monopedal Runner 97 -- 4.2.1 The Monopedal Runner 97 -- 4.2.2 Dynamics of the Flight Phase 97 -- 4.2.3 Dynamics of the Stance Phase 98 -- 4.2.4 Open-Loop Hybrid Model of Running 99 -- 4.3 Reconfiguration Algorithm for the Flight Phase 99 -- 4.3.1 Determination of the Reachable Set 103 -- 4.4 Control Laws for Stance and Flight Phases 120 -- 4.4.1 Stance Phase Control Law 121 -- 4.4.2 Flight Phase Control Law 122 -- 4.4.3 Event-Based Update Law 124 -- 4.5 Hybrid Zero Dynamics and Stabilization 125 -- 4.6 Numerical Results 127 -- 5. Online Generation of Joint Motions During Flight Phases of Planar Running 137 -- 5.1 Introduction 137 -- 5.2 Mechanical Model of a Planar Open Kinematic Chain 138 -- 5.3 Motion Planning Algorithm to Generate Continuous Joint Motions 140 -- 5.3.1 Determining the Reachable Set from the Origin 143 -- 5.3.2 Motion Planning Algorithm 150 -- 5.4 Motion Planning Algorithm to Generate Continuously Differentiable Joint Motions 152 -- 6. Stabilization of Periodic Orbits for 3D Monopedal Running 159 -- 6.1 Introduction 159 -- 6.2 Open-Loop Hybrid Model of a 3D Running 160 -- 6.2.1 Dynamics of the Flight Phase 162 -- 6.2.2 Dynamics of the Stance Phase 163 -- 6.2.3 Transition Maps 164 -- 6.2.4 Hybrid Model 166 -- 6.3 Design of a Period-One Solution for the Open-Loop Model of Running 167 -- 6.4 Numerical Example 172 -- 6.5 Within-Stride Controllers 175 -- 6.5.1 Stance Phase Control Law 175. 6.5.2 Flight Phase Control Law 178 -- 6.6 Event-Based Update Laws for Hybrid Invariance 181 -- 6.6.1 Takeoff Update Laws 184 -- 6.6.2 Impact Update Laws 185 -- 6.7 Stabilization Problem 186 -- 6.8 Simulation Results 189 -- 7. Stabilization of Periodic Orbits for Walking with Passive Knees 193 -- 7.1 Introduction 193 -- 7.2 Open-Loop Model of Walking 194 -- 7.2.1 Mechanical Model of the Planar Bipedal Robot 194 -- 7.2.2 Dynamics of the Single Support Phase 195 -- 7.2.3 Impact Map 195 -- 7.2.4 Open-Loop Impulsive Model of Walking 196 -- 7.3 Motion Planning Algorithm 197 -- 7.4 Numerical Example 200 -- 7.5 Continuous-Times Controllers 202 -- 7.6 Event-Based Controllers 209 -- 7.6.1 Hybrid Invariance 209 -- 7.6.2 Continuity of the Continuous-Time Controllers During the Within-Stride Transitions 212 -- 7.7 Stabilization Problem 213 -- 7.8 Simulation of the Closed-Loop Hybrid System 217 -- 8. Continuous-Time Update Laws During Continuous Phases of Locomotion 221 -- 8.1 Introduction 221 -- 8.2 Invariance of the Exponential Stability Behavior for a Class of Impulsive Systems 222 -- 8.3 Outline of the Proof of Theorem 8.1 224 -- 8.4 Application to Legged Locomotion 227 -- A. Proofs Associated with Chapter 3 229 -- A.1 Proof of Lemma 3.3 229 -- A.2 Proof of Lemma 3.4 230 -- A.3 Proof of Lemma 3.7 230 -- B. Proofs Associated with Chapter 4 233 -- B.1 Proof of Lemma 4.2 233 -- B.2 Proof of Theorem 4.2 234 -- C. Proofs Associated with Chapter 6 237 -- C.1 Proof of Lemma 6.1 237 -- C.2 Proof of Lemma 6.2 238 -- C.3 Invertibility of the Stance Phase Decoupling Matrix on the Periodic Orbit 240 -- Bibliography 241 -- Index 249. |
| Record Nr. | UNINA-9910139076103321 |
| Hoboken, N.J. : , : Wiley, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 | ||
| ||
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 | ||
| ||