Control and State Estimation for Dynamical Network Systems with Complex Samplings

This book focuses on the control and state estimation problems for dynamical network systems with complex samplings subject to various network-induced phenomena. It includes a series of control and state estimation problems tackled under the passive sampling fashion. Further, it explains the effects from the active sampling fashion, i.e., event-based sampling is examined on the control/estimation performance, and novel design technologies are proposed for controllers/estimators. Simulation results are provided for better understanding of the proposed control/filtering methods. By drawing on a variety of theories and methodologies such as Lyapunov function, linear matrix inequalities, and Kalman theory, sufficient conditions are derived for guaranteeing the existence of the desired controllers and estimators, which are parameterized according to certain matrix inequalities or recursive matrix equations. Covers recent advances of control and state estimation for dynamical network systems with complex samplings from the engineering perspective Systematically introduces the complex sampling concept, methods, and application for the control and state estimation Presents unified framework for control and state estimation problems of dynamical network systems with complex samplings Exploits a set of the latest techniques such as linear matrix inequality approach, Vandermonde matrix approach, and trace derivation approach Explains event-triggered multi-rate fusion estimator, resilient distributed sampled-data estimator with predetermined specifications This book is aimed at researchers, professionals, and graduate students in control engineering and signal processing. This book focusses on the control and state estimation problems for dynamical network systems with complex samplings subject to various network-induced phenomena. It includes a series of control and state estimation problems tackled under the passive sampling fashion, effects from the active sampling fashion with simulation results. Cover 1 Half Title 2 Title Page 4 Copyright Page 5 Dedication 6 Contents 8 List of Figures 12 List of Tables 14 Preface 16 Author Biographies 18 Acknowledgements 20 Symbols 22 List of Acronyms 24 1. Introduction 26 1.1. Background 26 1.2. Recent Advances 29 1.2.1. Nonuniform Sampling 29 1.2.2. Stochastic Sampling 31 1.2.3. Event-Triggered Sampling 32 1.2.4. Dynamic Event-Triggered Sampling 34 1.3. Outline 37 2. Stabilization and Control under Noisy Sampling Intervals 42 2.1. Stabilization with Single Input 42 2.1.1. Problem Formulation 42 2.1.2. Main Results 44 2.2. Quantized/Saturated Control with Multiple Inputs 48 2.2.1. Problem Formulation 48 2.2.2. Main Results 50 2.3. Illustrative Examples 56 2.3.1. Example 1 56 2.3.2. Example 2 57 2.4. Summary 62 3. Distributed State Estimation with Nonuniform Samplings 64 3.1. Problem Formulation 64 3.2. Main Results 67 3.3. An Illustrative Example 75 3.4. Summary 79 4. Event-Triggered Control for Switched Systems 80 4.1. Event-Triggered Control: The Input-to-State Stability 80 4.1.1. Problem Formulation 81 4.1.2. Main Results 85 4.2. Event-Triggered Pinning Synchronization Control 100 4.2.1. Problem Formulation 100 4.2.2. Main Results 103 4.3. Illustrative Examples 113 4.3.1. Example 1 113 4.3.2. Example 2 116 4.4. Summary 121 5. Event-Triggered H∞ State Estimation for State-Saturated Systems 122 5.1. Distributed Event-Triggered H∞ State Estimation in Sensor Networks 122 5.1.1. Problem Formulation 122 5.1.2. Main Results 126 5.2. Event-Triggered H∞ State Estimation in Complex Networks 133 5.2.1. Problem Formulation 133 5.2.2. Main Results 137 5.3. Illustrative Examples 143 5.3.1. Example 1 143 5.3.2. Example 2 147 5.4. Summary 150 6. Event-Triggered State Estimation for Discrete-Time Neural Networks 152 6.1. Event-Triggered State Estimation with Stochastic Parameters 152 6.1.1. Problem Formulation 153 6.1.2. Main Results 157 6.2. Event-Triggered H∞ State Estimation in Genetic Regulatory Networks 169 6.2.1. Problem Formulation 169 6.2.2. Main Results 172 6.3. Illustrative Examples 178 6.3.1. Example 1 178 6.3.2. Example 2 179 6.4. Summary 186 7. Event-Triggered Fusion Estimation for Multi-Rate Systems 188 7.1. Event-Triggered Fusion Estimation with Coloured Measurement Noises 188 7.1.1. Problem Formulation 188 7.1.2. Design of Local Filters 191 7.1.3. Fusion Estimation 198 7.2. Event-Triggered Fusion Estimation with Sensor Degradations 199 7.2.1. Problem Formulation 199 7.2.2. Design of Local Filters 202 7.2.3. Fusion Estimation 207 7.3. Illustrative Examples 209 7.3.1. Example 1 209 7.3.2. Example 2 213 7.4. Summary 217 8. Synchronization Control under Dynamic Event-Triggered Mechanisms 220 8.1. Problem Formulation 220 8.2. Main Results 222 8.3. Illustrative Examples 230 8.3.1. Demonstrations of Results 230 8.3.2. Comparisons of Results 231 8.4. Summary 234 9. Filtering or Estimation under Dynamic Event-Triggered Mechanisms 236 9.1. Dynamic Event-Triggered Robust Filtering with Censored Measurements 237 9.1.1. Problem Formulation 237 9.1.2. Main Results 239 9.2. Dynamic Event-Triggered Distributed Filtering on GE Channels 248 9.2.1. Problem Formulation 248 9.2.2. Main Results 251 9.3. Dynamic Event-Triggered Resilient H∞ State Estimation 257 9.3.1. Problem Formulation 257 9.3.2. Main Results 260 9.4. Illustrative Examples 271 9.4.1. Example 1 271 9.4.2. Example 2 275 9.4.3. Example 3 277 9.5. Sumamry 281 10. Conclusions and Future Work 282 10.1. Conclusions 282 10.2. Future Work 285 Bibliography 286 Index 306 Delays;,Filtering;,H?,Resilient,State,Estimation;,Sample-data,Systems;,Stochastic,Parameters;,Synchronization Delays,Filtering,H? Resilient State Estimation,Sample-data Systems,Stochastic Parameters,Synchronization "This book focuses on the control and state estimation problems for dynamical network systems with complex samplings subject to various network-induced phenomena. It includes a series of control and state estimation problems tackled under the passive sampling fashion. Further, it explains the effects from the active sampling fashion, i.e., event-based sampling is examined on the control/estimation performance, novel design technologies are proposed for controllers/estimators. Simulation results are provided for better understanding of the proposed control/filtering methods. This book aims at researchers, professionals, graduate students in control engineering, and signal processing"-- Provided by publisher