Control and State Estimation for Dynamical Network Systems with Complex Samplings

✍ Scribed by Bo Shen, Zidong Wang, Qi Li

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 307
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
List of Figures
List of Tables
Preface
Author Biographies
Acknowledgements
Symbols
List of Acronyms
1. Introduction
1.1. Background
1.2. Recent Advances
1.2.1. Nonuniform Sampling
1.2.2. Stochastic Sampling
1.2.3. Event-Triggered Sampling
1.2.4. Dynamic Event-Triggered Sampling
1.3. Outline
2. Stabilization and Control under Noisy Sampling Intervals
2.1. Stabilization with Single Input
2.1.1. Problem Formulation
2.1.2. Main Results
2.2. Quantized/Saturated Control with Multiple Inputs
2.2.1. Problem Formulation
2.2.2. Main Results
2.3. Illustrative Examples
2.3.1. Example 1
2.3.2. Example 2
2.4. Summary
3. Distributed State Estimation with Nonuniform Samplings
3.1. Problem Formulation
3.2. Main Results
3.3. An Illustrative Example
3.4. Summary
4. Event-Triggered Control for Switched Systems
4.1. Event-Triggered Control: The Input-to-State Stability
4.1.1. Problem Formulation
4.1.2. Main Results
4.2. Event-Triggered Pinning Synchronization Control
4.2.1. Problem Formulation
4.2.2. Main Results
4.3. Illustrative Examples
4.3.1. Example 1
4.3.2. Example 2
4.4. Summary
5. Event-Triggered H∞ State Estimation for State-Saturated Systems
5.1. Distributed Event-Triggered H∞ State Estimation in Sensor Networks
5.1.1. Problem Formulation
5.1.2. Main Results
5.2. Event-Triggered H∞ State Estimation in Complex Networks
5.2.1. Problem Formulation
5.2.2. Main Results
5.3. Illustrative Examples
5.3.1. Example 1
5.3.2. Example 2
5.4. Summary
6. Event-Triggered State Estimation for Discrete-Time Neural Networks
6.1. Event-Triggered State Estimation with Stochastic Parameters
6.1.1. Problem Formulation
6.1.2. Main Results
6.2. Event-Triggered H∞ State Estimation in Genetic Regulatory Networks
6.2.1. Problem Formulation
6.2.2. Main Results
6.3. Illustrative Examples
6.3.1. Example 1
6.3.2. Example 2
6.4. Summary
7. Event-Triggered Fusion Estimation for Multi-Rate Systems
7.1. Event-Triggered Fusion Estimation with Coloured Measurement Noises
7.1.1. Problem Formulation
7.1.2. Design of Local Filters
7.1.3. Fusion Estimation
7.2. Event-Triggered Fusion Estimation with Sensor Degradations
7.2.1. Problem Formulation
7.2.2. Design of Local Filters
7.2.3. Fusion Estimation
7.3. Illustrative Examples
7.3.1. Example 1
7.3.2. Example 2
7.4. Summary
8. Synchronization Control under Dynamic Event-Triggered Mechanisms
8.1. Problem Formulation
8.2. Main Results
8.3. Illustrative Examples
8.3.1. Demonstrations of Results
8.3.2. Comparisons of Results
8.4. Summary
9. Filtering or Estimation under Dynamic Event-Triggered Mechanisms
9.1. Dynamic Event-Triggered Robust Filtering with Censored Measurements
9.1.1. Problem Formulation
9.1.2. Main Results
9.2. Dynamic Event-Triggered Distributed Filtering on GE Channels
9.2.1. Problem Formulation
9.2.2. Main Results
9.3. Dynamic Event-Triggered Resilient H∞ State Estimation
9.3.1. Problem Formulation
9.3.2. Main Results
9.4. Illustrative Examples
9.4.1. Example 1
9.4.2. Example 2
9.4.3. Example 3
9.5. Sumamry
10. Conclusions and Future Work
10.1. Conclusions
10.2. Future Work
Bibliography
Index