Networked Non-linear Stochastic Time-Varying Systems: Analysis and Synthesis [1 ed.] 1032038780, 9781032038780

Table of contents :
Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
List of Figures
List of Tables
Acknowledgments
Symbols
List of Acronyms
1. Introduction
1.1. Background
1.1.1. Nonlinear Stochastic Time-Varying Systems
1.1.2. Network-Enhanced Complexities
1.1.3. Filter Design, Fault Estimation and Reliable Control
1.2. Outline
2. Event-Triggered Multi-objective Filtering and Control
2.1. Event-Triggered H∞ Filtering with Fading Channels
2.1.1. Problem Formulation
2.1.2. Design of Filter Gain
2.2. Event-Triggered Variance-Constrained H∞ Control
2.2.1. Problem Formulation
2.2.2. Finite-Horizon Controller Design
2.3. Illustrative Examples
2.3.1. Example 1
2.3.2. Example 2
2.4. Summary
3. Finite-Horizon Reliable Control Subject to Output Quantization
3.1. Problem Formulation
3.2. Reliable Controller Design
3.3. An Illustrative Example
3.4. Summary
4. Finite-Horizon Estimation of Randomly Occurring Faults
4.1. On H∞ Estimation of ROFs with Fading Channels
4.1.1. Problem Formulation
4.1.2. Main Results
4.2. Recursive Estimation of ROFs: the Finite-Horizon Case
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. Set-Membership Filtering under Weighted Try-Once-Discard Protocol
5.1. Problem Formulation
5.2. Main Results
5.2.1. Filter Design Subject to the P(k)-Dependent Constraint
5.2.2. Minimizing the Ellipsoids with Inequality Constraints
5.3. An Illustrative Example
5.4. Summary
6. Distributed Estimation over Sensor Network
6.1. Finite-Horizon Distributed State Estimation with RSTs and RCs
6.1.1. Problem Formulation
6.1.2. Main Results
6.2. Non-Fragile Distributed Fault Estimation: the Finite-Horizon Case
6.2.1. Problem Formulation
6.2.2. Main Results
6.3. Distributed Filtering with RSTs under the RR Protocol
6.3.1. Problem Formulation
6.3.2. Main Results
6.4. Illustrative Examples
6.4.1. Example 1
6.4.2. Example 2
6.4.3. Example 3
6.5. Summary
7. State Estimation for Complex Networks
7.1. State Estimation with Randomly Varying Topologies
7.1.1. Problem Formulation
7.1.2. Analysis of H∞ and Covariance Performances
7.1.3. Design of Finite-Horizon State Estimators
7.2. Partial-Nodes-Based State Estimation under Random Access Protocol
7.2.1. Problem Formulation
7.2.2. Main Results
7.3. Illustrative Examples
7.3.1. Example 1
7.3.2. Example 2
7.4. Summary
8. Event-Triggered Recursive Filtering for Complex Networks with Random Coupling Strengths
8.1. Problem Formulation
8.2. Main Results
8.3. Illustrative Examples
8.3.1. Example 1
8.3.2. Example 2
8.4. Summary
9. Conclusions and Future Work
9.1. Conclusions
9.2. Future Work
Bibliography
Index