Networked Non-linear Stochastic Time-Varying Systems: Analysis and Synthesis [1 ed.] 1032038780, 9781032038780
Networked Non-linear Stochastic Time-Varying Systems: Analysis and Synthesis copes with the filter design, fault estimat
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English Pages 280 [278] Year 2021
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
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
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