Networked Non-linear Stochastic Time-Varying Systems: Analysis and Synthesis

✍ Scribed by Hongli Dong, Zidong Wang, Nan Hou

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 278
• Edition: 1
• Category: Library

✦ Synopsis

Networked Non-linear Stochastic Time-Varying Systems: Analysis and Synthesis copes with the filter design, fault estimation and reliable control problems for different classes of nonlinear stochastic time-varying systems with network-enhanced complexities. Divided into three parts, the book discusses the finite-horizon filtering, fault estimation and reliable control, and randomly occurring nonlinearities/uncertainties followed by designing of distributed state and fault estimators, and distributed filters. The third part includes problems of variance-constrained H∞ state estimation, partial-nodes-based state estimation and recursive filtering for nonlinear time-varying complex networks with randomly varying topologies, and random coupling strengths.

• Offers a comprehensive treatment of the topics related to Networked Nonlinear Stochastic Time-Varying Systems with rigorous math foundation and derivation
• Unifies existing and emerging concepts concerning control/filtering/estimation and distributed filtering
• Provides a series of latest results by drawing on the conventional theories of systems science, control engineering and signal processing Deal with practical engineering problems such as event triggered H∞ filtering, non-fragile distributed estimation, recursive filtering, set-membership filtering
• Demonstrates illustrative examples in each chapter to verify the correctness of the proposed results

This book is aimed at engineers, mathematicians, scientists, and upper-level students in the fields of control engineering, signal processing, networked control systems, robotics, data analysis, and automation.

✦ 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