Description
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
1. Introduction 2. Event-Triggered Multi-objective Filtering and Control 3. Finite-Horizon Reliable Control Subject to Output Quantization 4. Finite-Horizon Estimation of Randomly Occurring Faults 5. Set-Membership Filtering under Weighted Try-Once-Discard Protocol 6. Distributed Estimation over Sensor Network 7. State Estimation for Complex Networks 8. Event-Triggered Recursive Filtering for Complex Networks with Random Coupling Strengths 9. Conclusions and Future Work
