Recursive Filtering of Networked Systems with Communication Protocol Scheduling

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This book provides theoretical frameworks using techniques like backward Riccati equations, Kalman filtering theory, Unscented transform, and ellipsoid estimation theory.

Recursive Filtering of Networked Systems with Communication Protocol Scheduling explores protocol-based state estimation for complex networked systems including Kalman filtering for nonlinear systems under Round-Robin (RR) and MEF-TOD protocols, finite-horizon robust state estimation under FlexRay protocol, robust filtering for multi-rate systems under RR and stochastic communication protocols, and state estimation under event-triggering protocol and redundant channel for neural networks. This book provides theoretical frameworks using techniques like backward Riccati equations, Kalman filtering theory, Unscented transform, and ellipsoid estimation theory.

Features

1. Focuses on the intersection of recursive filtering techniques and communication protocol scheduling
2. Introduces communication protocols to allocate limited network resources
3. Enhances the reliability and accuracy of information transmission and minimize the occurrence of information conflicts and congestion in the network
4. Offers practical strategies for real-world challenges in networked systems
5. Explains designing filtering unit for a class of complex networks

This book is aimed at graduate students and researchers in electrical engineering, power systems, control systems.

Obsah

1.Introduction 2. Protocol-Based Unscented Kalman Filtering in the Presence of Stochastic Uncertainties 3. Protocol-Based Extended Kalman Filtering with Quantization Effects: The Round-Robin Case 4. Recursive Set-Membership State Estimation over A FlexRay Network 5. Finite-Horizon H-infinite Filtering via a High-Rate Network with the FlexRay Protocol 6. On Quantized H-infinite Filtering for Multi-Rate Systems Under Stochastic Communication Protocols: The Finite-Horizon Case 7. Distributed Set-Membership Filtering for Multi-Rate Systems Under the Round-Robin Scheduling over Sensor Networks 8. Partial-Neurons-Based State Estimation for Delayed Neural Networks with State-Dependent Noises under Redundant Channels 9. Dynamic Event-Based State Estimation for Delayed Artificial Neural Networks with Multiplicative Noises: A Gain-Scheduled Approach 10. Conclusions and Future Topics