This book presents novel state estimation methods for several classes of networked multi-rate systems including state estimation methods for networked multi-rate systems with various complex networked-induced phenomena and communication protocols. The systems investigated include stochastic nonlinear systems, time-delay systems, linear repetitive processes, and artificial neural networks. The techniques used are mainly the Lyapunov stability theory, the optimal estimation theory, the lifting technique, and certain convex optimization method.
Features
Gives a systematic investigation of the state estimation of multi-rate systems
Discusses results on state estimation problems under network-induced complexities
Studies different kinds of multi-rate systems including multi-rate nonlinear systems, multi-rate neural networks, and multi-rate linear repetitive processes
Explores network-enhanced complexities and communication protocols
Includes case studies showing the applicability of developed estimation algorithms including practical examples like DC servo systems and continuous stirred tank reactor systems
Analysis and Synthesis for Networked Multi-Rate Systems is aimed at graduate students and researchers in signal processing, control systems, and electrical engineering.
Author(s): Yuxuan Shen, Zidong Wang, Hongli Dong
Publisher: CRC Press
Year: 2023
Language: English
Pages: 260
Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Authors
Acknowledgments
Symbols
1. Introduction
1.1. Multi-Rate Systems
1.1.1. State-Space Model of the Multi-Rate Systems
1.1.2. Transformation from Multi-Rate Systems to Single-Rate Systems
1.2. Estimation Problems for Multi-Rate Systems
1.2.1. H∞ Estimation Approach
1.2.2. Kalman Filtering Approach and Its Variants
1.2.3. Other Estimation Approaches
1.2.4. Handling the Network-Induced Challenges
1.3. Outline of This Book
2. Non-Fragile H∞ Filtering for Multi-Rate Time-Delayed Systems over Sensor Networks
2.1. Problem Formulation
2.2. Non-Fragile H∞ Filter Design
2.3. Some Special Cases
2.4. Simulation Examples
2.5. Conclusion
3. H∞ Filtering for Multi-Rate Artificial Neural Networks with Integral Measurements
3.1. Problem Formulation
3.2. H∞ Filter Design
3.3. Simulation Examples
3.4. Conclusion
4. Recursive State Estimation for Multi-Rate Systems with Sensor Resolutions
4.1. Problem Formulation
4.2. Estimator Design
4.3. A Simulation Example
4.4. Conclusion
5. Minimum-Variance State and Fault Estimation for Multi-Rate Systems with Dynamical Bias
5.1. Problem Formulation
5.2. Joint State and Fault Estimator Design
5.3. An Illustrative Example
5.4. Conclusion
6. H∞ Filtering for Multi-Rate Systems under p-Persistent CSMA Protocol
6.1. Problem Formulation and Preliminaries
6.2. H∞ Filter Design
6.3. Illustrative Examples
6.4. Conclusion
7. l2-l∞ State Estimation for Artificial Neural Networks under High-Rate Channels with Round-Robin Protocol
7.1. Problem Formulation
7.2. l2-l∞ Estimator Design
7.3. Simulation Examples
7.4. Conclusion
8. Recursive State Estimation for Multi-Rate Systems with Distributed Time-Delays under Round-Robin Protocol
8.1. Problem Formulation
8.2. Estimator Design
8.3. Simulation Examples
8.4. Conclusion
9. Fusion Estimation for Multi-Rate Linear Repetitive Processes under Weighted Try-Once-Discard Protocol
9.1. Problem Formulation
9.2. Estimator Design
9.3. A Simulation Example
9.4. Conclusion
10. Outlier-Resistant Recursive Filtering for Multi-Rate Systems under Weighted Try-Once-Discard Protocol
10.1. Problem Formulation
10.2. Outlier-Resistant Filter Design
10.3. Boundedness Analysis
10.4. Illustrate Examples
10.5. Conclusion
11. Dynamic Event-Based Recursive Filtering for Multi-Rate Systems with Integral Measurements over Sensor Networks
11.1. Problem Formulation
11.2. Dynamic Event-Based Filter Design
11.3. Simulation Examples
11.3.1. Effectiveness of the Proposed Filtering Scheme
11.3.2. Comparison of Results
11.4. Conclusion
12. Conclusion and Further Work
Bibliography
Index
