1st Edition

Control and State Estimation for Dynamical Network Systems with Complex Samplings

By Bo Shen, Zidong Wang, Qi Li Copyright 2023
    306 Pages 66 B/W Illustrations
    by CRC Press

    This book focuses on the control and state estimation problems for dynamical network systems with complex samplings subject to various network-induced phenomena. It includes a series of control and state estimation problems tackled under the passive sampling fashion. Further, it explains the effects from the active sampling fashion, i.e., event-based sampling is examined on the control/estimation performance, and novel design technologies are proposed for controllers/estimators. Simulation results are provided for better understanding of the proposed control/filtering methods. By drawing on a variety of theories and methodologies such as Lyapunov function, linear matrix inequalities, and Kalman theory, sufficient conditions are derived for guaranteeing the existence of the desired controllers and estimators, which are parameterized according to certain matrix inequalities or recursive matrix equations.

    • Covers recent advances of control and state estimation for dynamical network systems with complex samplings from the engineering perspective
    • Systematically introduces the complex sampling concept, methods, and application for the control and state estimation
    • Presents unified framework for control and state estimation problems of dynamical network systems with complex samplings
    • Exploits a set of the latest techniques such as linear matrix inequality approach, Vandermonde matrix approach, and trace derivation approach
    • Explains event-triggered multi-rate fusion estimator, resilient distributed sampled-data estimator with predetermined specifications

    This book is aimed at researchers, professionals, and graduate students in control engineering and signal processing.

    1. Introduction 1.1. Background 1.2 Recent Advances 1.3 Outline 2. Stabilization and Control under Noisy Sampling Intervals 2.1 Stabilization with Single Input 2.2 Quantized/Saturated Control with Multiple Inputs 2.3 Illustrative Examples 2.4 Summary 3. Distributed State Estimation over Sensor Networks with Nonuniform Samplings 3.1 Problem Formulation 3.2 Main Results 3.3 An Illustrative Example 3.4 Summary 4. Event-Triggered Control for Switched Systems 4.1 Event-Triggered Control: The Input-to-State Stability 4.2 Event-Triggered Pinning Synchronization Control 4.3 Illustrative Examples 4.4 Summary 5. Event-Triggered H∞ State Estimation for State-Saturated Systems 5.1 Distributed Event-Triggered H∞ State Estimation in Sensor Networks 5.2 Event-Triggered H∞ State Estimation in Complex Networks 5.3 Illustrative Examples 5.4 Summary 6. Event-Triggered State Estimation for Discrete-Time Neural Networks 6.1 Event-Triggered State Estimation with Stochastic Parameters 6.2 Event-Triggered H∞ State Estimation in Genetic Regulatory Networks 6.3 Illustrative Examples 6.4 Summary 7. Event-Triggered Fusion Estimation for Multi-Rate Systems 7.1 Event-Triggered Fusion Estimation with Colored Measurement Noises 7.2 Event-Triggered Fusion Estimation with Sensor Degradations 7.3 Illustrative Examples 7.4 Summary 8. Synchronization Control under Dynamic Event-Triggered Mechanisms 8.1 Problem Formulation 8.2 Main Results 8.3 Illustrative Examples 8.4 Summary 9. Filtering or State Estimation under Dynamic Event-Triggered Mechanisms 9.1 Dynamic Event-Triggered Robust Filtering with Censored Measurements 9.2 Dynamic Event-Triggered Distributed Filtering on Gilbert-Elliott Channels 9.3 Dynamic Event-Triggered Resilient H∞ State Estimation 9.4 Illustrative Examples 9.5 Summary 10. Conclusions and Future Work


    Bo Shen, Zidong Wang, Qi Li