Description
Taylor and Francis Ltd Control and State Estimation for Dynamical Network Systems with Complex Samplings 1st Edition 2022 Hardbound by Shen, Bo
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 perspectiveSystematically introduces the complex sampling concept, methods, and application for the control and state estimationPresents unified framework for control and state estimation problems of dynamical network systems with complex samplingsExploits a set of the latest techniques such as linear matrix inequality approach, Vandermonde matrix approach, and trace derivation approachExplains event-triggered multi-rate fusion estimator, resilient distributed sampled-data estimator with predetermined specificationsThis book is aimed at researchers, professionals, and graduate students in control engineering and signal processing. 1. Introduction 2. Stabilization and Control under Noisy Sampling Intervals 3. Distributed State Estimation over Sensor Networks with Nonuniform Samplings 4. Event-Triggered Control for Switched Systems 5. Event-Triggered H State Estimation for State-Saturated Systems 6. Event-Triggered State Estimation for Discrete-Time Neural Networks 7. Event-Triggered Fusion Estimation for Multi-Rate Systems 8. Synchronization Control under Dynamic Event-Triggered Mechanisms 9. Filtering or State Estimation under Dynamic Event-Triggered Mechanisms 10. Conclusions and Future Work