Control and State Estimation for Dynamical Network Systems with Complex Samplings

This book focusses 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, 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 is established for control and state estimation problems of dynamical network systems with complex samplings

Exploits set of latest techniques as linear matrix inequality approach, Vander monde 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.