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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, su%uFB03cient 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.
