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.
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