Control and State Estimation for Dynamical Network Systems with Complex Samplings
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- Hardcover
- Taschenbuch
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Form:Einzelkauf Download
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Sprache:Englisch
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eBook Format:PDF
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Produktdetails
Format
Kopierschutz
Ja
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
14.09.2022
Verlag
Taylor & Francis eBooksSeitenzahl
306 (Printausgabe)
Auflage
1. Auflage
Sprache
Englisch
EAN
9781000635454
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, sücient 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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