Event-triggered robust fault diagnosis and control of linear Roesser systems: A unified framework
Autor: | Nader Meskin, Hamid Alikhani |
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Rok vydání: | 2021 |
Předmět: |
0209 industrial biotechnology
Observer (quantum physics) Computer science Linear matrix inequalities Detection framework 02 engineering and technology Fault (power engineering) Fault detection and isolation Robust fault detection and isolation 020901 industrial engineering & automation Robust fault diagnosis Control theory Gas absorption 0202 electrical engineering electronic engineering information engineering Isolation (database systems) Electrical and Electronic Engineering Stochastics Control (linguistics) Event-triggered framework Linear roesse system Unified framework Stochastic systems 020208 electrical & electronic engineering Fault detection isolations Event-triggered Control framework Control and Systems Engineering Bounded function Fault detection Energy (signal processing) Data transmission |
Zdroj: | Automatica. 128:109575 |
ISSN: | 0005-1098 |
DOI: | 10.1016/j.automatica.2021.109575 |
Popis: | In this paper, the problem of simultaneous robust fault detection and isolation and control in the presence of the stochastic, energy bounded, and peak bounded disturbances for linear Roesser systems through the network with limited resources is investigated. Toward this goal, a novel event-triggered integrated fault detection, isolation and control (IFDIC) framework is proposed where a time concept is considered for linear Roesser systems to define an event-triggered mechanism for data transmission of the output measurement to the IFDIC module and the control input to the plant through the network. Then, the sufficient conditions for the robust event-triggered fault detection and isolation observer, robust event-triggered H ∞ ∕ H 2 ∕ L 1 control are derived using linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed event-triggered IFDIC framework is demonstrated through simulation studies for thermal and gas absorption processes. |
Databáze: | OpenAIRE |
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