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Distributed Fault-Tolerant Control of Large-Scale Systems: an Active Fault Diagnosis Approach

F. Boem
•
A. Gallo
•
T. Parisini
•
D. M. Raimondo
2020
  • journal article

Periodico
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
Abstract
The paper proposes a methodology to effectively address the increasingly important problem of distributed faulttolerant control for large-scale interconnected systems. The approach dealt with combines, in a holistic way, a distributed fault detection and isolation algorithm with a specific tube-based model predictive control scheme. A distributed fault-tolerant control strategy is illustrated to guarantee overall stability and constraint satisfaction even after the occurrence of a fault. In particular, each subsystem is controlled and monitored by a local unit. The fault diagnosis component consists of a passive set-based fault detection algorithm and an active fault isolation one, yielding fault-isolability subject to local input and state constraints. The distributed active fault isolation module – thanks to a modification of the local inputs – allows to isolate the fault that has occurred avoiding the usual drawback of controllers that possibly hide the effect of the faults. The Active Fault Isolation method is used as a decision support tool for the fault tolerant control strategy after fault detection. The distributed design of the tube-based model predictive control allows the possible disconnection of faulty subsystems or the reconfiguration of local controllers after fault isolation. Simulation results on a well-known power network benchmark show the effectiveness of the proposed methodology
DOI
10.1109/TCNS.2019.2913557
WOS
WOS:000521969000028
Archivio
http://hdl.handle.net/11368/2952723
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85065119340
https://ieeexplore.ieee.org/document/8700304
Diritti
open access
license:copyright editore
license:copyright editore
FVG url
https://arts.units.it/request-item?handle=11368/2952723
Soggetti
  • Distributed fault dia...

Scopus© citazioni
26
Data di acquisizione
Jun 14, 2022
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Web of Science© citazioni
52
Data di acquisizione
Mar 19, 2024
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Data di acquisizione
Apr 19, 2024
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