Offline neural network based fault tolerant control for vertical tail damaged aircraft

This paper investigates the offline neural network based-fault tolerant control for an aircraft that suffers vertical-tail damage. First, the damaged model of the aircraft is obtained, and the external disturbance is estimated by a disturbance observer then, an optimal control scheme is proposed to...

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主要な著者: Amin Ziaei, Hamed KharratI, Mina Salim, Ali Farzamnia
フォーマット: Proceedings
言語:English
English
出版事項: IEEE 2021
主題:
オンライン・アクセス:https://eprints.ums.edu.my/id/eprint/31223/3/Offline%20neural%20network%20based%20fault%20tolerant%20control%20for%20vertical%20tail%20damaged%20aircraft-ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/31223/2/Offline%20neural%20network%20based%20fault%20tolerant%20control%20for%20vertical%20tail%20damaged%20aircraft.pdf
https://eprints.ums.edu.my/id/eprint/31223/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105019389&doi=10.1109%2fICCIA52082.2021.9403566&partnerID=40&md5=f4c136835799f278a66f4714ce00c96f
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要約:This paper investigates the offline neural network based-fault tolerant control for an aircraft that suffers vertical-tail damage. First, the damaged model of the aircraft is obtained, and the external disturbance is estimated by a disturbance observer then, an optimal control scheme is proposed to control the aircraft in nominal condition. This optimal control scheme is developed into faulty condition by the offline neural networks. The simulation results show the effectiveness of the proposed method in comparison to the existing methods in the literature. Key Words—fault tolerant control, vertical tail damage, neural networks, linear quadratic regulator, offline learning.