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Neural network-based fault diagnosis scheme for satellite attitude control system
Gao S(高升); Zhang W(张伟); He X(何旭); Cao YX(曹云侠)
Department空间自动化技术研究室
Conference Name30th Chinese Control and Decision Conference, CCDC 2018
Conference DateJune 9, 2018 - June 11, 2018
Conference PlaceShenyang, China
Source PublicationProceedings of the 30th Chinese Control and Decision Conference, CCDC 2018
PublisherIEEE
Publication PlaceNew York
2018
Pages3990-3995
Indexed ByEI
EI Accession number20183205650004
Contribution Rank1
ISBN978-1-5386-1243-9
KeywordBp Neural Network, Fault Diagnosis Satellite Attitude Control System
Abstract

In order to improve reliability of on-orbit satellite, this paper proposes a novel fault diagnosis scheme based on BP neural network method for satellite attitude control systems (ACSs) subject to external disturbances of the system. First, a model of the satellite ACS is established. Second, the BP neural network is introduced in detail, which includes its structure and learning algorithm. Then, a fault diagnosis scheme based on the BP neural network observer is designed and further a recursive multistep prediction method is presented to train the network. Furthermore, the prediction output of the observer is used to compare with the real output of sensors to generate residual that is further used to diagnose faults of the system. Finally, numerical simulations of a satellite ACS are performed to illustrate the effectiveness and potential applications of the proposed scheme.

Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22393
Collection空间自动化技术研究室
Corresponding AuthorGao S(高升)
AffiliationShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Gao S,Zhang W,He X,et al. Neural network-based fault diagnosis scheme for satellite attitude control system[C]. New York:IEEE,2018:3990-3995.
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