Dynamic neural network-based fault diagnosis for attitude control subsystem of a satellite

Authors:
Z. Q. Li;L. Ma;K. Khorasani
Affiliations:
Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada;Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada and Department of Applied Computer Science, Tokyo Polytechnic University, Atsugi, Kanagawa, Japan;Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

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Abstract

The objective of this paper is to develop a dynamic neural network scheme for fault detection and isolation (FDI) in the reaction wheels of a satellite. The goal is to decide whether a bus voltage fault, a current loss fault or a temperature fault has occurred in one of the three reaction wheels and further to localize which wheel is faulty. In order to achieve these objectives, three dynamic neural networks are introduced to model the dynamics of the wheels on all three axes independently. Due to the dynamic property of the wheel, the architecture utilized is the Elman recurrent network with backpropagation learning algorithm. The effectiveness of this neural network-based FDI scheme is investigated and a comparative study is conducted with the performance of a generalized observer-based scheme. The simulation results have demonstrated the advantages of the proposed neural network-based method.