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.
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Li, Z.Q., Ma, L., Khorasani, K. (2006). Dynamic Neural Network-Based Fault Diagnosis for Attitude Control Subsystem of a Satellite. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_34
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DOI: https://doi.org/10.1007/978-3-540-36668-3_34
Publisher Name: Springer, Berlin, Heidelberg
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