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Dynamic neural network-based estimator for fault diagnosis in reaction wheel actuator of satellite attitude control system

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3 Author(s)
E. S. Tehrani ; Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que., Canada ; K. Khorasani ; S. Tafazoli

This paper presents an approach to simultaneous fault detection and isolation in the reaction wheel actuator of the satellite attitude control system. A model-based adaptive nonlinear parameter estimation technique is used based on a highly accurate reaction wheel dynamical model while each parameter is an indication of a specific type of fault in the system. The estimation is based on the nonlinear finite-memory filtering strategy that is solved for optimal estimation functions. To make the optimization feasible for on-line application, the optimal estimation functions are approximated by MLP neural networks thus reducing the functional optimization problem to a nonlinear programming problem, namely, the optimization of the neural weights. The well-known standard back-propagation algorithm and backpropagation through-time algorithm were employed inside the neural adaptation algorithms to obtain the required gradients. Simulation results show the effectiveness of the methodology for the proposed application.

Published in:

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.  (Volume:4 )

Date of Conference:

July 31 2005-Aug. 4 2005