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

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.