Abstract
This paper presents an actuator fault detection and identification (FDI) scheme for satellite attitude control systems. A state-space approach is used and a nonlinear-in-parameters neural network (NLPNN) is employed to identify the general unknown fault. The recurrent network configuration is obtained by a combination of feedforward network architectures and dynamical elements in the form of stable filters. The neural network weights are updated based on a modified backpropagation scheme. The stability of the overall fault detection scheme is shown using Lyapunov's direct method. Simulation results are presented to show the performance of the proposed fault detection scheme
Published Version
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