Robust fault diagnosis for a satellite large angle attitude system using an iterative neuron PID (INPID) observer

A fault detection and diagnosis (FDD) scheme using an iterative neuron PID (INPID) observer is explored in this paper. The observer input, which is used to estimate state faults, is computed by utilizing the proportional, integral, and derivative information of the fault estimation error. Two classes of robust adaptive algorithms are adopted to update the parameters of the observer input. Theoretically, the convergence properties of these adaptive algorithms are investigated in two different ways, and the stability of this fault detection and diagnosis scheme is analyzed as well. Finally, the proposed FDD scheme is applied to a satellite with large angle attitude maneuvers, and the simulation results demonstrate its good performance

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