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Actuator Fault Detection of Satellite Based on Neural Network Observer

机译:基于神经网络观测器的卫星执行器故障检测

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Satellite attitude control system is an important subsystem that ensures the normal operation of the satellite. Aiming at the problem that the model-based fault detection method relies on modeling accuracy, an actuator fault detection method based on neural network observer is proposed. The neural network is trained with the error back propagation algorithm including a correction term to ensure the accuracy and weight bounded. The neural network is used to identify and estimate actuator failure. The stability of the observer is proved by Lyapunov method. Simulation results show that this method can effectively detect and estimate actuator failure.
机译:卫星姿态控制系统是确保卫星正常运行的重要子系统。针对基于模型的故障检测方法依赖建模精度的问题,提出了一种基于神经网络观测器的执行器故障检测方法。用误差反向传播算法训练神经网络,该算法包括校正项,以确保准确性和加权边界。神经网络用于识别和估计执行器故障。用Lyapunov方法证明了观测器的稳定性。仿真结果表明,该方法可以有效地检测和估计执行器故障。

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