基于动态神经网络的机电作动系统传感器信号恢复研究

Research on Signal Recovery of Electromechanical Actuation System Based on Dynamic Neural Network

机电作动器逐步应用于新型多电飞机中,其中的传感器故障对于机电作动系统的正常工作具有较大影响。针对余度传感器故障无法判定正确的传感器信号以及丧失全部传感器数据情况下信号重构的问题,采用处理时序信号有优势的神经网络辨识学习方法对故障传感器的信号进行恢复,由此解决输入信号故障情况下的信号重构问题。对比了两种动态神经网络的差异,即非线性动态神经网络与NARX网络。试验结果表明:NARX网络可有效准确完成信号丧失下的信号恢复,对比网络线性回归、误差自相关与互相关系数可得NARX恢复结果优于非线性动态神经网络。

Electromechanical actuator is gradually applied in new more-electric aircraft. However, sensor failure in EMA system has a great impact on the normal operation of the system. Considering the problem of the correct sensor determination after the redundant sensors fault and the signal recovery after complete loss of signal, the dynamic neural network was used to recover the sensor signal, so as to solve the problem of faulty input signals after the signal loss. The differences between the computed results of the nonlinear and the NARX dynamic neural network were compared. The simulation and test results show that the NARX can be used to effectively and accurately perform signal recovery under signal loss, and its recovery result is better than that of the nonlinear dynamic neural network considering the linear regression, error self-correlation and cross-correlation coefficient.