主要针对无人机传感器故障种类较多、类型复杂等特点, 通过灰色模型与Elman神经网络对时延进行建模预测, 利用最小方差原理得到组合时延预测模型, 最后将其应用于无人机传感器故障诊断, 并通过仿真验证组合预测模型对故障诊断时延具有较高的预测精度, 证明了该诊断方法的有效性。

Considering the fault characteristics of UAV sensors of multiple and complex fault types, we modeled and predicted the time delay by using grey model and Elman neural network, and used the principle of minimum variance to determine the best combination of model weighting coefficients.The method was applied to the fault diagnosis of UAV sensors.The simulation result showed that the combined prediction model has a high precision in predicting the fault diagnosis delay, and proved the validity of the proposed diagnostic method.