一类基于神经网络非线性观测器的鲁棒故障检测

针对一类仿射非线性动态系统，提出了一种基 于神经网络非线性观测器的鲁棒故障检测与隔离的新方法．该方法采用神经网络逼近观测器 系统中的非线性项，提高了状态估计的精度，并从理论上证明了状态估计误差稳定且渐近收 敛到零；另一方面引入神经网络分类器进行故障的模式识别，通过在神经网络输入端加入噪 声项来进行训练，提高神经网络的泛化逼近能力，从而保证对被监测系统的建模误差和外部 扰动具有良好的鲁棒性．最后，利用本文方法针对某型歼击机结构故障进行仿真验证，仿真 结果表明本文方法是有效的．

ROBUST FAULT DETECTION FOR A CLASS OF NONLINEAR SYSTEM BASED ON NEURAL NETWORKS OBSERVER

A new type of nonlinear observerbased robust fault detection and isolation (FDI) using neural networks is presented in this paper. Firstly, a radial basis function neural networks is used to approximate the nonlinear item of the monitored system to improve the accuracy of state estimation, and the state estimation error is proved to be zero asymptotically. On the other hand, a neural network classifier is applied to identify the type and location of faults. In order to improve the robustness of fault classification, the neural network has been trained with noise injected inputs and the generalization capability an remarkably be enhanced. Therefore, this FDI strategy has good robustness against modeling error and environment disturbance. At last , this method is applied to faults detection of fighter aircraft with structure damage, and simulation results reveal that this FDI strategy is effective.