Sensor fault diagnosis for a class of time delay uncertain nonlinear systems using neural network
Sensor fault diagnosis for a class of time delay uncertain nonlinear systems using neural network
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DOI：
10.1007/s11633-008-0401-8
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发表时间：
2008-10
影响因子：
4.3
通讯作者：
Mou Chen;Changsheng Jiang;Qingxian Wu
中科院分区：
文献类型：
--
作者：
Mou Chen;Changsheng Jiang;Qingxian Wu
针对一类输入不确定的时滞非线性系统，提出了一种基于神经网络的传感器故障诊断滑模观测器方案。传感器故障和系统输入不确定性被假定为未知但有界的。采用径向基函数（RBF）神经网络对传感器故障进行逼近。基于RBF神经网络的输出，提出了滑模观测器。利用李雅普诺夫方法，以矩阵不等式的形式给出了系统稳定性的一个判据.最后，通过一个实例说明了基于所提出的滑模观测器的故障诊断的有效性。
In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on the output of the RBF neural network, the sliding mode observer is presented. Using the Lyapunov method, a criterion for stability is given in terms of matrix inequality. Finally, an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.