Sensor fault diagnosis for a class of time delay uncertain nonlinear systems using neural network

被引：23

作者：

Chen M. ^{[1
]}

Jiang C.-S. ^{[1
]}

Wu Q.-X. ^{[1
]}

机构：

[1] College of Automation Engineering, Nanjing University of Aeronautics and Astronautics

来源：

International Journal of Automation and Computing | 2008年 / 5卷 / 04期

关键词：

Fault diagnosis; Radial basis function (RBF) neural network; Sliding mode observer; Time delay; Uncertain nonlinear system;

D O I：

10.1007/s11633-008-0401-8

中图分类号：

学科分类号：

摘要：

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. © 2008 Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH.

引用

页码：401 / 405

页数：4

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