Iterative learning fault diagnosis algorithm for nonlinear systems based on extended filter

被引：0

作者：

Tao, Hong-Feng ^{[1
]}

Chen, Da-Peng ^{[1
]}

Yang, Hui-Zhong ^{[1
]}

机构：

[1] Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi

来源：

Kongzhi yu Juece/Control and Decision | 2015年 / 30卷 / 06期

关键词：

Fault diagnosis filter; Iterative learning; Nonlinear system; System extension; Virtual fault;

D O I：

10.13195/j.kzyjc.2014.0512

中图分类号：

学科分类号：

摘要：

A fault reconstruction approach based on the filter is proposed for nonlinear systems with actuator and sensor fault. To make the algorithm applicable to both state and output sides, a new state equation is constructed by the system equation to transform and extend the system, which can convert nonlinear terms and fault of original system output to the state equation of the extended system. Afterwards, a fault diagnosis filter based on the iterative learning algorithm is chosen to update virtual fault to make it approximate to actual fault, and the algorithm can detect and estimate the system faults of different types adaptively. Simulation results of a single-joint robot show the feasibility and effectiveness of the proposed algorithm. ©, 2015, Northeast University. All right reserved.

引用

页码：1027 / 1032

页数：5

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