Iterative Learning Based Intermittent Fault Estimation for a Class of Linear Uncertain Repeated Systems

被引：0

作者：

Feng L. ^{[1
,2
]}

Chai Y. ^{[2
]}

Xu S.-Q. ^{[2
,3
]}

Zhang K. ^{[2
]}

Yang Z.-M. ^{[2
]}

机构：

[1] College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing

[2] Key Laboratory of Complex System Safety and Control, Ministry of Education Chongqing, College of Automation, Chongqing

[3] College of Electrical and Automation Engineering, Hefei University of Technology, Hefei

来源：

Chai, Yi (chaiyi@cqu.edu.cn) | 1600年 / Science Press卷 / 46期

基金：

中国国家自然科学基金;

关键词：

Fault estimation; Intermittent fault; Iterative learning scheme; Linear matrix inequality; Linear uncertain repeated systems;

D O I：

10.16383/j.aas.2018.c170252

中图分类号：

学科分类号：

摘要：

This paper presents a novel observer-based fault estimation method using the iterative leaning scheme for linear uncertain repeated systems where the parameter uncertainties are time-varying. Firstly, a state observer is constructed to monitor the system status and the Lyapunov function is utilized to ensure the stability of the system. After providing the design of a robust monotonically convergence for the error system, an optimal function is presented to ensure the iterative learning law is applicable to systems. Meanwhile, two Lemmas and a reasonable assumption are utilized to linearize the nonlinear terms in our initial results. The LMI toolbox is utilized to obtain the results of the learning gain. In addition, the theoretical results have been verified through simulation tests. It has also shown that the proposed iterative learning based approach can be applicable to more general linear uncertain repeated systems. Copyright © 2020 Acta Automatica Sinica. All rights reserved.

引用

页码：307 / 319

页数：12

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