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A new strategy for fault estimation in Takagi-Sugeno fuzzy systems via a fuzzy learning observer

  • Qingxian Jia
  • , Wen Chen*
  • , Yi Jin
  • , Yingchun Zhang
  • , Huayi Li
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper is to suggest a new strategy for fault estimation in Takagi-Sugeno (T-S) fuzzy systems. A fuzzy Learning Observer (FLO) is constructed to achieve simultaneous estimation of system states and actuator faults. The FLO is able to estimate both constant and time-varying faults accurately, and a systematic method is also proposed to select gain matrices for the FLOs. Stability and convergence of the proposed observer is proved using Lyapunov stability theory. The design of FLOs can be formulated in terms of Linear Matrix Inequalities (LMIs) that can be conveniently solved using LMI optimization technique. A single-link flexible manipulator is employed to verify the effectiveness of the proposed fault-estimating approaches.

Original languageEnglish
Title of host publicationProceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3228-3233
Number of pages6
EditionMarch
ISBN (Electronic)9781479958252
DOIs
StatePublished - 2 Mar 2015
Event2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 - Shenyang, China
Duration: 29 Jun 20144 Jul 2014

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)
NumberMarch
Volume2015-March

Conference

Conference2014 11th World Congress on Intelligent Control and Automation, WCICA 2014
Country/TerritoryChina
CityShenyang
Period29/06/144/07/14

Keywords

  • Fault estimation
  • Fuzzy systems
  • Learning observers

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