Adaptive Event-Triggered Fault Detection for Interval Type-2 T-S Fuzzy Systems with Sensor Saturation

Xiang Gui Guo, Xiao Fan, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)

Abstract

This article deals with the adaptive event-triggered (AET) fault detection filter (FDF) problem for nonlinear-networked control systems with component and sensor faults, network-induced delays, uncertainties, external disturbances, and asynchronous premise variables. This system is represented by the interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model, which can effectively capture parameter uncertainties. A new AET mechanism with many advantages, such as no singular problem, no degradation into a traditional time-triggered mechanism, fewer triggers, and no Zeno behavior, is constructed. The error caused by the AET mechanism is first regarded as a disturbance and thus can be attenuated by the H_{\infty } norm bound. Based on Lyapunov's stability theory, novel sufficient conditions for H_\infty performance and stability are then derived. In addition, the filter parameters and the weight matrix of the trigger condition are obtained in terms of linear matrix inequality (LMI) techniques. Finally, a numerical example is used to demonstrate the feasibility and merit of the proposed fault detection scheme.

Original languageEnglish
Article number9099612
Pages (from-to)2310-2321
Number of pages12
JournalIEEE Transactions on Fuzzy Systems
Volume29
Issue number8
DOIs
Publication statusPublished - 2021 Aug

Bibliographical note

Funding Information:
Manuscript received April 1, 2020; accepted May 18, 2020. Date of publication May 25, 2020; date of current version August 4, 2021. This work was supported in part by National Natural Science Foundation of China under Grant 61773056, in part by Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB under Grant BK19AE018, in part by the Open Project Program of Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology under Grant MADTOF2019A02, in part by Fundamental Research Funds for the Central Universities of USTB under grant 230201606500061, in part by the National Key Research and Development Program of China under Grant 2017YFB1401203, in part by the National Natural Science Foundation of China under Grant 61473195, Grant 61873338, Grant 61673055, Grant 61673056, Grant 61803026, and Grant 61603274, in part by Beijing Key Discipline Development Program under grant XK100080537, in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. NRF-2020R1A2C1005449), and in part by the Brain Korea 21 Plus Project in 2020. (Corresponding author: Choon Ki Ahn.) Xiang-Gui Guo is with the Shunde Graduate School, University of Science and Technology Beijing, Foshan 528000, China, and also with Beijing Engineering Research Center of Industrial Spectrum Imaging, the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China (e-mail: guoxianggui@163.com).

Publisher Copyright:
© 1993-2012 IEEE.

Keywords

  • Adaptive event-triggered (AET) mechanism
  • fault detection filter (FDF)
  • interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model
  • linear matrix inequality (LMI)
  • performance

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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