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

18 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

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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