Event-triggered fault detection for discrete-time T-S fuzzy systems

Xiao-Lei Wang; Guang-Hong Yang

doi:10.1016/j.isatra.2018.02.016

Event-triggered fault detection for discrete-time T-S fuzzy systems

ISA Trans. 2018 May:76:18-30. doi: 10.1016/j.isatra.2018.02.016.

Authors

Xiao-Lei Wang¹, Guang-Hong Yang²

Affiliations

¹ College of Information Science and Engineering, Northeastern University, Shenyang, 110819, PR China. Electronic address: xiaoleiwang1990@126.com.
² College of Information Science and Engineering, Northeastern University, Shenyang, 110819, PR China; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, PR China. Electronic address: yangguanghong@ise.neu.edu.cn.

PMID: 29503010
DOI: 10.1016/j.isatra.2018.02.016

Abstract

This paper is concerned with the design of piecewise fuzzy diagnostic observers for discrete-time T-S fuzzy systems under an event-triggered (ET) communication mechanism. Considering that the premise variables of the fuzzy diagnostic observer and the system may belong to different local space regions due to the introduction of ET mechanism, a partition method-based piecewise fuzzy diagnostic observer is designed to detect faults. The two-term approximation approach is introduced to approximate the time-varying delay. By transforming the augmented system into an input-output form consisting of two interconnected subsystems, the design condition of the piecewise fuzzy diagnostic observer is obtained by using the scaled small gain (SSG) theorem and a piecewise Lyapunov-Krasovskii functional. Furthermore, the L_∞/L₂ and L_∞ fault detection (FD) scheme is used to optimize the FD performance. Finally, two simulation examples are provided to show the efficiency of the proposed design method.

Keywords: Event-triggered mechanism; Fault detection; Piecewise fuzzy diagnostic observer; Scaled small gain (SSG) theorem; Two-term approximation approach.