Stability of stochastic T-S fuzzy systems via dynamic event-triggered control

Abstract This paper aims to examine the practical input-to-state stability (p-ISS) of stochastic T-S fuzzy systems (T-SFS) with parameter uncertainty by employing dynamic event-triggered control (DETC). In order to reduce the frequency of triggering events and the number of messages transmitted, we propose a DETC scheme, which introduces the waiting time to avoid Zeno behaviour and further saves communication resources. Additionally, within the DETC approach, LMI-based criteria are formulated to guarantee p-ISS for nonlinear stochastic T-SFS.

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Publication Details

Journal
Journal of Physics Conference Series
Published
2025-02-01
DOI
https://doi.org/10.1088/1742-6596/2960/1/012004
Primary Topic
Neural Networks Stability and Synchronization
Type
article
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article

Stability of stochastic T-S fuzzy systems via dynamic event-triggered control

Linna Liu, Yali Zhang, Shixiang Zhou, Chenglong Pan
Journal of Physics Conference Series
Neural Networks Stability and Synchronization
article

Stability of stochastic T-S fuzzy systems via dynamic event-triggered control

Linna Liu, Yali Zhang, Shixiang Zhou, Chenglong Pan
article en

Abstract

Abstract This paper aims to examine the practical input-to-state stability (p-ISS) of stochastic T-S fuzzy systems (T-SFS) with parameter uncertainty by employing dynamic event-triggered control (DETC). In order to reduce the frequency of triggering events and the number of messages transmitted, we propose a DETC scheme, which introduces the waiting time to avoid Zeno behaviour and further saves communication resources. Additionally, within the DETC approach, LMI-based criteria are formulated to guarantee p-ISS for nonlinear stochastic T-SFS.

Journal of Physics Conference SeriesVol. 2960(1)
Zhongyuan University of Technology (CN)
Openalex Percentile: Top 98%
Neural Networks Stability and Synchronization
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