Adaptive Event‐Triggered Delayed Impulsive Control for the Mean‐Square Exponential Stabilization of Uncertain Stochastic Complex Networks With Mixed Delays

ABSTRACT This study investigates the mean‐square exponential stabilization of uncertain stochastic complex networks subject to multiple hybrid delays, including nonlinear time‐varying delays, coupling delays, and distributed delays. To overcome the challenges introduced by these delays, we develop a new impulsive differential inequality that accommodates mixed delay effects. By leveraging an adaptive event‐triggered delayed impulsive control strategy, we derive sufficient stability conditions for the network. The proposed approach not only addresses parameter uncertainties but also eliminates the Zeno behavior. Numerical simulations demonstrate the effectiveness of the control methodology.

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Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2026-04-30
DOI
https://doi.org/10.1002/rnc.70568
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
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article

Adaptive Event‐Triggered Delayed Impulsive Control for the Mean‐Square Exponential Stabilization of Uncertain Stochastic Complex Networks With Mixed Delays

Z Zhang, Hao Chen, Chengfeng Yang, Liangliang Li
International Journal of Robust and Nonlinear Control
Neural Networks Stability and Synchronization
article

Adaptive Event‐Triggered Delayed Impulsive Control for the Mean‐Square Exponential Stabilization of Uncertain Stochastic Complex Networks With Mixed Delays

Z Zhang, Hao Chen, Chengfeng Yang, Liangliang Li
article en

Abstract

ABSTRACT This study investigates the mean‐square exponential stabilization of uncertain stochastic complex networks subject to multiple hybrid delays, including nonlinear time‐varying delays, coupling delays, and distributed delays. To overcome the challenges introduced by these delays, we develop a new impulsive differential inequality that accommodates mixed delay effects. By leveraging an adaptive event‐triggered delayed impulsive control strategy, we derive sufficient stability conditions for the network. The proposed approach not only addresses parameter uncertainties but also eliminates the Zeno behavior. Numerical simulations demonstrate the effectiveness of the control methodology.

International Journal of Robust and Nonlinear Control
Huaibei Normal University (CN)
University Natural Science Research Project of Anhui Province
Reduced inequalities
Openalex Percentile: Top 7%
Neural Networks Stability and Synchronization
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