Stability Analysis of Stochastic Time‐Varying Systems Involving Unbounded Delay and Multiple Impulses

ABSTRACT This article mainly discusses th moment exponential stability for impulsive stochastic dynamical systems with unbounded delay. By utilizing the improved Lyapunov–Razumikhin approach together with the mode‐dependent average dwell time method, some novel criteria are obtained from the perspective of impulsive perturbation and impulsive control, which specifically provide the intrinsic connection among current state, unbounded delay and multiple impulses. Compared with some existing works, it should be highlighted that our results do not require the steady state behavior of both discrete system and continuous dynamical system. Finally, two numerical examples are presented to demonstrate the validity and the distinctiveness of derived results.

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Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2025-11-02
DOI
https://doi.org/10.1002/mma.70284
Primary Topic
Neural Networks Stability and Synchronization
Type
article
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article

Stability Analysis of Stochastic Time‐Varying Systems Involving Unbounded Delay and Multiple Impulses

Haofeng Xu, Quanxin Zhu, Ye Tan
Mathematical Methods in the Applied Sciences
Neural Networks Stability and Synchronization
article

Stability Analysis of Stochastic Time‐Varying Systems Involving Unbounded Delay and Multiple Impulses

Haofeng Xu, Quanxin Zhu, Ye Tan
article en

Abstract

ABSTRACT This article mainly discusses th moment exponential stability for impulsive stochastic dynamical systems with unbounded delay. By utilizing the improved Lyapunov–Razumikhin approach together with the mode‐dependent average dwell time method, some novel criteria are obtained from the perspective of impulsive perturbation and impulsive control, which specifically provide the intrinsic connection among current state, unbounded delay and multiple impulses. Compared with some existing works, it should be highlighted that our results do not require the steady state behavior of both discrete system and continuous dynamical system. Finally, two numerical examples are presented to demonstrate the validity and the distinctiveness of derived results.

Mathematical Methods in the Applied SciencesVol. 49(4)
Hunan Normal University (CN)
Openalex Percentile: Top 62%
Neural Networks Stability and Synchronization
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