Self-Triggered Prescribed-Time Impulsive Control for Nonlinear Systems

This article presents a new design of self-triggered impulsive control (STIC) for prescribed-time stability (PTS) of nonlinear systems. A prescribed-time convergent function is proposed to determine the impulsive control strength together with the information of the impulsive interval. Different from previous impulsive control strategies, the impulsive control with designable strength is implemented under a non-Zeno self-triggering mechanism (STM) with enforced bounded execution times and the triggering times established by a comparison system. The involvement of the upper bound of the impulsive interval in both control strength and STM not only renders the closed-loop prescribed-time stable but also ensures decreasing impulsive strengths as the time approaches the prescribed time. The validity of the proposed STIC scheme is verified by the application to prescribed-time synchronization of reaction-diffusion neural networks (RDNNs) and numerical examples.

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Publication Details

Journal
IEEE Transactions on Cybernetics
Published
2025-11-25
DOI
https://doi.org/10.1109/tcyb.2025.3633794
Primary Topic
Neural Networks Stability and Synchronization
Type
article
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article

Self-Triggered Prescribed-Time Impulsive Control for Nonlinear Systems

Min Xue, James Lam, Tengda Wei, Xiaodi Li
IEEE Transactions on Cybernetics
Neural Networks Stability and Synchronization
article

Self-Triggered Prescribed-Time Impulsive Control for Nonlinear Systems

Min Xue, James Lam, Tengda Wei, Xiaodi Li
article en

Abstract

This article presents a new design of self-triggered impulsive control (STIC) for prescribed-time stability (PTS) of nonlinear systems. A prescribed-time convergent function is proposed to determine the impulsive control strength together with the information of the impulsive interval. Different from previous impulsive control strategies, the impulsive control with designable strength is implemented under a non-Zeno self-triggering mechanism (STM) with enforced bounded execution times and the triggering times established by a comparison system. The involvement of the upper bound of the impulsive interval in both control strength and STM not only renders the closed-loop prescribed-time stable but also ensures decreasing impulsive strengths as the time approaches the prescribed time. The validity of the proposed STIC scheme is verified by the application to prescribed-time synchronization of reaction-diffusion neural networks (RDNNs) and numerical examples.

IEEE Transactions on CyberneticsVol. 56(4)
City University of Hong Kong (HK), Shandong Normal University (CN), University of Hong Kong (HK)
Openalex Percentile: Top 59%
Neural Networks Stability and Synchronization
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