Synchronization control of partial-state-based neural networks: Event-triggered impulsive control with distributed actuation delay

Zhengli Liu, Mengzhuo Luo, Jun Cheng and Kaibo Shi

Physica A: Statistical Mechanics and its Applications, 2025, vol. 657, issue C

Abstract: Within the framework of event-triggered impulsive control (ETIC), this paper delves into the synchronization of partial-state-based neural networks, meticulously considering the distributed actuation delay inherent to ETIC. By seamlessly integrating the Lyapunov-Razumikhin (L-R) technique with a dimension extension method, we articulate sufficient conditions for achieving synchronization in partially measurable states through linear matrix inequalities (LMIs), all while effectively circumventing Zeno behavior. Furthermore, an innovative partial-state-based event-triggered mechanism (ETC) is conceived to address network challenges arising from distributed actuation delay, incorporating a forced triggering strategy to ensure synchronization. Additionally, at each impulse instant, unmeasurable states can be elegantly complemented by their measurable counterparts through the design of an appropriate impulse gain matrix, without imposing any constraints on the number of unmeasurable states. Ultimately, two illustrative examples serve to underscore the efficacy of our principal findings.

Keywords: Distributed actuation delay; Partial states; Event-triggered impulsive control; Synchronization; Neural networks (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:657:y:2025:i:c:s0378437124007374

DOI: 10.1016/j.physa.2024.130228

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