Finite Time Stabilization of Dynamical Networks Under Pinning Event-Triggered Control

The finite time stabilization problem of dynamical directed networks under pinning adaptive event-triggered control was addressed. Unlike the existing results as for finite time stabilization with event-triggered protocol, in view of the difficulties of the control cost and the large node number, a novel pinning event-triggered protocol was designed. Given its high dimension, the analysis of the finite time stabilization under the pinning event-triggered control for dynamical networks is challenging. Based on the Lyapunov stability theory and the appropriately designed protocol, sufficient conditions were derived to guarantee the finite time stabilization. Finally, an example was given to demonstrate the effectiveness of the theoretical results.

Authors

Publication Details

Journal
应用数学和力学
Published
2025-01-01
DOI
https://doi.org/10.21656/1000-0887.450072
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00
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article

Finite Time Stabilization of Dynamical Networks Under Pinning Event-Triggered Control

Zhao Wei, Fengli Ren
应用数学和力学
Neural Networks Stability and Synchronization
article

Finite Time Stabilization of Dynamical Networks Under Pinning Event-Triggered Control

Zhao Wei, Fengli Ren
article en

Abstract

The finite time stabilization problem of dynamical directed networks under pinning adaptive event-triggered control was addressed. Unlike the existing results as for finite time stabilization with event-triggered protocol, in view of the difficulties of the control cost and the large node number, a novel pinning event-triggered protocol was designed. Given its high dimension, the analysis of the finite time stabilization under the pinning event-triggered control for dynamical networks is challenging. Based on the Lyapunov stability theory and the appropriately designed protocol, sufficient conditions were derived to guarantee the finite time stabilization. Finally, an example was given to demonstrate the effectiveness of the theoretical results.

应用数学和力学Vol. 46(3)
Openalex Percentile: Top 94%
Neural Networks Stability and Synchronization
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Finite Time Stabilization of Dynamical Networks Under Pinning Event-Triggered Control — Zhao Wei, Fengli Ren · 应用数学和力学 (2025) | TGRS Research Map | TGRS