Finite-time Synchronization of Complex Networks with Time-varying Delays via Periodically Intermittent Control

Finite-time synchronization between two dynamical systems with coupled and internal time-varying delays and nonlinear coupling functions is examined in this article. The effectiveness of the periodically intermittent controller design has been demonstrated. The synchronization problem is analyzed using a novel Lyapunov function. The theory of finite-time stability is instrumental in enabling drive-response systems to achieve rapid synchronization. In certain circumstances, the system is capable of attaining time-limited synchronization, which is a crucial aspect in the domain of nonlinear dynamics and chaos control. The stability time is easy to calculate, which adds to the stability of the control system. The controller is simple to use and helps reduce control expenses.

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Published
2024-11-22
DOI
https://doi.org/10.1145/3722237.3722385
Primary Topic
Neural Networks Stability and Synchronization
Type
article
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article

Finite-time Synchronization of Complex Networks with Time-varying Delays via Periodically Intermittent Control

Yali Tai
Neural Networks Stability and Synchronization
article

Finite-time Synchronization of Complex Networks with Time-varying Delays via Periodically Intermittent Control

Yali Tai
article en

Abstract

Finite-time synchronization between two dynamical systems with coupled and internal time-varying delays and nonlinear coupling functions is examined in this article. The effectiveness of the periodically intermittent controller design has been demonstrated. The synchronization problem is analyzed using a novel Lyapunov function. The theory of finite-time stability is instrumental in enabling drive-response systems to achieve rapid synchronization. In certain circumstances, the system is capable of attaining time-limited synchronization, which is a crucial aspect in the domain of nonlinear dynamics and chaos control. The stability time is easy to calculate, which adds to the stability of the control system. The controller is simple to use and helps reduce control expenses.

Jimei University (CN)
Openalex Percentile: Top 65%
Neural Networks Stability and Synchronization
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