Input–Output Finite-Time Bipartite Synchronization for Multiweighted Complex Dynamical Networks Under Dynamic Hybrid Triggering Mechanism

Abstract The problem of input–output finite-time (IO-FT) bipartite synchronization for a class of nonlinear multiweighted complex dynamical networks (CDNs) in the presence of multiple coupling delays, external disturbances, and deception attacks is explored in this study. To be precise, the limited communication resources have been mitigated with the aid of undertaken hybrid triggered strategy, which reduces the unwanted network transmission and simultaneously improves the system's performance. Specifically, in the hybrid-trigger scheme, a Bernoulli distributed random variable has been employed to switch between the two communication channels. Moreover, the event-triggered scheme involving the dynamic trigger conditions is incorporated in the sensor-to-controller, which reduces the number of triggers compared to static event-triggered strategy. Further, the adequate conditions are derived in terms of linear matrix inequalities by constructing a Lyapunov–Krasovskii functional candidate. In light of this, the required parameters involved in triggering and the gain matrix are acquired by solving the developed linear matrix inequalities. Eventually, the reliability of the developed approach is verified via the illustration of two numerical examples, including the Chua's circuit with simulation verifications.

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Publication Details

Journal
Journal of Computational and Nonlinear Dynamics
Published
2024-08-05
DOI
https://doi.org/10.1115/1.4066121
Citations
2
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.70
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article

Input–Output Finite-Time Bipartite Synchronization for Multiweighted Complex Dynamical Networks Under Dynamic Hybrid Triggering Mechanism

S. Priyanka, Nallamuthu Birundha Devi, Ramalingam Sakthivel, O.M. Kwon
2 citations
Journal of Computational and Nonlinear Dynamics
Neural Networks Stability and Synchronization
0.70
article

Input–Output Finite-Time Bipartite Synchronization for Multiweighted Complex Dynamical Networks Under Dynamic Hybrid Triggering Mechanism

S. Priyanka, Nallamuthu Birundha Devi, Ramalingam Sakthivel, O.M. Kwon
article en
2 citations

Abstract

Abstract The problem of input–output finite-time (IO-FT) bipartite synchronization for a class of nonlinear multiweighted complex dynamical networks (CDNs) in the presence of multiple coupling delays, external disturbances, and deception attacks is explored in this study. To be precise, the limited communication resources have been mitigated with the aid of undertaken hybrid triggered strategy, which reduces the unwanted network transmission and simultaneously improves the system's performance. Specifically, in the hybrid-trigger scheme, a Bernoulli distributed random variable has been employed to switch between the two communication channels. Moreover, the event-triggered scheme involving the dynamic trigger conditions is incorporated in the sensor-to-controller, which reduces the number of triggers compared to static event-triggered strategy. Further, the adequate conditions are derived in terms of linear matrix inequalities by constructing a Lyapunov–Krasovskii functional candidate. In light of this, the required parameters involved in triggering and the gain matrix are acquired by solving the developed linear matrix inequalities. Eventually, the reliability of the developed approach is verified via the illustration of two numerical examples, including the Chua's circuit with simulation verifications.

Journal of Computational and Nonlinear DynamicsVol. 19(11)
Bharathiar University (IN), Chungbuk National University (KR), Karpagam Academy of Higher Education (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 29%
Neural Networks Stability and Synchronization
0.70
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Input–Output Finite-Time Bipartite Synchronization for Multiweighted Complex Dynamical Networks Under Dynamic Hybrid Triggering Mechanism — S. Priyanka, Nallamuthu Birundha Devi, et al. · Journal of Computational and Nonlinear Dynamics (2024) | TGRS Research Map | TGRS