Output Cluster Synchronization of Heterogeneous Networks via Pinning Control

This paper studies an output cluster synchronization problem of heterogeneous networks. Based on the internal model principle, heterogeneous networks are transformed into networks with a heterogeneous layer and a virtual nonidentical layer. By means of pinning control, a novel output cluster synchronization strategy is proposed. Three algebraic and graphic conditions are derived to specify control parameters. It is shown that the outputs of agents in heterogeneous networks are synchronized according to the clusters as long as fewer agents are pinned. Compared with the existing results, cluster tracking can be further achieved and control parameters can be always determined no matter how the magnitudes of coupling strengths among agents vary. Application cases on heading cluster synchronization of heterogeneous ships are provided to verify the effectiveness of theoretical results.

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Published
2024-10-25
DOI
https://doi.org/10.1109/rcae62637.2024.10833988
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00

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article

Output Cluster Synchronization of Heterogeneous Networks via Pinning Control

Lu Sun
Neural Networks Stability and Synchronization
article

Output Cluster Synchronization of Heterogeneous Networks via Pinning Control

Lu Sun
article en

Abstract

This paper studies an output cluster synchronization problem of heterogeneous networks. Based on the internal model principle, heterogeneous networks are transformed into networks with a heterogeneous layer and a virtual nonidentical layer. By means of pinning control, a novel output cluster synchronization strategy is proposed. Three algebraic and graphic conditions are derived to specify control parameters. It is shown that the outputs of agents in heterogeneous networks are synchronized according to the clusters as long as fewer agents are pinned. Compared with the existing results, cluster tracking can be further achieved and control parameters can be always determined no matter how the magnitudes of coupling strengths among agents vary. Application cases on heading cluster synchronization of heterogeneous ships are provided to verify the effectiveness of theoretical results.

Nanjing Normal University (CN)
Nanjing Normal University
Openalex Percentile: Top 70%
Neural Networks Stability and Synchronization
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