https://doi.org/10.1140/epjb/s10051-024-00827-9
Regular Article - Statistical and Nonlinear Physics
A data-driven model-free adaptive pinning synchronization control study for complex networks
1
School of Computer and Engineering, Shenyang JianZhu University, No. 25, Hunnan Middle Road, 110168, Shenyang, Liaoning, China
2
Department of Sciences, Shenyang JianZhu University, No. 25, Hunnan Middle Road, 110168, Shenyang, Liaoning, China
Received:
30
June
2024
Accepted:
18
November
2024
Published online:
11
December
2024
This paper explores the problem of synchronous control of discrete complex network dynamics models. In view of the challenges such as the difficulty of modeling complex networks, the complexity of network structure and the difficulty of controller design, this paper proposes an improved model-free adaptive pinning control method. First, a method of entropy of the betweenness centrality and node strength is proposed to select the key nodes, construct the augmentation and generalization error system, and design the control strategy based on the node input and output data. Second, the synchronous stability is analyzed theoretically and the controller parameters are optimized by firefly optimization algorithm in order to overcome the parameter tuning difficulties. Finally, the effectiveness of the proposed pinning node selection strategy in this paper is verified by simulation, and it is verified by simulation experiments of BA scale-free network and ER stochastic network that the pinning control method in this paper only needs to control a few key nodes in the network to realize the synchronous state of the whole network. The method of this paper provides a new idea for synchronous control of complex networks.
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© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2024
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
