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Secure impulsive tracking of multi-agent systems with directed hypergraph topologies against hybrid deception attacks.
Yang, Zonglin; Ling, Guang; Ge, Ming-Feng.
Afiliación
  • Yang Z; School of Mathematics and Statistics, Wuhan University of Technology, Wuhan 430070, China. Electronic address: zonglin1211@whut.edu.cn.
  • Ling G; School of Mathematics and Statistics, Wuhan University of Technology, Wuhan 430070, China. Electronic address: guangling@whut.edu.cn.
  • Ge MF; School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China. Electronic address: gemf@cug.edu.cn.
Neural Netw ; 180: 106691, 2024 Sep 02.
Article en En | MEDLINE | ID: mdl-39255635
ABSTRACT
This research delves into the challenges of achieving secure consensus tracking within multi-agent systems characterized by directed hypergraph topologies, in the face of hybrid deception attacks. The hybrid discrete and continuous deception attacks are targeted at the controller communication channels and the hyperedges, respectively. To overcome these threats, an impulsive control mechanism based on hypergraph theory are introduced, and sufficient conditions are established, under which consensus can be maintained in a mean-square bounded sense, supported by rigorous mathematical proofs. Furthermore, the investigation quantifies the relationship between the mean-square bounded consensus of the multi-agent system and the intensity of the deception attacks, delineating a specific range for this error metric. The robustness and effectiveness of the proposed control method are verified through comprehensive simulation experiments, demonstrating its applicability in varied scenarios influenced by these sophisticated attacks. This study underscores the potential of hypergraph-based strategies in enhancing system resilience against complex hybrid attacks.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos