二部图
计算机科学
趋同(经济学)
节点(物理)
协议(科学)
国家(计算机科学)
非线性系统
多智能体系统
算法
理论计算机科学
人工智能
图形
工程类
量子力学
经济增长
结构工程
医学
物理
病理
经济
替代医学
作者
Fan Tao Fang,Hongyong Yang,Fei Liu,Shuochen Wang
标识
DOI:10.1177/01423312241291288
摘要
This article addresses the finite-time asymmetric bipartite consensus problem for multi-agent systems (MASs) with privacy-preserving. Firstly, a novel algorithm for MASs called the sub-state sharing privacy-preserving algorithm (SSPPA) is proposed, which prevents the leakage of privacy information by hiding the initial state of agents within the system. SSPPA decomposes the initial state of each agent in the system, and then transmits the decomposed sub-states. After receiving the sub-state information from neighboring nodes, the destination node recombines them to generate a new hidden value. Secondly, a nonlinear control protocol with asymmetric influence coefficient is constructed, which can reach finite- and fixed-time asymmetric bipartite consensus. Then, through the analysis of the performance of the proposed algorithm, it is found that the algorithm will not affect the ultimate convergence of the system. Finally, simulation experiments are conducted to verify the effectiveness of both the privacy-preserving algorithm and control protocol.
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