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  • 标题:A “Safe Kernel” Approach for Resilient Multi-Dimensional Consensus
  • 本地全文:下载
  • 作者:Jiaqi Yan ; Yilin Mo ; Xiuxian Li
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2020
  • 卷号:53
  • 期号:2
  • 页码:2507-2512
  • DOI:10.1016/j.ifacol.2020.12.224
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThis paper considers the resilient multi-dimensional consensus problem in networked systems, where some of the agents might be malicious (or faulty). We propose a multidimensional consensus algorithm, where at each time step each healthy agent computes a “safe kernel” based on the information from its neighbors, and modifies its own state towards a point inside the kernel. Assuming that the number of malicious agents is locally (or globally) upper bounded, sufficient conditions on the network topology are presented to guarantee that the benign agents exponentially reach an agreement within the convex hull of their initial states, regardless of the actions of the misbehaving ones. It is also revealed that the graph connectivity and robustness required to achieve the resilient consensus increases linearly with respect to the dimension of the agents’ state, indicating the existence of a trade-off between the low communication cost and system security. Numerical examples are provided in the end to validate the theoretical results.
  • 关键词:KeywordsAverage consensusResilient algorithmMultidimensional systems
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