On convergence rate for multi-agent consensus: A community detection algorithm
Baofeng Zhang, Debao Chang, Zhanjie Li, Dan Ma · 2017
This paper considers the problem of improving the convergence rate for multi-agent consensus via a community detection algorithm used to divide the single layer topology into layers of connected subgraphs. This divided topological graph maintains the constraints of the original topological graph. Combining with the consensus protocol, community detection algorithm improves the convergence rate of multi-agent consensus effectively, and we propose a grouping improvement algorithm and a hierarchical grouping improvement algorithm to verify its feasibility.