Abstract
This paper presents a hierarchical structure to solve the consensus problem of multiagent systems. The new scheme divides the agents into several groups, with each group containing a value concerning all of the intragroup agents' states, which we call group information. For each single agent, it receives not only the agent information from its intragroup neighbors, but also the group information from its neighboring groups. It is then shown that global consensus can be achieved under the proposed scheme in both discrete time and continuous time. Moreover, a sufficient condition to achieve average consensus is provided. This hierarchical model can be well used in the PageRank algorithm to reduce the communication loads, and to reveal the attractors for Boolean networks by reducing the computational complexity.
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