Group Information Based Nonlinear Consensus for Multi-Agent Systems

This paper continues our previous hierarchical consensus work by considering a nonlinear case. All agents are partitioned into a set of groups, each of which contains a value called group information, representing a convex combination of all agents' states inside. The control input for each agent consists of two parts, i.e., agent state inside its associated group and its group information. When the received group information is a nonlinear transformation, it is shown that the consensus can be achieved under the proposed scheme in both discrete time and continuous time. Finally, the numerical simulations are performed to validate the theoretical results.