Iterative learning consensus control for multi-agent systems under independent position and velocity topologies
Jiaxian Wang, Zhang Li, Junmin Li, Jinsha Li · 2016
In this paper, we propose an iterative learning control (ILC) for the consensus of multi-agent systems in a very general framework where the position and velocity interactions among agents are modeled by independent graphs. A class of distributed consensus protocol is constructed as iterative learning algorithm. Assuming that the leader node is globally reachable under independent position and velocity topologies, a sufficient condition to guarantee the multi-agent consensus is derived for the directed communication topologies. Further, the proposed scheme is also extended to achieve the formation control for the multi-agent systems. Simulation results are finally presented to illustrate the performance and effectiveness of our iterative learning protocol.