Finite time asymmetric bipartite consensus for multi-agent systems based on iterative learning control

In this paper, the finite-time asymmetric bipartite consensus problem of multi-agent systems with signed digraph is considered. Firstly, an asymmetric index is introduced to describe a desired output relationship of the agents in terms of quantity. Based on the information of the agent's communication and the index, a novel iterative learning control protocol is proposed. By establishing the input and output errors relationship of the agents along the iteration domain, a sufficient condition is derived and a defined leaderless tracking error is proved to be asymptotically convergence as iteration increases. The result shows that the proposed design can ensure the agents achieve the asymmetric bipartite consensus goal in the finite-time. Moreover, the proposed design is also extended to deal with the problem of the multi-agent systems with heterogeneous dynamics. Finally, numerical simulation examples verify the effectiveness of the proposed protocol.