Finite-Time Asymmetric Bipartite Consensus for Multi-Agent Systems Using Data-Driven Iterative Learning Control

A general finite-time bipartite consensus problem is studied for multi-agent systems with completely unknown nonlinearities. An asymmetric bipartite consensus task is defined by introducing a proportional-related coefficient and a relationship-related index, which arranges that the agents reach an agreement with proportional modulus and opposite signs. With the cooperative-antagonistic interactions, a model-free adaptive bipartite iterative learning consensus protocol is proposed for promoting the accuracy of the performance within a finite-time interval. By employing the matrix transformation and property of the nonnegative matrix, the iteratively asymptotic convergence of the error of the MAS is guaranteed under the structurally balanced digraph has an oriented spanning tree. This differs from MFAILC results that have been proven based on matrix norm and do not require strong connectivity of digraphs. Moreover, the bounds for elements in the estimation-related matrices are presented, followed by providing a graph correlated sufficient condition to guide selection of control parameters. The results further extend to the control of asymmetric bipartite consensus tracking. The simulation examples verify the effectiveness of the distributed learning control protocols.