Bipartite Consensus via Iterative Learning Control for Heterogeneous Singular Nonlinear Delayed Multi-agent Systems

This article incorporates the technique of iterative learning control (ILC) to deal with the bipartite consensus problems for heterogeneous singular nonlinear delayed multi-agent systems. In order to achieve high-precision bipartite consensus over a finite-time interval. The distributed ILC updating law is constructed using bipartite error based on the antagonistic communication topology. Then, by the method of compression mapping, the sufficient conditions for bipartite error monotonic convergence are obtained. Under the action of distributed ILC updating law and initial value learning protocol, the bipartite errors can convergent to zero as the iteration index approaches to infinity. The effectiveness of the proposed distributed ILC updating law is validated by numerical simulation results.