Global Bipartite Exact Consensus of Unknown Nonlinear Multi-Agent Systems With Switching Topologies: Iterative Learning Approach

In this paper, a novel global adaptive iterative learning bipartite consensus control algorithm is proposed for a class of uncertain nonlinear second-order multi-agent systems with time-varying disturbances, in which the communication topology among all agents switches relying both on time and iteration. By signal replacement technique, we apply the neural networks to describe the unknown nonlinear dynamics of each agent, overcoming the defect of semi-global results in existing works and obtaining the global results. Unquestionably, our new control algorithm ensures that all agents could achieve global bipartite exact consensus on the finite time interval with the switching topologies. And this result is also extended to the bipartite exact formation control problem. Finally, the performance of our control scheme is demonstrated by three simulation examples.