On iterative learning algorithms for the formation control of nonlinear multi-agent systems

This paper deals with formation control problems for multi-agent systems with nonlinear dynamics and switching network topologies. Using the nearest neighbor knowledge, a distributed algorithm is constructed by employing the iterative learning control approach. Sufficient conditions are given to obtain the desired relative formations of agents, which benefits from the strict positiveness of products of stochastic matrices. It is shown that the derived results can effectively work, although the network topologies dynamically change along both time and iteration axes and the corresponding directed graphs may not have spanning trees. Such result is also illustrated via numerical simulations.