Neural-network-based distributed adaptive synchronization for nonlinear multi-agent systems in pure-feedback form

In this paper, the problem of distributed adaptive synchronization for unknown nonlinear multi-agent systems in pure-feedback form is studied under a directed graph. Neural networks are used to approximate the unknown nonlinear dynamics, and by incorporating the dynamic surface control (DSC) technique into backstepping design procedure, distributed adaptive consensus controllers are developed. A novel method is given for reducing the burden of networked communication. It is shown that the proposed distributed consensus controllers guarantee that all signals in the closed-loop system are cooperatively semi-globally uniformly ultimately bounded, and the consensus errors converge to a small neighborhood of the origin. Finally, a simulation example is given to show the effectiveness of the designed control scheme.