Robust adaptive distributed dynamic surface consensus tracking control for nonlinear multi-agent systems with dynamic uncertainties

This paper proposes an adaptive distributed consensus tracking control approach for uncertain nonlinear multi-agent systems in pure-feedback form under a directed topology where each follower is dominated by dynamic uncertainties and unmeasured states. Radial basis function neural networks (RBFNNs) are employed to compensate the unknown nonlinear functions obtained by recursive design procedure for followers. The distributed dynamic surface controllers are able to eliminate the condition in which the approximation error of the traditional neural networks is bounded. By introducing an available dynamic signal and two smooth scalar functions, the obstacle caused by unmodeled dynamics is conquered. The main advantage of the proposed method is that for M pure-feedback nonlinear followers, only one learning parameter needs to be updated online. It is also shown that the proposed consensus controller can guarantee cooperatively semi-global uniform ultimate boundedness (CSUUB) of all the signals, and the consensus errors converge to an adjustable neighborhood of the origin.