Adaptive neural finite-time containment control for nonlower triangular nonlinear multi-agent systems with dynamics uncertainties

Abstract

This paper considers the finite-time containment control problem for nonstrict-feedback nonlinear multi-agent systems (MASs) in the presence of unmodeled dynamics. A new variable separation approach is developed to overcome the difficulty of unknown functions with nonstrict-feedback structure in the backstepping design process. The second-order tracking differentiator is extended to the nonstrict-feedback nonlinear MASs for avoiding the problem of “explosion of complexity”. Neural networks are used to approximate the unknown nonlinear functions. With introducing a dynamic signal, the difficulty from the unmodeled dynamics is successfully solved. And, by introducing a finite-time stability criterion, an new adaptive finite-time containment control scheme is designed, which ensures all signals are bounded. Moreover, the output signals of all followers converge to a convex hull spanned by the outputs of the leaders. Finally, a simulation example illustrates the effectiveness of the developed control scheme.