Distributed adaptive neural containment control for multi-UAV systems with nonlinear uncertainties under a directed graph

We investigate the distributed containment control problem for multiple unmanned aerial vehicles (UAVs) systems with nonlinear uncertainties and bounded disturbances under a directed graph, where the leaders are neighbors of only a subset of the followers. For each follower, there exists at least one leader that has a directed path to the follower. It is assumed that aerodynamic characteristics of UAVs are nonlinear uncertainties, and the outputs of leaders are timevarying. A distributed containment control protocol combined with backstepping design method is proposed by using neighbors'information, so that the states of the followers will converge to the convex hull spanned by the dynamic leaders. The function approximation technique using neural networks is employed to compensate unknown nonlinear terms induced from the controller design procedure. By Lyapunov stability theorem, it is shown that the containment control errors will converge to an expected neighborhood of the origin with an arbitrary convergence rate. Simulation examples are presented to illustrate the effectiveness of the proposed control algorithm.