LAUSR

This paper addresses the spherical formation tracking control problem of nonlinear vehicles under digraphs, where the dynamics of vehicle contains spatiotemporal uncertainties consisted of the mismatched and matched flowfields and model uncertainties. The novel cooperative adaptive flow estimates and adaptive neural networks for the reconstruction of model uncertainties are designed based on the neighbor information. A fully distributed formation tracking protocol is generated by dynamics surface and achieves the three-dimensional sphere landing task with the position restriction, the orbit tracking task and the lateral circular formation task without using any global information of the digraph. The stability of the closed-loop system is analyzed in the Lyapunov sense, and the spherical formation tracking errors converge to a small neighborhood of the origin. Simulation results show the feasibility and effectiveness of the proposed scheme.