Adaptive neural control for consensus of multiple UAVs with heterogeneous matching uncertainties under a directed graph

In this paper, the distributed consensus problem is investigated for multiple UAVs systems with nonlinear uncertainty and bounded disturbances under a directed graph. It is assumed that aerodynamic characteristics of UAV are nonlinear uncertainties, and the output of the leader is time-varying. UAV dynamics model can be divided into two parts, including completely known and the unknown nonlinear function. An adaptive neural controller is designed with backstepping design method. Neural networks are used as the function approximation technique to compensate unknown nonlinear terms. From the Lyapunov function, it is shown that the consensus error can converge to any small neighborhood of the origin with an arbitrary convergence rate. Simulations verify the effectiveness of the proposed protocol.