The docking controller for autonomous aerial refueling (AAR) is intractable considering the high precision requirement and the complex disturbances of multiple environment flows. To solve the problems in the docking phase of AAR, such as the uncertainties of the aerodynamic parameters of receiver aircraft and the disturbances acting on the receiver aircraft, an adaptive dynamic surface control (ADSC) scheme based on radical basis function neural network (RBF-NN) is presented in this paper. Firstly, a nonlinear model of longitudinal dynamics of the receiver aircraft relative to the tanker aircraft is established, which incorporates the tanker vortex term. Secondly, a nonlinear strict-feedback form is introduced to design an adaptive dynamic surface controller with RBF-NN. Thirdly, the upper bounds of the ''total disturbances'' are estimated with the adaptive law, and the uncertain aerodynamic parameters of receiver aircraft are estimated with RBF-NN. It is proved that the proposed controller can guarantee the uniform boundedness of all the signals in the closed-loop system using Lyapunov theory. Finally, simulation results demonstrate the effectiveness of the proposed controller for the docking control of AAR. INDEX TERMS Autonomous aerial refueling, docking controller, adaptive dynamic surface control, RBF neural network.