An adaptive radial basis function（RBF）neural network control strategy was proposed for the trajectory tracking control of quadrotor aircraft with model uncertainty and unknown external disturbance.In this method, RBF neural network was used to online approximate and compensate the unknown nonlinear function in the system to reduce the dependence on the mathematical model and improve the anti-interference ability; combined with Lyapunov method, the adaptive law of on-line adjusting weights of neural network was derived to enhance its robustness; the stability of the controller was proved by using Lyapunov theory.The effectiveness and engineering application value of the method were verified through simulation and experiment. The results show that under the conditions of time-varying interference and parameter perturbation, the adjustment time of the proposed method is shortened by 1.1~2.1 s compared with ADRC control, and the average absolute error of trajectory tracking is reduced by 38.27%, the method has better robustness and anti-interference capability.