The parameter uncertainty and environmental interferences may result in unstable attitude of quadrotor aircraftsand the traditional PID control cant meet the control requirements of the quadrotors attitude stability and maneuverability.Aiming at the probleman Extended State Observer (ESO) neural network RBF PID controller is proposed.Firstlythe extension characteristics of ESO and nonlinear functions are used to estimate and compensate for disturbances to reduce system errors.Secondlythe ESOs estimated value of the system output is used as the input of the RBF neural networkto make the gradient information more accurate and better optimize the parameters of the incremental PID.Finallya Gaussian function is adopted as the excitation function of the neural networkand the model control parameters are adjusted by using the self-adaptability and self-learning ability of the RBF neural network.The Matlab simulation experiment shows that:in the unknown interference environmentthe ESOs RBF neural network PID controller can significantly improve the anti-interference ability of the system,and has a smaller overshoot and better robustness.