There are a series of complex nonlinear problems such as friction torque,inertia moment and load disturbances in the AC servo system of a vehicle-mounted stable platform driven by a Permanent Magnet Synchronous Motor (PMSM).In view of the strong anti-interference ability of Active Disturbance Rejection Control (ADRC) and the strong self-learning ability of BP Neural Network (BPNN),an Active Disturbance Rejection Controller improved by BPNN (BPNN-ADRC) is designed.In order to simplify the process of parameter tuning of ADRC,which is time-consuming and laborious,the BPNN is used to tune the important parameters of the ADRC controller online.In order to overcome the defects of slow convergence of the BPNN and its tendency of falling into local optimum,the genetic algorithm is introduced to perform online optimization of the weights of the initially connected nodes,so as to further improve the control accuracy of the system.The results of simulation experiment show that the control strategy can effectively improve the anti-interference ability of the system,which is a feasible scheme for improving the control performance of the servo system of the vehicle-mounted stable platform.