For a fixed-wing UAV system with limited input under disturbance,a reinforcement learning controller based on Actor-Critic is proposed.Firstly,the longitudinal model of fixed-wing UAV under disturbance is established,and the performance function with integral term is designed to evaluate the flight state and system performance of UAV.Then,an Actor-Critic structure based on reinforcement learning is adopted.The Actor is used to solve the control law of minimization strategy performance function,and the Critic is used to approximate the nonlinear performance function.Finally,Disturbance network is used to apply disturbance to the UAV to test the UAV's capability of operating under disturbance,and the momentum gradient descent algorithm is used to improve the learning speed and stability of the neural network to enhance the control performance of the UAV controller.The simulation results show that,compared with the traditional control methods,the proposed reinforcement learning controller can realize the control faster and more stably under the condition of limited input.