To improve the stability of PID controller to the system and reduce the control error, an adaptive neural network PID controller is proposed. Firstly, PID controller is developed in discrete-time model to reduce the problems of controller design in continuous time. Then, an adaptive neural network is defined to adjust the control gain to minimize the tracking error of the UAV during the navigation mission. The important parameters of PID controller are adjusted by gradient descent method. In addition, Kalman filter is used to filter the measured values of sensors to improve the performance of on-line adaptive. The experimental results verify the effectiveness of the proposed controller. The integral of absolute error (IAE) is 2.576×103, and The integral multiplied time of absolute error (ITAE) is 5.152×105. Both indexes are one order of magnitude lower than the classical PID controller.