In this study, focuses on the non-linear Hammerstein model under external disturbance with white Gaussian noise based on the experimental input (motor velocities) and output (end effector position) data of the three axes gimbal system on the Unmanned Aerial Vehicle (UAV), which is autonomously moving for route tracking. The performance of UAV in reaching the target point on a planned route in sinusoidal form in avoiding obstacles, depends on the route tracking performance of the three axes gimbal system on the UAV. In intelligence activities such as exploration and surveillance, Hammerstein and Nonlinear AutoRegressive and Moving Average (NARMA) models of the gimbal system with great importance for UAV that real time image transmission and in the tasks of leaving payloads to targets with unknown coordinates with the least mistake are obtained. The parameters of the obtained models are estimated by Recursive Least Squares (RLS) algorithm and the model performances are compared.