Applications of real-time recurrent neural network based on extended Kalman filter in unsteady aerodynamics modeling

According to the characteristics of extended Kalman filter (EKF) and real-time recurrent learning (RTRL) algorithm, the EKF-RTRL algorithm is applied to recurrent neural network (RNN). Based on the large amplitude harmonic oscillation and ramp motion experimental data of an aircraft model with yawing, rolling, and yaw-roll motion, the application of the RNN in modeling unsteady aerodynamics are studied. The results show that, this recurrent neural network has advantages of high computing precision and fast convergence feature. A good agreement is observed between the simulation results and testing results. It indicates that the neural network modeling method is efficient.