Trajectory Tracking Method of Large Elliptical Orbit Aerospace Vehicle Based on Long Short-Term Memory Network

The large elliptical orbit aerospace vehicle has complex motion characteristics and harsh flight environment. When tracking its trajectory, it is prone to the loss of measurement data caused by the interference of the detector. The traditional tracking algorithm can not accurately describe its maneuvering characteristics, and the tracking accuracy and robustness are poor. In order to solve this problem, this paper proposes a trajectory tracking method for large elliptical orbit aerospace vehicles based on long short-term memory network (LSTM). Aiming at the situation that the acceleration and turning rate of the large elliptical orbit aerospace vehicle are not constant, the tangential acceleration and three-dimensional turning rate are modeled, and a trajectory tracking model with three-dimensional variable turning rate is proposed. Aiming at the problem of intermittent measurement, a continuous tracking algorithm based on LSTM is proposed, which uses the memory characteristics of LSTM to predict the measurement data reasonably. Finally, the simulation verification is carried out in the equatorial plane orbit and the polar plane orbit. The results show that the trajectory tracking algorithm of the large elliptical aerospace vehicle based on LSTM realizes the accurate tracking of the large elliptical orbit of the aerospace vehicle under maneuvering conditions. In the case of intermittent measurement, the LSTM network is used to predict the measurement data online, which can achieve continuous trajectory tracking. Compared with the traditional algorithm, the tracking accuracy and system robustness have been significantly improved.