Two-Dimensional Space Turntable Pitch Axis Trajectory Prediction Method Based on Sun Vector and CNN-LSTM Model

Abstract

A two-dimensional space turntable system has been used to ensure that the Solar X-ray and Extreme Ultraviolet Imager (X-EUVI) can track the Sun stably, and the prediction of the two-dimensional turntable trajectory is an important part of payload health management. Different from the dynamic model using traditional trajectory prediction, we propose a new method for predicting the pitch axis trajectory of the turntable based on the sun vector and a deep learning CNN-LSTM model. First, the ideal solar position of the pitch axis was calculated using the sun vector. Then, the ideal solar position was combined with the running turntable pitch axis motor speed, current, and solar position error signal as the CNN-LSTM model input data. The model parameters were trained and adjusted through test data simulation using Fengyun-3E satellite orbit data. Finally, the next position of the pitch axis was predicted. The test results showed that in the sun vector and CNN-LSTM model, the RMSE value was 0.623 and the MSE value was 0.388. It was better than the LSTM model or CNN model alone and could accurately predict the pitch axis position.

Keywords

• two-dimensional turntable
• pitch axis trajectory
• sun vector
• CNN-LSTM model
• deep learning