Satellite Attitude Identification and Prediction Based on Neural Network Compensation
Zibin Sun, Jules Simo, Shengping Gong
Abstract
This paper proposed a new attitude determination method for low-orbit spacecraft. The attitude prediction accuracy is greatly improved by adding the unmodeled environmental torque to the dynamic equation. Specifically, the environmental torque extraction algorithm based on extended Kalman filter and series extended state observer is introduced, and the unmodeled part of dynamic is identified through the inverse dynamic model. Then, the collected data are analyzed and trained by a backpropagation neural network, resulting in an attitude-torque mapping network with compensation ability. The simulation results show that the proposed feedback attitude prediction algorithm can outperform standard methods and provide a high accurate picture of prediction and reliability with discontinuous measurement.