Satellite Attitude Identification and Prediction Based on Neural Network Compensation  

作　　者：Zibin Sun Jules Simo Shengping Gong 

机构地区：[1]School of Aerospace Engineering,Tsinghua University,Beijing 100084,China [2]School of Engineering,University of Central Lancashire,Preston PR11XJ,UK [3]School of Astronuatics,Beihang University,Beijing 102206,China

出　　处：《Space(Science & Technology)》2023年第1期71-79,共9页空间科学与技术（英文）

基　　金：supported by the National Natural Science Foundation of China(no.11822205 and 11772167).

摘　　要：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.

关 键 词：PREDICTION NETWORK ALGORITHM 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]