太阳翼机构模拟微重力展开过程预测及不确定性量化  

Forecasting and Uncertainty Quantification of Simulated Solar Wing Deployment Process under Microgravity Conditions

作　　者：陶言和 郭勤涛[1] 周瑾[1] 马嘉倩 刘晓飞 陈瑞启 TAO Yanhe;GUO Qintao;ZHOU Jin;MA Jiaqian;LIU Xiaofei;CHEN Ruiqi(College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;Shanghai Institute of Satellite Equipment,Shanghai 200240,China)

机构地区：[1]南京航空航天大学机电学院,南京210016 [2]上海卫星装备研究所,上海200240

出　　处：《宇航学报》2024年第9期1498-1508,共11页Journal of Astronautics

基　　金：国家自然科学基金(U23B20105)。

摘　　要：在地面微重力环境中模拟太阳翼机构展开实验,为发现潜在问题并实现精确重力卸载调控,提出一种利用高保真刚柔耦合模型生成数据集训练神经网络,再对机构展开过程位姿进行预测及不确定性量化的方法。首先,概率盒法表征观测数据混合不确定性,采用变分贝叶斯-蒙特卡洛法对太阳翼刚柔耦合模型进行不确定性参数修正,建立其高保真刚柔耦合模型。然后,采用高保真模型生成数据集训练测试NAR神经网络,生成预测误差集,并采用误差集训练NARX网络。最后,用NAR网络预测太阳翼展开过程位姿参数,再将预测值输入NARX网络,给出预测值的不确定性量化区间。仿真和实验算例表明,所提方法预测精度高、不确定性量化合理、求解速度快,证明了其有效性和鲁棒性。In the simulated deployment test of the space solar wing mechanism within a ground micro-gravity environment,a methodology is proposed to employ a high-fidelity rigid-flexible coupled model for generating datasets for training neural networks.The objective is to predict the position and attitude of the mechanism to achieve more precise gravity unloading control and identify potential deviations or problems for risk mitigation.The approach entails characterizing the hybrid uncertainty of the observation data by using the probability box(p-box)method and leveraging the variational Bayesian Monte Carlo(VBMC)to establish the high-fidelity rigid-flexible coupling dynamic model through updating the uncertainty parameters.Subsequently,the updated model is utilized to generate the datasets for training and testing the nonlinear auto-regressive(NAR)neural network.Then,the forecasting error set is produced and utilized to train the nonlinear auto-regressive with exogeneous inputs(NARX)neural network.The NAR network is used to predict the attitude parameters of the solar wing deployment process.Then,the predicted values are input into the NARX network to obtain the uncertainty quantification(UQ)interval of the predicted values.Numerical and experimental examples demonstrate that the proposed method possesses high prediction accuracy,reasonable UQ,and fast solution speed,thereby verifying the effectiveness and robustness of the proposed method.

关 键 词：太阳翼 贝叶斯修正 不确定性量化 高保真模型 神经网络 

分 类 号：V41[航空宇航科学与技术—航空宇航推进理论与工程]