| 宋健峰,祝连庆,于明鑫,宋言明,张 旭.基于神经网络的星敏支架指向倾角监测方法[J].电子测量与仪器学报,2022,36(11):1-8 |
| 基于神经网络的星敏支架指向倾角监测方法 |
| Star tracker bracket pointing inclination monitoringmethod based on neural network |
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| DOI： |
| 中文关键词: 星敏支架 形变监测 深度学习 倾角预测 |
| 英文关键词:star tracker bracket deformation monitoring deep learning inclination prediction |
| 基金项目:北京市自然科学基金(4202027)、国家自然科学基金(61801030，62003346)项目资助 |
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| Author | Institution |
| Song Jianfeng | 1. Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University |
| Zhu Lianqing | 1. Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing InformationScience & Technology University,2. Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science & Technology University,3. Beijing Key Laboratory of Optoelectronic Measurement
Technology, Beijing Information Science & Technology University |
| Yu Mingxin | 1. Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University，3. Beijing Key Laboratory of Optoelectronic Measurement
Technology, Beijing Information Science & Technology University |
| Song Yanming | 1. Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University |
| Zhang Xu | 1. Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University，2. Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science & Technology University |
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| 中文摘要: |
| 针对星敏支架热致变形导致其指向精度降低的问题,提出了一种基于神经网络的指向倾角监测方法。 首先,分析星敏
支架结构特征,搭建星敏支架指向倾角预测系统,采集星敏支架结构形变和倾角变化数据,并对实验数据进行预处理;其次,构
建深度神经网络模型,将星敏支架模型各测量点的应变信息作为输入变量,并使用 Adam 优化算法更新网络参数,经训练迭代
后得到指向倾角预测模型;然后针对传统深度神经网络收敛速度慢、容易产生局部最小值等局限性,使用遗传算法对深度神经
网络的超参数进行优化,以提升神经网络的训练速度;最后使用测试集数据对星敏支架指向倾角变化进行预测,分析该模型在
不同温度条件下对星敏支架指向倾角监测的准确率。 实验结果表明,优化后深度神经网络模型的指向倾角预测方法的平均误
差为 0. 20″,且倾角预测精度明显优于传统算法,证明利用深度学习方法实现星敏支架指向倾角监测具有可行性。 |
| 英文摘要: |
| Aiming at the problem that the pointing accuracy of star tracker bracket is reduced due to thermal deformation, a pointing
inclination monitoring method based on neural network is proposed. Firstly, the structural characteristics of the star tracker bracket are
analyzed, the pointing inclination prediction system of the star tracker bracket is built, the structural deformation and inclination change
data of the star tracker bracket are collected, and the experimental data are preprocessed. Secondly, the depth neural network model is
constructed, the strain information of each measurement point of the star tracker bracket model is taken as the input variable, and the
network parameters are updated by Adam optimization algorithm. After training iteration, the pointing inclination prediction model is
obtained. Then, aiming at the limitation of slow convergence and easy to produce local minimum value of traditional deep neural
network, genetic algorithm is used to optimize the hyperparameters of deep neural network to improve the training efficiency. Finally, the
test set data is used to predict the change of the pointing angle of the star tracker bracket, and the accuracy of the model in monitoring
the pointing angle of the star tracker bracket under different temperature conditions is analyzed. The experimental results show that the
average error of the directional inclination prediction method of the optimized depth neural network model is 0. 20" , and the accuracy of
inclination prediction is significantly better than the traditional algorithm, which proves that the deep learning method is feasible to
realize the directional inclination monitoring of star tracker bracket. |
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