Temperature modeling and compensation method of hemispherical resonator gyro based on KPCA and grouped method of data handling neural network

doi:10.3785/j.issn.1008-973X.2024.07.003

Journal of ZheJiang University (Engineering Science)

2024, Vol. 58

Issue (7): 1336-1345 DOI: 10.3785/j.issn.1008-973X.2024.07.003

Temperature modeling and compensation method of hemispherical resonator gyro based on KPCA and grouped method of data handling neural network

Chen ZHANG(

),Lixin WANG*(

),Xiangyu KONG

School of Missile Engineering, Rocket Force University of Engineering, Xi’an 710025, China

Download:

HTML

PDF(3323KB) HTML
Export: BibTeX | EndNote (RIS)

Abstract

A modeling and compensation method based on kernel principal component analysis (KPCA) and grouped method of data handling (GMDH) neural network was proposed aiming at the temperature modeling and compensation of hemispherical resonator gyro (HRG). By analyzing the temperature characteristics and the big data characteristics of HRG, the eigenvectors of the network were initially selected. To remove the correlation and redundancy of the HRG outputs, KPCA was introduced and the eigenvector dimension was reduced. The eigenvectors were substituted into the GMDH neural network and the training set and the validation set were distinguished to determine the network weight and structure to model and compensate for the HRG temperature drift. Experiment results showed that the proposed method was significantly better than the traditional polynomial model for single-sample predictions; for multiple-sample predictions, under four different training samples, the accuracy of the proposed method was 46.5%, 51.5%, 54.6% and 65.3% higher than that of the traditional polynomial model, also 3.6%, 5.1%, 3.8% and 8.8% higher than that of the GMDH model. The proposed method effectively improved the measurement accuracy of HRG under variable temperature conditions.

Key words： hemispherical resonator gyro(HRG) kernel principal component analysis(KPCA) grouped method of data handling(GMDH) temperature modeling and compensation measurement accuracy

Received: 13 June 2023 Published: 01 July 2024

CLC:

V 241.5

Fund: 国防科技创新特区基金资助项目（HHJJ-2022-0402）.

Corresponding Authors: Lixin WANG E-mail: buaa0318@163.com;wlxxian@sina.com

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Chen ZHANG
	Lixin WANG
	Xiangyu KONG

Cite this article:

Chen ZHANG,Lixin WANG,Xiangyu KONG. Temperature modeling and compensation method of hemispherical resonator gyro based on KPCA and grouped method of data handling neural network. Journal of ZheJiang University (Engineering Science), 2024, 58(7): 1336-1345.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.07.003 OR https://www.zjujournals.com/eng/Y2024/V58/I7/1336

基于KPCA和数据处理组合方法神经网络的半球谐振陀螺温度建模补偿方法

针对半球谐振陀螺（HRG）的温度建模与补偿问题，提出基于核主成分分析（KPCA）和数据处理组合方法（GMDH）神经网络的建模补偿方法. 通过分析HRG的温度特性和大数据特征，初步确定网络模型的特征向量. 为了去除HRG输出数据的相关性和冗余性，引入KPCA并降低特征向量维度. 将特征向量代入GMDH神经网络训练，区分训练集和验证集以确定网络权值和网络结构，实现HRG温度漂移的建模与补偿. 实验结果表明，单一样本预测时，所提方法预测效果明显好于传统多项式模型；多样本预测时，在4种不同训练样本下，所提方法相比传统多项式模型精度分别提升了48.5%、54.0%、56.3%、68.4%，相比GMDH模型分别提升了3.6%、5.1%、3.8%、8.8%. 所提方法能够有效提高HRG在变温工况下的测量精度.

关键词： 半球谐振陀螺(HRG), 核主成分分析(KPCA), 数据处理组合方法(GMDH), 温度建模与补偿, 测量精度

Fig.1 Network structure of grouped method of data handing

Fig.2 Network model of kernel principal component analysis and grouped method of data handling neural network

Fig.3 Experimental device of hemispherical resonator gyro

Fig.4 Parameter variation curves of hemispherical resonator gyro

Fig.5 Hemispherical resonator gyro rotation angle before and after deducting turntable movement

Fig.6 Hemispherical resonator gyro rotation angle before and after smoothing

Fig.7 Hemispherical resonator gyro rotation angle after removing data from inactive state

Fig.8 Hemispherical resonator gyro angular velocity after resampling

Tab.1 Statistical characteristics of primary feature vectors for samples

Tab.2 Statistical characteristics of feature vectors for samples after kernel principal component analysis

Tab.3 Statistical characteristics of angular velocity for samples

Fig.9 Prediction curve and error curve for sample two

Tab.4 Root mean squared error of prediction results for different samples

Fig.10 Prediction curve and error curve for fused sample A fitting sample four

Fig.11 Prediction curve and error curve for fused sample B fitting sample four

Fig.12 Prediction curve and error curve for fused sample C fitting sample four

Fig.13 Prediction curve and error curve for fused sample D fitting sample four

Tab.5 Comparison of error standard deviations for different modeling methods


[1]	汪立新, 周小刚, 刘洁瑜, 等. 半球谐振陀螺惯性系统[M]. 西安: 西北工业大学出版社, 2011: 20–34.

[2]	周小刚, 汪立新, 佘嫱, 等半球谐振陀螺温度补偿与实验研究[J]. 宇航学报, 2010, 31 (4): 1083- 1087 ZHOU Xiaogang, WANG Lixin, SHE Qiang, et al HRG temperature compensation and experiment research[J]. Journal of Astronautics, 2010, 31 (4): 1083- 1087

[3]	周强, 覃施甦, 方海斌, 等半球谐振陀螺温度特性及补偿分析[J]. 压电与声光, 2015, 37 (5): 818- 820 ZHOU Qiang, QIN Shisu, FANG Haibin, et al The temperature characteristic and compensating analysis of HRG[J]. Piezoelectrics and Acoustooptics, 2015, 37 (5): 818- 820

[4]	吴宗收, 汪立新, 李新三, 等一种改进PSO-ARMA半球谐振陀螺温度误差建模方法[J]. 北京航空航天大学学报, 2022, 48 (6): 1050- 1056 WU Zhongshou, WANG Lixin, LI Xinsan, et al An improved PSO-ARMA method for temperature error modeling of hemispherical resonator gyroscope[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48 (6): 1050- 1056

[5]	李广胜, 蒋英杰, 孙志强, 等基于AR多变量模型的半球谐振陀螺温度漂移建模[J]. 传感技术学报, 2009, 22 (10): 1442- 1445 LI Guangsheng, JIANG Yingjie, SUN Zhiqiang, et al Modeling temperature drift of HRG based on AR multi-variable model[J]. Chinese Journal of Sensors and Actuators, 2009, 22 (10): 1442- 1445

[6]	LI X S, LI C, SHEN Q, et al A multiple regression based method for indirect compensation of hemispherical resonator gyro temperature error[J]. Scientific Reports, 2023, 13: 6019 doi: 10.1038/s41598-023-31868-2

[7]	汪国新, 郝勇生, 苏志刚基于KPCA-GG的火力发电设备状态诊断方法[J]. 东南大学学报:自然科学版, 2019, 49 (3): 542- 548 WANG Guoxin, HAO Yongsheng, SU Zhigang Condition diagnosis method for thermal power generation equipment based on KPCA-GG[J]. Journal of Southeast University: Natural Science Edition, 2019, 49 (3): 542- 548

[8]	黄泽丰, 杨莘, 邓慧萍, 等基于MDLatLRR和KPCA的光场图像全聚焦融合[J]. 光子学报, 2023, 52 (4): 0410004 HUANG Zefeng, YANG Shen, DENG Huiping, el al Light field all-in-focus image fusion based on MDLatLRR and KPCA[J]. Acta Photonica Sincia, 2023, 52 (4): 0410004 doi: 10.3788/gzxb20235204.0410004

[9]	高运广, 蔡艳平, 盛安基于模糊AGA-KPCA的MIMU传感器故障诊断方法[J]. 中国惯性技术学报, 2022, 30 (6): 835- 840 GAO Yunguang, CAI Yanping, SHENG An Fault diagnosis method for MIMU sensors based on fuzzy AGA-KPCA[J]. Journal of Chinese Inertial Technology, 2022, 30 (6): 835- 840

[10]	段伟, 蔡国军, 袁俊, 等基于GMDH的地震液化场地侧向变形预测模型[J]. 东南大学学报: 自然科学版, 2021, 51 (2): 306- 311 DUAN Wei, CAI Guojun, YUAN Jun, et al Prediction model for liquefaction-induced lateral spread displacement based on GHDH[J]. Journal of Southeast University: Natural Science Edition, 2021, 51 (2): 306- 311

[11]	凯立德·艾尔巴兹. 基于机器学习技术的隧道掘进机性状的预测模型研究[D]. 上海: 上海交通大学, 2019: 35–46. Khalid Elbaz. Performance prediction of tunnel boring machine by using machine learning techniques [D]. Shanghai: Shanghai Jiao Tong University, 2019: 35–46.

[12]	AHMADI A, ASADI Y, AMANI A M, et al Resilient model predictive adaptive control of networked Z-source inverters using GMDH[J]. IEEE Transactions on Smart Grid, 2022, 13 (5): 3723- 3734 doi: 10.1109/TSG.2022.3174250

[13]	XIE L, JIA Y, XIAO J, et al GMDH-based outlier detection model in classification problems[J]. Journal of Systems Science and Complexity, 2020, 33: 1516- 1532 doi: 10.1007/s11424-020-9002-6

[14]	BAND S S, MOHAMMADZADEH A, CSIBA P, et al Voltage regulation for photovoltaics-battery-fuel systems using adaptive group method of data handling neural networks (GMDH-NN)[J]. IEEE Access, 2020, 8: 213748- 213757 doi: 10.1109/ACCESS.2020.3037134

[15]	刘强, 秦泗钊过程工业大数据建模研究展望[J]. 自动化学报, 2016, 42 (2): 161- 171 LIU Qiang, QIN Joe Perspectives on big data modeling of process industries[J]. Acta Automatica Sinica, 2016, 42 (2): 161- 171

[16]	王晓军. 基于大数据的风洞马赫数集成建模方法的研究[D]. 沈阳: 东北大学, 2016: 9–15. WANG Xiaojun. The research of ensemble methods based on big data for the Mach number prediction in wind tunnel [D]. Shen Yang: Northeastern University, 2016: 9–15.

[17]	王龙晖. 基于并行计算的调节阀大数据智能分析及建模方法研究[D]. 济南: 山东大学, 2019: 81–90. WANG Longhui. Research on intelligent analysis and modeling of control valve big data based on parallel computing [D]. Jinan: Shandong University, 2019: 81–90.

[18]	杨小佳. 基于腐蚀大数据技术的含Cr低合金钢耐蚀性能调控研究[D]. 北京: 北京科技大学, 2021: 98–110. YANG Xiaojia. Research on the control of corrosion resistance of Cr-containing low-alloy steel based on corrosion big data technology [D]. Beijing: University of Science and Technology Beijing, 2021: 98–110.

[19]	陈效真, 周姣, 董燕琴, 等大数据理论在高精度惯性导航系统测试技术中的应用[J]. 导航与控制, 2018, 17 (1): 11- 20 CHEN Xiaozhen, ZHOU Jiao, DONG Yanqin, et al Application of big data technology on test technology of high accuracy inertial navigation system[J]. Navigation and Control, 2018, 17 (1): 11- 20

[20]	王庆杰. 基于特征选择和时频分析的径流预测机器学习模型研究[D]. 乌鲁木齐: 新疆农业大学, 2022: 65–67. WANG Qingjie. A machine learning model for runoff prediction based on feature selection and joint time-frequency analysis [D]. Urumqi: Xinjiang Agricultural University, 2022: 65–67.

[21]	DENG X, TIAN X, CHEN S, et al Deep principal component analysis based on layerwise feature extraction and its application to nonlinear process monitoring[J]. IEEE Transactions on Control Systems Technology, 2019, 27 (6): 2526- 2540 doi: 10.1109/TCST.2018.2865413

[22]	孔祥玉, 曹泽豪, 安秋生, 等偏最小二乘线性模型及其非线性动态扩展模型综述[J]. 控制与决策, 2018, 33 (9): 1537- 1548 KONG Xiangyu, CAO Zehao, AN Qiusheng, et al Review of partial least squares linear models and their nonlinear dynamic expansion models[J]. Control and Decision, 2018, 33 (9): 1537- 1548

[23]	徐田军, 王桂增改进的GMDH方法及其在参数预报中的应用[J]. 控制与决策, 1994, (5): 367- 371 XU Tianjun, WANG Guizeng Improved GMDH algorithm and its application in parameter prediction[J]. Control and Decision, 1994, (5): 367- 371

[24]	姚泽楷, 蔡耀仪, 李诗文, 等基于平滑样条曲线结合离散状态转移算法的拉曼光谱基线校正方法[J]. 中国激光, 2022, 49 (18): 1811001 YAO Zekai, CAI Yaoyi, LI Shiwen, et al Baseline correction for raman spectroscopy using cubic spline smoothing combined with discrete state transformation algorithm[J]. Chinese Journal of Lasers, 2022, 49 (18): 1811001 doi: 10.3788/CJL202249.1811001

[1]	YANG Xian-Yong, ZHOU Xiao-Jun, ZHANG Wen-Bin, YANG Fu-Chun. Rolling bearing fault diagnosis based on local wave method and KPCA-LSSVM[J]. Journal of ZheJiang University (Engineering Science), 2010, 44(8): 1519-1524.

Viewed

Full text

Abstract

Cited

Shared

Discussed