基于CEEMD半球谐振陀螺输出预测方法

doi:10.12305/j.issn.1001-506X.2023.10.30

摘要/Abstract

摘要：

为解决半球谐振陀螺(hemispherical resonator gyro, HRG)实时输出预测问题, 提高陀螺输出预测精度, 提出了一种基于互补集合经验模态分解的时间序列反向传播(back propagation, BP)神经网络输出预测方法。该方法通过对陀螺输出数据使用互补集合经验模态分解, 对分解得到的信号分量进行平稳性检验, 根据检验结果选择时间序列分析和BP神经网络建模预测, 将预测信号重构得到最终预测信号。所提方法克服了HRG输出非线性和非平稳性较强的特点与传统时间序列分析建模要求的矛盾。进行了该方法与单独使用时间序列分析、BP神经网络建模预测方法的对比, 所提方法的预测精度提高了1~2个数量级, 验证了该方法的有效性和精度。

关键词: 半球谐振陀螺, 互补集合经验模态分解, 时间序列分析, 反向传播神经网络

Abstract:

In order to solve the real time prediction problem of hemispherical resonator gyro (HRG) output and improve the prediction accuracy of HRG output, a time-series back propagation (BP) neural network output prediction method based on complementary ensemble empirical mode decomposition is proposed. The method uses complementary ensemble empirical mode decomposition to decompose output data of gyro, and then tests the stability of the decomposed signal components. According to the test results, the time-series analysis modeling and BP neural network modeling are selected to model to predict gyro data, and then reconstruct the predicted signal to obtain the final prediction signal.The method overcomes the contradiction between the strong nonlinearity and nonstationarity of HRG output and the requirements of traditional time series analysis modeling. Compared with time series analysis and BP neural network modeling prediction method, the prediction accuracy of the proposed method is improved by 1~2 orders of magnitude, which verifies the effectiveness and accuracy of the proposed method.

Key words: hemispherical resonator gyro (HRG), complementary ensemble empirical mode decom-position (CEEMD), time-series analysis, back propagation (BP) neural network

中图分类号:

U666.1

吴宗收, 汪立新, 沈强, 李灿, 李文华. 基于CEEMD半球谐振陀螺输出预测方法[J]. 系统工程与电子技术, 2023, 45(10): 3259-3264.

Zongshou WU, Lixin WANG, Qiang SHEN, Can LI, Wenhua LI. Output prediction method of hemispherical resonator gyro based on CEEMD[J]. Systems Engineering and Electronics, 2023, 45(10): 3259-3264.

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参考文献 30

1	刘付成, 赵万良, 宋丽君. 半球谐振陀螺惯性敏感器及其空间应用[M]. 北京: 中国宇航出版社, 2019.
	LIU F C , ZHAO W L , SONG L J . Inertial sensor of hemisphe-rical resonator gyroscope and its space application[M]. Beijing: China Aerospace Press, 2019.
2	沈玉芃, 杨文钰, 朱鹤, 等. 2020年国外惯性技术的发展与展望[J]. 飞航导弹, 2021, (4): 7- 12. doi: 10.16338/j.issn.1009-1319.20210812
	SHEN Y F , YANG W Y , ZHU H , et al. Development and prospect of foreign inertial technology in 2020[J]. Aerodynamic Missile Journal, 2021, (4): 7- 12. doi: 10.16338/j.issn.1009-1319.20210812
3	ROZELLE D M . The hemispherical resonator gyro: from wineglass to the planets[J]. Advances in the Astronautical Sciences, 2013, 134, 1157- 1178.
4	陈雪. 半球谐振陀螺仪的误差机理分析和实验方法的研究[D]. 哈尔滨: 哈尔滨工业大学, 2009.
	CHEN X. Error mechanism analysis and experimental method dedign for the hemispherical resonator gyro[D]. Harbin: Harbin Institute of Technology, 2009.
5	QIU B M , WANG J W , LI P H . Full digital control of hemispherical resonator gyro under force-to-rebalance mode[J]. IEEE of Sensors Journal, 2015, 15 (1): 71- 75. doi: 10.1109/JSEN.2014.2339229
6	刘付成, 赵万良, 杨浩, 等. 半球谐振陀螺技术[J]. 导航与控制, 2020, 19 (Z1): 208- 215.
	LIU F C , ZHAO W L , YANG H , et al. Technology of hemispherical resonator gyroscope[J]. Navigation and Control, 2020, 19 (Z1): 208- 215.
7	王琪, 秦伟伟, 沈强, 等. 基于Weibull云模型的陀螺输出可靠性分析[J]. 中国惯性技术学报, 2020, 28 (3): 415- 420. doi: 10.13695/j.cnki.12-1222/o3.2020.03.021
	WANG Q , QIN W W , SHEN Q , et al. Reliability analysis of gyro based on Weibull cloud model[J]. Journal of Chinese Inertial Technology, 2020, 28 (3): 415- 420. doi: 10.13695/j.cnki.12-1222/o3.2020.03.021
8	XU Z Y , ZHU W D , YI G X , et al. Dynamic modeling and output error analysis of an imperfect hemispherical shell resonator[J]. Journal of Sound and Vibration, 2021, 498 (12): 115964.
9	DAI C L , PI D C . Parameter auto-selection for hemispherical resonator gyroscope's long-term prediction model based on coope-rative game theory[J]. Knowledge-Based Systems, 2017, 134, 105- 115. doi: 10.1016/j.knosys.2017.07.022
10	ZHAO W L , YANG H , LIU F C , et al. High sensitivity rate-integrating hemispherical resonator gyroscope with dead area compensation for damping asymmetry[J]. Scientific Reports, 2021, 11 (1): 2195. doi: 10.1038/s41598-020-80885-y
11	SONG L J , NI J Y , ZHOU L , et al. The analysis and simulation with the fatigue life of hemispherical resonator gyro[J]. Journal of Sensors, 2021, 3, 5564932.
12	XU Z Y , YI G X . Acceleration drift mechanism analysis and compensation for hemispherical resonant gyro based on dynamics[J]. Micro-system Technologies, 2019, 25 (9): 3425- 3435. doi: 10.1007/s00542-019-04327-0
13	WEI Z N , YI G X , HUO Y , et al. The synthesis model of flat-electrode hemispherical resonator gyro[J]. Sensors, 2019, 19 (7): 1690. doi: 10.3390/s19071690
14	覃施甦. 半球谐振陀螺误差模型分析及补偿技术研究[D]. 成都: 电子科技大学, 2014.
	QIN S S. Analysis and compensation of the error of hemisphe-rical resonator gyro[D]. Chengdu: University of Electronic Science and Technology, 2014.
15	JUNGSHIN L , YUN S , JAEWOOK R . Design and verification of a digital controller for a 2-piece hemispherical resonator gyroscope[J]. Sensors, 2016, 16 (4): 555. doi: 10.3390/s16040555
16	YU X D, LONG X W, WEI G, et al. Statistical analysis of nature frequencies of hemispherical resonator gyroscope based on probability theory[C]//Proc. of the International Society for Optics and Photonics, 2015.
17	TABRIZI A , GARIBALDI L , FASANA A , et al. Early da-mage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine[J]. Meccanica, 2015, 50 (3): 865- 874. doi: 10.1007/s11012-014-9968-z
18	FOUED S , OTHMAN B M . Multiscaled neural autoregressive distributed lag: a new empirical mode decomposition model for nonlinear time series forecasting[J]. International Journal of Neural Systems, 2020, 30 (8): 15.
19	李文华, 汪立新, 沈强, 等. 基于EMD的MEMS陀螺仪随机漂移分析方法[J]. 北京航空航天大学学报, 2021, 47 (9): 1927- 1932.
	LI W H , WANG L X , SHEN Q , et al. Random drift analysis method of MEMS gyroscope based on EMD[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47 (9): 1927- 1932.
20	LI J , LIU C , ZENG Z F , et al. GPR signal denoising and target extraction with the CEEMD method[J]. IEEE Geoscience & Remote Sensing Letters, 2015, 12 (8): 1615- 1619.
21	刘明, 刘金辉, 陈金萌, 等. 基于改进EMD的MEMS陀螺仪降噪方法[J]. 传感技术学报, 2020, 33 (5): 705- 710.
	LIU M , LIU J H , CHEN J M , et al. MEMS gyroscope noise reduction method based on improved EMD[J]. Chinese Journal of Sensors and Actuators, 2020, 33 (5): 705- 710.
22	DANG S W , LI L J , WANG Q Q , et al. Fiber optic gyro noise reduction based on hybrid CEEMDAN-LWT method[J]. Mea-surement, 2020, 161, 107865.
23	田颖, 汪立新, 李灿, 等. 基于EEMD-RVM的陀螺漂移混合建模预测[J]. 传感技术学报, 2015, 28 (10): 1520- 1524.
	TIAN Y , WANG L X , LI C , et al. Mixed modeling for gyro drift prediction based on EEMD-RVM[J]. Chinese Journal of Sensors and Actuators, 2015, 28 (10): 1520- 1524.
24	YEH J R , SHIEH J S , HUANG N E . Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010, 2 (2): 135- 156. doi: 10.1142/S1793536910000422
25	DANG S W , TIAN W F , QIAN F . EMD-and LWT-based stochastic noise eliminating method for fiber optic gyro[J]. Mea-surement, 2011, 44 (10): 2190- 2193.
26	ZHOU B , WANG W . Research on a wavelet filtering method of the fiber optic gyroscope based on EMD[J]. Advanced Materials Research, 2013, 705, 253- 257.
27	LI G H , WANG S L . Sunspots time-series prediction based on complementary ensemble empirical mode decomposition and wavelet neural network[J]. Mathematical Problems in Engineering: Theory, Methods and Applications, 2017, 2017, 3513980.
28	ZHA F , XU J N , LI J S , et al. IUKF neural network modeling for FOG temperature drift[J]. Journal of Systems Engineering and Electronics, 2013, 24 (5): 838- 844.
29	李志杰, 王卿, 党建军, 等. BP神经网络在半球谐振陀螺仪零偏温度补偿中的应用[J]. 微纳电子技术, 2021, 58 (2): 152- 157.
	LI Z J , WANG Q , DANG J J , et al. Application of BP neural network in hemispherical resonant gyroscope zero bias temperature compensation[J]. Micronanoelectronic Technology, 2021, 58 (2): 152- 157.
30	段志强, 刘洁瑜, 汪立新, 等. BPNN辅助KF的MEMS陀螺仪数据处理方法[J]. 压电与声光, 2020, 42 (2): 284- 288.
	DUAN Z Q , LIU J Y , WANG L X , et al. Research on data processing method of MEMS gyroscope based on BPNN assisted Kalman filter[J]. Piezoelectrics & Acoustooptics, 2020, 42 (2): 284- 288.

参数	IMF1 ARMA(3, 3)	IMF2 ARMA(3, 3)	IMF3 ARMA(3, 3)	IMF4 ARMA(3, 3)	IMF5 ARMA(3, 3)	IMF6 ARMA(3, 3)	IMF7 ARMA(3, 3)
α₁	1.030 2	-0.950 8	-2.166 5	-2.788 4	-2.952 4	-2.975 8	-2.991 4
α₂	1.013 2	0.784 7	1.872 4	2.679 5	2.919 4	2.953 9	2.982 9
α₃	0.349 8	-0.185 9	-0.572 5	-0.882 4	-0.966 7	-0.978 1	-0.991 9
β₁	0.208 0	0.485 2	1.052 6	1.362 9	1.870 0	1.792 2	2.283 3
β₂	0.456 2	-0.690 3	0.630 2	0.876 2	1.608 0	1.566 9	2.171 5
β₃	-0.144 1	-0.438 5	0.371 8	0.278 7	0.618 5	0.645 8	0.843 7

模型	MSE/V	平均相对预测误差/%
组合模型	5.32×10^-10	1.29
ARMA模型	4.61×10^-8	12.56
BP神经网络模型	2.09×10^-9	2.36

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[12]	邓森，景博，周宏亮. 基于灰色时序组合模型的产品实时寿命预测方法[J]. 系统工程与电子技术, 2014, 36(4): 802-808.
[13]	姜学鹏，洪贝. 基于AP的Volterra级数自适应多重回归及其多步预测应用[J]. 系统工程与电子技术, 2014, 36(12): 2562-2565.
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