基于多参量模型的光纤陀螺温度误差补偿
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摘要
温度漂移误差是制约光纤陀螺精度的重要因素之一。针对传统光纤陀螺温度补偿方法仅对温度项建模导致补偿精度差的问题,提出了一种新型多参量模型来补偿光纤陀螺温度误差的方法。通过对陀螺零漂误差和温度各相关项进行相关性分析,将温度和温度速率的乘积项及温度梯度滞后项引入到温度漂移误差模型中,建立了多参量分段补偿模型对零偏进行补偿,显著改善了光纤陀螺的零偏稳定性。使用实测光纤陀螺数据对提出的补偿方法进行实验验证,结果表明采用该方法补偿后,零偏误差平方和降低2个数量级,陀螺漂移均值、方差稳定在零点附近,补偿效果优于温度项分段拟合方法,与非线性模型预测效果相当。
The temperature drift error is one of the important factors restricting the accuracy of FOG. Aiming at the problem that the traditional FOG temperature compensation models had low accuracy for only modeling about temperature term, a new multi-parameter model is proposed to compensate the temperature error. By analyzing the correlation of gyro bias error and temperature related items, the product term of temperature and temperature rate, temperature gradient hysteresis are introduced into the temperature drift model. A multi-parameter piecewise compensation model is established to compensate the bias, and the bias stability of FOG is improved remarkab-ly. Experimental verification of the proposed compensation method using the actual measured FOG data, the results show that the sum of the squares of the FOG bias error is reduced by two orders of magnitude, and the mean and variance of the gyro drift is stable near the zero point, the compensation effect is better than that of the only temperature term piecewise compensation method. It is comparable to the predictive effect of nonlinear models.
The temperature drift error is one of the important factors restricting the accuracy of FOG. Aiming at the problem that the traditional FOG temperature compensation models had low accuracy for only modeling about temperature term, a new multi-parameter model is proposed to compensate the temperature error. By analyzing the correlation of gyro bias error and temperature related items, the product term of temperature and temperature rate, temperature gradient hysteresis are introduced into the temperature drift model. A multi-parameter piecewise compensation model is established to compensate the bias, and the bias stability of FOG is improved remarkab-ly. Experimental verification of the proposed compensation method using the actual measured FOG data, the results show that the sum of the squares of the FOG bias error is reduced by two orders of magnitude, and the mean and variance of the gyro drift is stable near the zero point, the compensation effect is better than that of the only temperature term piecewise compensation method. It is comparable to the predictive effect of nonlinear models.
引文
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[12] 冯卡力,李安,覃方君,等.光纤陀螺温度误差自适应神经模糊补偿方法[J].兵工学报,2016,37(4):641-647.Feng Kali,Li An,Qin Fangjun,et al.Temperature error compensation method based on adaptive neuro fuzzy inference for fiber-optic gyro[J].Acta Armamentarii,2016,37(4):641-647(in Chinese).
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[2] 薛连莉,陈少春,陈效真.2017年国外惯性技术发展与回顾[J].导航与控制,2018,17(2):1-9+40.Xue Lianli,Chen Shaochun,Chen Xiaozhen.Development and review of foreign inertial technology in 2017[J].Navigation and Control,2018,17(2):1-9+40(in Chinese).
[3] 那永林.基于简化Mohr模型的光纤陀螺温度补偿方法研究[J].导航定位与授时,2017,4(6):99-102.Na Yonglin.Temperature compensation research for FOG based on mohr model [J].Navigation Position-ing & Timing,2017,4(6):99-102(in Chinese).
[4] Dzhashitov V E,Pankratov V M.Control of temperature fields of a strapdown inertial navigation system based on fiber optic gyroscopes[J].Journal of Computer and Systems Sciences International,2014,53(4):565-575.
[5] Liaw C Y,Zhou Y,Lam Y L.Theory of an amplified closed-Sagnac-loop interferometric fiber-optic gyroscope[J].IEEE Journal of Quantum Electronics,1999,35(12):1777-1785.
[6] Zha F,Xu J,Li J,et al.IUKF neural network modeling for FOG temperature drift[J].Journal of Systems Engineering and Electronics,2013,24(5):838-844.
[7] 吴军伟,缪玲娟,李福胜,等.改进支持向量机的光纤陀螺温度漂移补偿方法[J].红外与激光工程,2018,47(5):142-147.Wu Junwei,Miao Lingjuan,Li Fusheng,et al.Compensation method of FOG temperature drift with improved support vector machine[J].Infrared and Laser Engineering,2018,47(5):142-147(in Chinese).
[8] 张燕萍,潘子军,魏志武,等.光纤陀螺标度因数温度补偿硬件实现[J].中国惯性技术学报,2013,21(5):660-662.Zhang Yanping,Pan Zijun,Wei Zhiwu,et al.Hardware implementation of temperature compensation for FOG’s scale-factor[J].Journal of Chinese Inertial Technology,2013,21(5):660-662(in Chinese).
[9] 杨纪刚,毕聪志,孙国飞.光纤传感环圈骨架热应力仿真计算与实验研究[J].导航定位与授时,2016,3(3):65-73.Yang Jigang,Bi Congzhi,Sun Guofei.Thermal stress calculation and experiment investigation of fiber optical sensor coil spool[J].Navigation Positioning & Timing,2016,3(3):65-73(in Chinese).
[10] 王俊璞,金志华,田蔚风.光纤陀螺温度漂移的多变量模型[J].光电工程,2008,35(5):66-69.Wang Junpu,Jin Zhihua,Tian Weifeng.Multi-variable modeling strategy for the fiber optic gyro temperature-dependent drift[J].Opto-Electronic Engineering,2008,35(5):66-69(in Chinese).
[11] 冯卡力,李安,覃方君.基于多模型分段拟合的光纤陀螺温度误差补偿方法[J].中国惯性技术学报,2014,22(6):825-828.Feng Kali,Li An,Qin Fangjun.Temperature error compensation method for FOG based on multi-model piecewise fitting[J].Journal of Chinese Inertial Technology,2014,22(6):825-828(in Chinese).
[12] 冯卡力,李安,覃方君,等.光纤陀螺温度误差自适应神经模糊补偿方法[J].兵工学报,2016,37(4):641-647.Feng Kali,Li An,Qin Fangjun,et al.Temperature error compensation method based on adaptive neuro fuzzy inference for fiber-optic gyro[J].Acta Armamentarii,2016,37(4):641-647(in Chinese).
[13] Chernoff H,Scheffe H.A generalization of the Neyman-Pearson fundamental lemma[J].The Annals of Mathematical Statistics,1952,23(2):213-225.
[14] 游士兵,严研.逐步回归分析法及其应用[J].统计与决策,2017(14):31-35.You Shibing,Yan Yan.Stepwise regression analysis and its application[J].Statistics and Decision,2017(14):31-35(in Chinese).