改进极大似然法动力调谐陀螺仪闭环辨识(PDF)
《纳米技术与精密工程》[ISSN:1672-6030/CN:12-1351/O3]
- 期数:
- 2017年6期
- 页码:
- 499-506
- 栏目:
- 出版日期:
- 2017-11-15
文章信息/Info
- Title:
- Dynamically Tuned Gyroscope Closed-Loop Identification Based on Modified Maximum Likelihood Method
- 文章编号:
- 1672-6030(2017)06-0499-08
- 作者:
- 王亚辉1; 李醒飞1; 2; 纪越1; 赵建远1
- 1. 天津大学精密仪器与光电子工程学院, 天津 300072; 2. 精密测试技术及仪器国家重点实验室(天津大学), 天津 300072
- Author(s):
- Wang Yahui1; Li Xingfei1; 2; Ji Yue1; Zhao Jianyuan1
- 1. School of Precision Instrument and OptoElectronics Engineering, Tianjin University, Tianjin 300072, China; 2. State Key Laboratory of Precision Measuring Technology and Instruments (Tianjin University), Tianjin 300072, China
- 关键词:
- 闭环辨识; 动力调谐陀螺仪; 极大似然法; 有色噪声
- Keywords:
- closed-loop identification; dynamically tuned gyroscope; maximum likelihood method; colored noise
- 分类号:
- TP273; U666
- DOI:
- 10.13494/j.npe.20160005
- 文献标识码:
- A
- 摘要:
- 针对Box-Jenkins(BJ)模型辅助向量法和Newton-Raphson法计算繁杂、收敛速度慢、辨识精度不高等问题和极大似然法无法直接应用在闭环辨识的限制,把结合BJ模型的递推的极大似然(recursive maximum likelihood,RML)参数估计法应用于动力调谐陀螺仪的闭环辨识,提出了不受耦合有色噪声影响的BJ模型近似递推极大似然(BJRML)闭环辨识法,获取了动力调谐陀螺仪的参数估计值并实现陀螺仪在线性能监测.结合动力调谐陀螺仪的闭环简化模型等先验知识,通过数值仿真验证BJRML法辨识结果的无偏一致性与渐进最优性;在实验室条件下采用本方法进行动力调谐陀螺仪闭环辨识实验.仿真结果表明:在有色噪声存在的条件下,BJRML法的辨识结果是一致无偏渐进最优的;闭环辨识实验结果表明:辨识精度优于92%;辨识结果能够跟踪陀螺特性,基本实现陀螺仪性能在线监测.
- Abstract:
- Regarding the problems that Box-Jenkins instumental variable (BJIV) method is slow in convergence speed, that Newton-Raphson method is low in precision, and that maximum likelihood method cannot be directly applied in closedloop identification, Box-Jenkins recursive maximum likelihood (BJRML) method was proposed and applied to dynamically tuned gyroscope (DTG) closed-loop identification. DTG model parameter was obtained, and online performance monitoring was achieved. The method combined BoxJenkins model with recursive maximum likelihood method. Also, it is not affected by coupling colored noise. Firstly, the prior knowledge of simplified closedloop model of DTG was obtained. Then, the paper verified the unbiased consistency and asymptotic optimality of BJRML identification results. Finally, identification experiments were conducted on the DTG closed-loop system in laboratory. The simulation results are as follows: the estimations of BJRML method are unbiased and consistent with different noise levels, and the asymptotic variance is near-optimal. Experiment results show that the identification fitting degree is more than 92%. Identification results can track gyroscope characteristics and achieve basic online monitoring.
备注/Memo
- 备注/Memo:
- 收稿日期: 2016-03-25.
基金项目: 国家自然科学基金资助项目(60972129);国家重点实验室开放基金资助项目(pil1006).
作者简介: 王亚辉(1989—),男,硕士研究生.
通讯作者: 李醒飞,教授,lixf@tju.edu.cn.
更新日期/Last Update: 2017-12-21