自适应卡尔曼滤波在无刷直流电机系统辨识中的应用

魏彤; 郭蕊

doi:10.3788/OPE.20122010.2308

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自适应卡尔曼滤波在无刷直流电机系统辨识中的应用

信息科学 | 更新时间：2020-08-12

- 自适应卡尔曼滤波在无刷直流电机系统辨识中的应用
- Application of adaptive Kalman filtering to system identification of brushless DC motor
- 光学精密工程 2012年20卷第10期页码：2308-2314
- 作者机构：
  
  北京航空航天大学惯性技术重点实验室, 新型惯性仪表与导航系统技术国防重点学科实验室北京,100191
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No. 61174134)();国家民用航天预研项目()
- DOI：10.3788/OPE.20122010.2308
  中图分类号： TM351
- 收稿日期：2012-05-14，
  
  修回日期：2012-07-06，
  
  纸质出版日期：2012-10-10
- 稿件说明：
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魏彤, 郭蕊. 自适应卡尔曼滤波在无刷直流电机系统辨识中的应用[J]. 光学精密工程, 2012,20(10): 2308-2314

WEI Tong, GUO Rui. Application of adaptive Kalman filtering to system identification of brushless DC motor[J]. Editorial Office of Optics and Precision Engineering, 2012,20(10): 2308-2314
魏彤, 郭蕊. 自适应卡尔曼滤波在无刷直流电机系统辨识中的应用[J]. 光学精密工程, 2012,20(10): 2308-2314 DOI： 10.3788/OPE.20122010.2308.

WEI Tong, GUO Rui. Application of adaptive Kalman filtering to system identification of brushless DC motor[J]. Editorial Office of Optics and Precision Engineering, 2012,20(10): 2308-2314 DOI： 10.3788/OPE.20122010.2308.

摘要

为了有效抑制量测噪声特性变化对系统辨识精度的影响以获得准确的无刷直流电机模型

提出了一种采用自适应卡尔曼滤波算法的无刷直流电机系统辨识方法。通过计算新息理论方差的极大似然最优估计

并将其引入卡尔曼滤波算法中修正滤波增益来抑制量测噪声特性变化对辨识结果的影响

使该滤波算法实现对模型参数的准确估计

提高辨识精度。实验结果表明

在量测噪声特性变化的情况下

该算法能够准确跟踪实际量测噪声特性的变化

参数估计平滑

相对于目前系统辨识广泛采用的带有遗忘因子的递推最小二乘算法

输出误差的均方根值减小了73.5%。该算法简单易行

计算量小

辨识结果可以很好地描述系统行为

便于在工程实践中应用。

Abstract

To restrain the effect of variable measurement noise and to acquire the accurate model of a brushless DC motor

the identification method for the motor based on adaptive Kalman filtering algorithm was proposed. By computing the maximum likelihood estimation of the innovation variance and using it to modify the filter gain

the influence of variable measurement noise could be restrained and the parameters could be estimated accurately. In this way

the identification accuracy was improved. Experiments show that the adaptive Kalman filtering algorithm can follow the change of actual measurement noise accurately and get smooth estimation results. Compared with the recursive least square algorithm which is widely used in system identification at present

the root mean square value of output error is reduced by 73.5% under the variable measurement noise.The identification results can describe well the system behavior

and offer the same response with the real system.The algorithm is easy to apply to the engineering practice.

关键词

Keywords

references

徐向波, 房建成, 李海涛. 控制力矩陀螺框架系统的谐振抑制与精度控制[J]. 光学精密工程, 2012, 20(2):305-312. XU X B, FANG J CH, LI H T. Resonance elimination and precision control of CMG gimbal system[J]. Opt. Precision Eng., 2012, 20(2):305-312.(in Chinese)[2] 魏彤, 房建成, 刘珠荣. 双框架磁悬浮控制力矩陀螺动框架效应补偿方法[J]. 机械工程学报, 2010, 46(2):159-165. WEI T, FANG J CH, LIU ZH R. Moving-gimbal effects compensation of double gimbal magnetically suspended control moment gyroscope based on compound control[J]. Journal of Mechanical Engineering, 2010, 46(2):159-165.(in Chinese)[3] HAN B CH, MA J J. The influence and optimization design of transmission ratio on the performance of gimbal Servo-system in CMG. 2010 International Conference on Computer Application and System Modeling, Taiyuan, 2010:280-284.[4] 吴靖. 电机传动系统参数辨识方法的研究. 杭州: 浙江大学, 2008. WU J. Research on parameter identification method of motor drive system. Hangzhou: Zhejiang University, 2008. (in Chinese)[5] HE Y H, FENG Y P, WANG Y, et al.. Parameter identification of an induction machine at standstill using the vector constructing method[J]. IEEE Transactions on Power Electronics,2012, 27(2):905-915.[6] 黄宇, 韩璞, 刘长良, 等. 改进量子粒子群算法及其在系统辨识中的应用[J]. 中国电机工程学报, 2011,31(20):114-120. HUANG Y, HAN P, LIU CH L, et al.. An improved quantum particle swarm optimization and its application in system identification[J]. Proceedings of the CSEE, 2011,31(20):114-120.(in Chinese)[7] 王松, 刘明光, 石双双. 基于卡尔曼滤波和神经网络的PMSM参数辨识[J]. 北京交通大学学报, 2010, 34(2):124-136. WAN G S, LIU M G, SHI SH SH. Identification of PMSM based on EKF and Elman neural network[J]. Journal of Beijing Jiaotong University, 2010, 34(2):124-136.(in Chinese)[8] 董岩, 张涛, 李文明, 等. 机载立体测绘相机滚转轴伺服系统的辨识与设计[J]. 光学精密工程, 2011,19(7):1580-1586. DONG Y, ZHANG T, LI W M, et al.. Identification and design of roll axis servo system in airborne solid mapping camera[J]. Opt. Precision Eng., 2011, 19(7):1580-1586.(in Chinese)[9] 李迪, 陈向坚, 续志军, 等. 自组织递归区间二型模糊神经网络在动态时变系统辨识中的应用[J]. 光学精密工程, 2011,19(6):1406-1412. LI D, CHEN X J, XU ZH J, et al.. TypeⅡfuzzy neural networks with self-organizing recurrent intervals for dynamic time varing system identification[J]. Opt. Precision Eng., 2011,19(6):1406-1412.(in Chinese)[10] 李颖, 陈兴林, 宋申民. 小波神经网络用于光纤陀螺漂移误差辨识[J]. 光学精密工程, 2007,15(5):773-778. LI Y, CHEN X L, SONG SH M. Application of wavelet neural network for identification of drifts errors in fiber optical gyroscope[J]. Opt. Precision Eng., 2007,15(5):773-778.(in Chinese)[11] 徐田来, 游文虎, 崔平远. 基于模糊自适应卡尔曼滤波的INS/GPS组合导航系统算法研究[J]. 宇航学报, 2005,26(5):571-575. XU T L, YOU W H, CUI P Y. Research on GPS/INS Integrated Navigation System Based on Fuzzy Adaptive Kalman Filtering[J]. Journal of Astronautics, 2005,26(5):571-575. (in Chinese)[12] 赵龙, 吴康. 新型自适应Kalman滤波算法及其应用[J]. 压电与声光, 2009, 31(6):908-911. ZHAO L, WU K. A new type of adaptive kalman filtering algorithm and its application[J]. Piezoelectrics & Acoustooptics, 2009, 31(6):908-911.(in Chinese)[13] 李旦, 秦永元, 梅春波. 组合导航自适应卡尔曼滤波改进算法研究[J]. 测控技术, 2011,30(3):114-116. LI D, QIN Y Y, MEI CH B. An improved adaptive Kalman filter algorithm for SINS /GPS integrated navigation system[J]. Measurement and control technology, 2011,30(3):114-116. (in Chinese)[14] 卞鸿巍, 金志华, 王俊璞, 等. 组合导航系统新息自适应卡尔曼滤波算法[J]. 上海交通大学学报, 2006,40(6):1000-1003. BIAN H W, JIN Zh H, WANG J P, et al.. The Innovation-based estimation adaptive Kalman filter algorithm for INS/GPS integrated navigation system[J]. Journal of Shang Hai Jiao Tong University, 2006, 40(6):1000-1003. (in Chinese)[15] LYAS E. Open-loop and closed-loop experimental on-line identification of a three-mass electromechanical system[J]. Mechatronics, 2004,14:549-565.[16] NECLA T, SEDATBAYSEC, TOLGAYKARA. Nonlinear modeling and identification of a spark ignition engine torque[J]. Mechanical Systems and Signal Processing, 2012,26:294-304.

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