Application of adaptive Kalman filtering to system identification of brushless DC motor

WEI Tong; GUO Rui

doi:10.3788/OPE.20122010.2308

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Application of adaptive Kalman filtering to system identification of brushless DC motor

Article | 更新时间：2020-08-12

- Application of adaptive Kalman filtering to system identification of brushless DC motor
- Optics and Precision Engineering Vol. 20, Issue 10, Pages: 2308-2314(2012)
- 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：
- DOI：10.3788/OPE.20122010.2308
  CLC： TM351
- Received：14 May 2012，
  
  Revised：06 July 2012，
  
  Published：10 October 2012
- 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.

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references

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