| 基于辅助变量的永磁同步直线电机系统辨识研究 |
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| 引用本文： | 梁健强,,吴金洲,魏巍.基于辅助变量的永磁同步直线电机系统辨识研究[J].微电机,2021,0(11):52-57+102. |
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| 作者姓名： | 梁健强 吴金洲 魏巍 |
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| 作者单位： | (1.中国工程物理研究院机械制造工艺研究所,四川 绵阳 621900;2.华中科技大学 机械科学与工程学院,武汉 430074) |
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| 摘 要： | 针对永磁同步直线电机系统在有色噪声干扰下的辨识问题，提出了一种基于辅助变量的模型参数辨识方法。分析并建立了永磁同步直线电机的数学模型和系统的开环传递函数，引入辅助变量对标准的递推最小二乘法进行改进，对夹杂有色噪声数据的系统模型进行参数辨识。同时，基于固定模型的变回归估计方法（FMVRE）辨识了系统中可能存在的纯延时环节因子。仿真结果表明:在有色噪声影响下，辅助变量递推最小二乘法的辨识精度要高于标准的递推最小二乘法，各参数估计值的误差均在4%以下，并且额外增加的计算量较少。辨识实验的结果也证明了辅助变量递推最小二乘法能够在有色噪声干扰下辨识出较为精确的系统模型。
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| 关 键 词： | 永磁同步直线电机 系统辨识 辅助变量 最小二乘法 固定模型变回归估计法 |
Identification of Permanent Magnet Synchronous Linear Motor System Based on Recursive Instrumental Variable Method |
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| LIANG Jianqiang,,WU Jinzhou,WEI Wei.Identification of Permanent Magnet Synchronous Linear Motor System Based on Recursive Instrumental Variable Method[J].Micromotors,2021,0(11):52-57+102. |
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| Authors: | LIANG Jianqiang WU Jinzhou WEI Wei |
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| Affiliation: | (1. Institute of Mechanical Manufacturing Technology, China Academy of Engineering Physics , Mianyang 621900, China; 2. School of Mechanical Science and Engineering , Huazhong University of Science and Technology , Wuhan 430074, China) |
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| Abstract: | Aiming at identification of permanent magnet synchronous linear motor system, a model parameter identification method based on instrumental variables was proposed. The mathematical model of permanent magnet synchronous linear motor and the open loop transfer function of the system were established by analyzing the circuit and dynamics. The standard recursive least square method was improved by introducing instrumental variables, and the parameters of the system model with colored noise data were identified. Meanwhile, aiming at the possible pure delay process in the system, the variable regression analys is method based on fixed model (FMVRE) was used to determine the delay factor. The simulation results show that with the influence of colored noise, the identification accuracy of the recursive instrumental variable least square method is higher than the standard recursive least square method . The error of each parameter estimation by the recursive instrumental variable least square method is less than 4%, and the additional calculation is less. The experimental results also prove that the recursive instrumental variable least square method can effectively identify the system model under colored noise interference. |
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| Keywords: | permanent magnet synchronous linear motor system identification instrumental variable least-square method variable regression analysis method based on fixed model |
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