Motor Frequency Estimation by using Instrumental Variable method

被引:0
|
作者
Kim, Yong Hwi [1 ]
Choi, Ka Hyung [1 ]
Yoon, Tae Sung [2 ]
Parkh, Jin Bae [1 ]
机构
[1] Yonsei Univ, Dept Elect Engn, Seoul 120749, South Korea
[2] Changwon Natl Univ, Dept Elect Engn, Chang Won 641773, South Korea
来源
2013 13TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2013) | 2013年
关键词
Frequency estimation; Instrumental variable; Weighted robust least squares; Bias estimation;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Motion control is an important task in industrial automation systems. And the exact motor speed estimation is needed for precise motion control. To obtain the motor speed, linear hall sensor is used in this paper for implementation as a low cost and a simple calculation. Since the linear hall sensor output is sinusoid wave, the measurement equation can be modeled with a sinusoid signal easily. Based on the model, the instrumental variable (IV) method is proposed to estimate the motor frequency in this paper. To prove its performance, the estimation from IV is compared with those from the nominal least squares (NoLS), weighted robust least squares (WRLS), and true value. Experimental results show that the IV method is superior to the NoLS algorithm, and similar with the WRLS algorithm. Moreover, the proposed IV method is useful because it can be applied even if the stochastic properties are unknown or not exact.
引用
收藏
页码:1274 / 1276
页数:3
相关论文
共 50 条
  • [21] Composite quantile regression estimation of linear error-in-variable models using instrumental variables
    Weiming Yang
    Yiping Yang
    Metrika, 2020, 83 : 1 - 16
  • [22] Instrumental Variable Analysis for Estimation of Treatment Effects With Dichotomous Outcomes
    Rassen, Jeremy A.
    Schneeweiss, Sebastian
    Glynn, Robert J.
    Mittleman, Murray A.
    Brookhart, M. Alan
    AMERICAN JOURNAL OF EPIDEMIOLOGY, 2009, 169 (03) : 273 - 284
  • [23] Learning Conditional Instrumental Variable Representation for Causal Effect Estimation
    Cheng, Debo
    Xu, Ziqi
    Li, Jiuyong
    Liu, Lin
    Thuc Duy Le
    Liu, Jixue
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES: RESEARCH TRACK, ECML PKDD 2023, PT I, 2023, 14169 : 525 - 540
  • [24] INSTRUMENTAL VARIABLE AND GMM ESTIMATION FOR PANEL DATA WITH MEASUREMENT ERROR
    Xiao, Zhiguo
    Shao, Jun
    Palta, Mari
    STATISTICA SINICA, 2010, 20 (04) : 1725 - 1747
  • [25] Networked Instrumental Variable for Treatment Effect Estimation With Unobserved Confounders
    Zhao, Ziyu
    Wu, Anpeng
    Kuang, Kun
    Xiong, Ruoxuan
    Li, Bo
    Wang, Zhihua
    Wu, Fei
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2025, 37 (02) : 823 - 836
  • [26] On instrumental variable estimation of semiparametric dynamic panel data models
    Baltagi, BH
    Li, Q
    ECONOMICS LETTERS, 2002, 76 (01) : 1 - 9
  • [27] Bayesian instrumental variable estimation in linear measurement error models
    Wang, Qi
    Wang, Lichun
    Wang, Liqun
    CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2024, 52 (02): : 500 - 531
  • [28] Semiparametric Bayes instrumental variable estimation with many weak instruments
    Kato, Ryo
    Hoshino, Takahiro
    STAT, 2021, 10 (01):
  • [29] Instrumental Variable Estimation of Dynamic Treatment Effects on a Duration Outcome
    Beyhum, Jad
    Centorrino, Samuele
    Florens, Jean-Pierre
    Van Keilegom, Ingrid
    JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2024, 42 (02) : 732 - 742
  • [30] Instrumental variable estimation in ordinal probit models with mismeasured predictors
    Guan, Jing
    Cheng, Hongjian
    Bollen, Kenneth A.
    Thomas, D. Roland
    Wang, Liqun
    CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2019, 47 (04): : 653 - 667