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| XGBoost机器学习在光电编码器误差补偿中的应用 |
| Application of XGBoost machine learning in error compensation of photoelectric encoder |
| 投稿时间：2022-07-05 |
| DOI：10.3969/j.issn.1005-5630.2023.001.005 |
| 中文关键词: 光电编码器 误差补偿 XGBoost 检测精度 |
| 英文关键词:photoelectric encoder error compensation XGBoost accuracy of detection |
| 基金项目:上海市电站自动化技术重点实验室项目(13DZ2273800) |
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| 摘要点击次数: 43 |
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| 中文摘要: |
| 光电编码器检测系统的误差主要受基准光电编码器测角误差、数据采集误差、检测系统同轴误差影响。其中，基准光电编码器的测角误差可进行补偿。因此设计了一种基于极度梯度提升树（extreme gradient boosting，XGBoost）机器学习的算法用来补偿基准光电编码器的误差。经该算法补偿后，静态精度提高了35倍，标准差由3.62″减小至0.13″，最大误差值由5.53″降低至0.39″。与传统的误差反传（back progagation，BP）神经网络算法以及径向基函数（radial basis function，RBF）神经网络算法补偿效果相比，XGBoost的补偿效果更优。XGBoost机器学习算法有效降低了基准光电编码器的测量误差，提高了光电编码器检测系统的检测精度。 |
| 英文摘要: |
| The error of photoelectric encoder detection system is mainly affected by the angle measurement error of the reference photoelectric encoder, data acquisition error and coaxial error. The angle measurement error can be compensated. In this paper, an algorithm based on extreme gradient boosting (XGBoost) machine learning is designed to compensate the error of the reference photoelectric encoder. After compensation, the static accuracy is improved by 35 times. The standard deviation is decreased from 3.62" to 0.13", and the maximum error value is reduced from 5.53" to 0.39". Compared with the traditional back progagation (BP) neural network algorithm and radial basis function (RBF) neural network algorithm, XGBoost's compensation is better than the others. XGBoost machine learning algorithm compensation effectively reduces the measurement error of the reference photoelectric encoder and improves the detection accuracy of the photoelectric encoder detection system. |
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