Optical Instruments, Volume. 45, Issue 1, 32(2023)
Application of XGBoost machine learning in error compensation of photoelectric encoder
Figures & Tables(9)
Fig. 2. Structure of error detection system for reference photoelectric encoder
Table 1. Encoder error detection results
View tableView in ArticleTable 1. Encoder error detection results
角度/（°） 转角误差均值/（″） 角度/（°） 转角误差均值/（″） 0 0 180 −4.44 15 −4.81 195 −3.70 30 −3.02 210 −4.94 45 5.28 225 −4.12 60 4.11 240 2.19 75 −3.99 255 −0.12 90 1.31 270 3.09 105 4.59 285 4.25 120 4.93 300 3.11 135 3.30 315 1.27 150 −0.07 330 5.53 165 −2.16 345 −1.20
Table 2. Comparison of compensation effect of each compensation system
View tableView in ArticleTable 2. Comparison of compensation effect of each compensation system
系统 静态精度/ （″） 标准差/ （″） 最大误差值/ （″） 补偿前 3.15 3.62 5.53 BP神经网络补偿系统 0.51 0.77 1.59 RBF神经网络补偿系统 0.19 0.31 0.87 XGBoost补偿系统 0.09 0.13 0.39
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Yingzheng LI, Zhibin LI, Lei JIN, Zhenzhen HU, Yefei KANG, Gengbai LI. Application of XGBoost machine learning in error compensation of photoelectric encoder[J]. Optical Instruments, 2023, 45(1): 32
Paper Information
Category: APPLICATION TECHNOLOGY
Received: Jul. 5, 2022
Accepted: --
Published Online: Mar. 20, 2023
The Author Email: LI Zhibin (thermal_li@163.com)
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