An RBF neural network approach to geometric error compensation with displacement measurements only

Rui Yang*, Kok Kiong Tan, Arthur Tay, Sunan Huang, Jie Sun, Jerry Fuh, Yoke San Wong, Chek Sing Teo, Zidong Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


A novel radial basis function (RBF) neural network-based geometric error compensation method with displacement measurements only is proposed in this paper. The individual geometric error components are formulated mathematically based on laser interferometer calibration with displacement measurements only and modeled using RBF neural network for error compensation in motion controller. Only 4 and 15 displacement measurements are required to identify the error components for XY and XYZ table, respectively. The experiment results on two XY tables illustrate the effectiveness of the proposed method. The overall errors can be reduced significantly after compensation, and different data intervals can be selected to reduce calibration time but maintain a high level of accuracy. The proposed methodology can be extended to other types of precision machine and is more suitable for precision machines requiring a relative low level of accuracy, but fast calibration like those used for acceptance testing and periodic checking.

Original languageEnglish
Pages (from-to)1235-1248
Number of pages14
JournalNeural Computing and Applications
Issue number6
Publication statusPublished - 1 Jun 2017
Externally publishedYes


  • Error compensation
  • Error estimation
  • Precision machine
  • Radial basis function network

Cite this