The Library
Calibration of a Stewart platform by designing a robust joint compensator with artificial neural networks
Tools
Song, Yongbin, Tian, Wenjie, Tian, Yanling and Liu, Xianping (2022) Calibration of a Stewart platform by designing a robust joint compensator with artificial neural networks. Precision Engineering, 77 . pp. 375-384. doi:10.1016/j.precisioneng.2022.07.001 ISSN 0141-6359.
|
PDF
WRAP-Calibration-Stewart-platform-designing-robust-joint-compensator-neural-networks-22.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (831Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.precisioneng.2022.07.0...
Abstract
By taking a Stewart platform as an example, this paper presents a novel calibration method by designing a robust joint compensator based on artificial neural networks. In this method, the pose error arising from various time-independent error sources is treated as that produced only by configuration-dependent joint motion errors equivalently, thus allowing the static pose error to be eliminated by directly correcting the nominal joint variables. Hence, the calibration procedure can be implemented in three successive steps: (1) acquisition of necessary joint corrections with point measurement at finite configurations considering near singularity problems, (2) approximation of the function between joint corrections and nominal joint variables using feedforward neural networks with coupled/decoupled architectures, and (3) design of a joint compensator embedded in the numerical control system to realize online real-time error compensation. Experimental results show that the proposed robust compensator based on coupled or decoupled networks can significantly improve the static pose accuracy in comparison with previous methods.
Item Type: | Journal Article | ||||||||
---|---|---|---|---|---|---|---|---|---|
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Journal or Publication Title: | Precision Engineering | ||||||||
Publisher: | Elsevier Science Inc. | ||||||||
ISSN: | 0141-6359 | ||||||||
Official Date: | September 2022 | ||||||||
Dates: |
|
||||||||
Volume: | 77 | ||||||||
Page Range: | pp. 375-384 | ||||||||
DOI: | 10.1016/j.precisioneng.2022.07.001 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 10 August 2022 | ||||||||
Date of first compliant Open Access: | 12 July 2023 |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year