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A hybrid model for pre-compensating servo error in the ball screw system based on high-bandwidth controller.
- Source :
- CIRP: Journal of Manufacturing Science & Technology; Sep2024, Vol. 52, p175-187, 13p
- Publication Year :
- 2024
-
Abstract
- This article presents a hybrid model to predict the positions of the ball screw drive system of machine tool and then modify the trajectory through constructing a pre-compensation method to reduce servo errors in machine motion axes. To achieve this objective, a flexible control model is initially developed to characterize the ball screw drive system, and by leveraging this model, a high-bandwidth controller is constructed, with its physical representation, i.e. the state-space equation, being derived. Subsequently, a data-driven hybrid model is proposed to predict the positions of the ball screw drive system concerning the next multiple time steps from the current time step, and then the predicted positions associated with these steps are utilized as initial conditions to adjust and compensate for the physical model's prediction errors corresponding to these multiple time steps. As a result, a compensated trajectory with high tracking accuracy is generated. Finally, experiments confirm that the proposed prediction method offers superior prediction accuracy and enhanced adaptability, and the pre-compensated trajectory leads to reduced tracking errors. [Display omitted] • A hybrid model is constructed to pre-compensate servo error in the ball screw system. • A high bandwidth controller is designed by incorporating the feedback of the velocity. • A compensated trajectory with high tracking accuracy is generated. • The method can achieve a nice tracking accuracy and improve the bandwidth. • A series of comparative experiments validate the proposed method. [ABSTRACT FROM AUTHOR]
- Subjects :
- SCREWS
MACHINE tools
PREDICTION models
Subjects
Details
- Language :
- English
- ISSN :
- 17555817
- Volume :
- 52
- Database :
- Supplemental Index
- Journal :
- CIRP: Journal of Manufacturing Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 178400649
- Full Text :
- https://doi.org/10.1016/j.cirpj.2024.06.002