%0 Generic %1 steinle2024replication %A Steinle, Lukas %D 2024 %K darus ubs_10007 ubs_20011 ubs_30110 ubs_40167 unibibliografie %R 10.18419/darus-3759 %T Replication Data for: Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives %X This dataset contains all experimental data that is shown within the paper "Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives". Rack-and-pinion drives are commonly used in large machine tools to provide linear motion of heavy loads over long travel distances. A key concern in this context is the achievable path accuracy, which is limited by assembly and manufacturing tolerances of the gearing components in conjunction with load-dependent deformation and the inherent backlash of the system. To address this issue, this paper presents a method for robust modeling of the individual and state-dependent transmission errors of a drive utilizing a two-stage machine learning approach. Based on this, the position control is extended to include an error compensation, which suppresses the modeled deviations in the mechanical system including the position-dependent backlash. The achievable increase in path accuracy as well as the robustness of the approach are evaluated and quantified by an experimental validation on a system with industry standard components. The data are structured to correspond to the figures in the publication and are available in TAB or Excel format:Fig. 2 TE measurements:Measured transmission errors of the examined rack-and-pinion drive in both directions of motion under varying external load.Fig. 4 Path errors: Comparison of calculated and measured path errors for different velocities with no external load.Fig. 6 Model training: Training data for the deformation regression models and the predictions of the trained neural network and the regression tree ensemble.Fig. 8 Compensation validation sine: Evaluation of the compensation of the transmission errors and backlash for a sinusoidal trajectory.Fig. 9 Compensation validation overall: Evaluation of the improvement of the path accuracy by the compensation for varying loads and velocities.

@misc{steinle2024replication, abstract = {This dataset contains all experimental data that is shown within the paper "Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives". Rack-and-pinion drives are commonly used in large machine tools to provide linear motion of heavy loads over long travel distances. A key concern in this context is the achievable path accuracy, which is limited by assembly and manufacturing tolerances of the gearing components in conjunction with load-dependent deformation and the inherent backlash of the system. To address this issue, this paper presents a method for robust modeling of the individual and state-dependent transmission errors of a drive utilizing a two-stage machine learning approach. Based on this, the position control is extended to include an error compensation, which suppresses the modeled deviations in the mechanical system including the position-dependent backlash. The achievable increase in path accuracy as well as the robustness of the approach are evaluated and quantified by an experimental validation on a system with industry standard components. The data are structured to correspond to the figures in the publication and are available in TAB or Excel format:Fig. 2 TE measurements:Measured transmission errors of the examined rack-and-pinion drive in both directions of motion under varying external load.Fig. 4 Path errors: Comparison of calculated and measured path errors for different velocities with no external load.Fig. 6 Model training: Training data for the deformation regression models and the predictions of the trained neural network and the regression tree ensemble.Fig. 8 Compensation validation sine: Evaluation of the compensation of the transmission errors and backlash for a sinusoidal trajectory.Fig. 9 Compensation validation overall: Evaluation of the improvement of the path accuracy by the compensation for varying loads and velocities. }, added-at = {2024-03-25T15:12:44.000+0100}, affiliation = {Steinle, Lukas/Universität Stuttgart}, author = {Steinle, Lukas}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/27e40c4ebbb535c5c2d35833b1f7794d7/unibiblio}, doi = {10.18419/darus-3759}, howpublished = {Dataset}, interhash = {cafcd7b12680d6e4a37b1ba6fd82ae3d}, intrahash = {7e40c4ebbb535c5c2d35833b1f7794d7}, keywords = {darus ubs_10007 ubs_20011 ubs_30110 ubs_40167 unibibliografie}, note = {Related to: L. Steinle, V. Leipe, A. Lechler and A. Verl, "Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives", 2024 European Control Conference (ECC), Stockholm, Sweden, 2024}, orcid-numbers = {Steinle, Lukas/0000-0002-6035-0067}, timestamp = {2024-03-25T15:12:44.000+0100}, title = {Replication Data for: Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives}, year = 2024 }