An Online Learning Procedure for Feedback Linearization Control Without Torque Measurements
Abstract
By exploiting an a-priori estimate of the dynamic model of a manipulator, it is possible to command joint torques which ideally realize a Feedback Linearization (FL) controller. The exact cancellation may nevertheless not be achieved due to model uncertainties and possible errors in the estimation of the dynamic coefficients. In this work, an online learning scheme for control based on FL is presented. By reading joint positions and joint velocities information only (without the use of any torque measurement), we are able to learn those model uncertainties and thus achieve perfect FL control. Simulations results on the popular KUKA LWR iiwa robot are reported to show the quality of the proposed approach.
Cite
Text
Capotondi et al. "An Online Learning Procedure for Feedback Linearization Control Without Torque Measurements." Conference on Robot Learning, 2019.Markdown
[Capotondi et al. "An Online Learning Procedure for Feedback Linearization Control Without Torque Measurements." Conference on Robot Learning, 2019.](https://mlanthology.org/corl/2019/capotondi2019corl-online/)BibTeX
@inproceedings{capotondi2019corl-online,
title = {{An Online Learning Procedure for Feedback Linearization Control Without Torque Measurements}},
author = {Capotondi, M. and Turrisi, G. and Gaz, C. and Modugno, V. and Oriolo, G. and De Luca, A.},
booktitle = {Conference on Robot Learning},
year = {2019},
pages = {1359-1368},
volume = {100},
url = {https://mlanthology.org/corl/2019/capotondi2019corl-online/}
}