The drivetrain flexibility of industrial robots limits their accuracy. To open up new areas of application for industrial robots, an increased dynamic path accuracy has to be obtained. Therefore, this paper addresses this issue by a gain-scheduled drive-based damping control for industrial robots with secondary encoders. For this purpose, a linear parameter-varying (LPV) model is derived as well as a system identification method is presented. Based on this, a gain-scheduled drive-based LPV damping control design is proposed, which guarantees stability and performance under variation of the manipulator configuration. The control performance of the approach is experimentally validated for the three base joints of a KUKA KR210-2 industrial robot. The approach realizes a trade-off between ease of implementation and control performance as well as robustness.
%0 Conference Paper
%1 9863417
%A Mesmer, Patrick
%A Hinze, Christoph
%A Lechler, Armin
%A Verl, Alexander
%B 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
%D 2022
%K isw
%P 1706-1713
%R 10.1109/AIM52237.2022.9863417
%T Gain-Scheduled Drive-based Damping Control for Industrial Robots
%X The drivetrain flexibility of industrial robots limits their accuracy. To open up new areas of application for industrial robots, an increased dynamic path accuracy has to be obtained. Therefore, this paper addresses this issue by a gain-scheduled drive-based damping control for industrial robots with secondary encoders. For this purpose, a linear parameter-varying (LPV) model is derived as well as a system identification method is presented. Based on this, a gain-scheduled drive-based LPV damping control design is proposed, which guarantees stability and performance under variation of the manipulator configuration. The control performance of the approach is experimentally validated for the three base joints of a KUKA KR210-2 industrial robot. The approach realizes a trade-off between ease of implementation and control performance as well as robustness.
@inproceedings{9863417,
abstract = {The drivetrain flexibility of industrial robots limits their accuracy. To open up new areas of application for industrial robots, an increased dynamic path accuracy has to be obtained. Therefore, this paper addresses this issue by a gain-scheduled drive-based damping control for industrial robots with secondary encoders. For this purpose, a linear parameter-varying (LPV) model is derived as well as a system identification method is presented. Based on this, a gain-scheduled drive-based LPV damping control design is proposed, which guarantees stability and performance under variation of the manipulator configuration. The control performance of the approach is experimentally validated for the three base joints of a KUKA KR210-2 industrial robot. The approach realizes a trade-off between ease of implementation and control performance as well as robustness.},
added-at = {2022-08-29T08:48:42.000+0200},
author = {Mesmer, Patrick and Hinze, Christoph and Lechler, Armin and Verl, Alexander},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2502d9e852819a8ead36b534fb5364f92/isw-bibliothek},
booktitle = {2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)},
doi = {10.1109/AIM52237.2022.9863417},
interhash = {cf8213549e765db032c2d7807c8cf275},
intrahash = {502d9e852819a8ead36b534fb5364f92},
issn = {2159-6255},
keywords = {isw},
month = {July},
pages = {1706-1713},
timestamp = {2022-08-29T06:48:42.000+0200},
title = {Gain-Scheduled Drive-based Damping Control for Industrial Robots},
year = 2022
}
