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2023 (English)In: 2023 IEEE International Conference on Robotics and Automation (ICRA) / [ed] Marcia K. O'Malley, IEEE , 2023, p. 11432-11438Conference paper, Published paper (Refereed)
Abstract [en]
The control system of industrial robots is often model-based, and the quality of the model of high importance. Therefore, a fast and easy-to-use process for finding the model parameters from a combination of prior knowledge and measurement data is required. It has been shown that the experiment design can be improved in terms of short experiment times and an accurate parameter estimate if the robot configurations for the identification experiments are selected carefully. Estimates of the information matrix can be generated based on simulations for a number of candidate configurations, and an optimization problem can be solved for finding the optimal configurations. This work shows that the proposed method for improved experiment design works with a real manipulator, i.e. it is demonstrated that the experiment time is reduced significantly and the accuracy of the parameter estimate can be maintained or reduced if experiments are conducted only in the optimal manipulator configurations. It is also shown that the model improvement is relevant for realizing accurate control. Finally, the experimental data reveals that, in order to further improve the model accuracy, a more advanced model structure is needed for taking into account the commonly present nonlinear transmission stiffness of the robotic joints.
Place, publisher, year, edition, pages
IEEE, 2023
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-196489 (URN)10.1109/icra48891.2023.10161092 (DOI)001048371103078 ()9798350323658 (ISBN)9798350323665 (ISBN)
Conference
IEEE International Conference on Robotics and Automation (ICRA), 29th May - 2nd June 2023, ExCel London
Note
Funding: Vinnova competence center LINK-SIC
2023-08-092023-08-092023-10-11