Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7574
Title: Learning control of robot manipulators in task space
Authors: Doğan, Kadriye Merve
Tatlıcıoğlu, Enver
Zergeroğlu, Erkan
Çetin, Kamil
Keywords: Learning control
Robot manipulators
Task space control
Controllers
Learning algorithms
Robot applications
Issue Date: May-2018
Publisher: John Wiley and Sons Inc.
Source: Doğan, K. M., Tatlıcıoğlu, E., Zergeroğlu, E., and Çetin, K. (2018). Learning control of robot manipulators in task space. Asian Journal of Control, 20(3), 1003-1013. doi:10.1002/asjc.1648
Abstract: Two important properties of industrial tasks performed by robot manipulators, namely, periodicity (i.e., repetitive nature) of the task and the need for the task to be performed by the end-effector, motivated this work. Not being able to utilize the robot manipulator dynamics due to uncertainties complicated the control design. In a seemingly novel departure from the existing works in the literature, the tracking problem is formulated in the task space and the control input torque is aimed to decrease the task space tracking error directly without making use of inverse kinematics at the position level. A repetitive learning controller is designed which “learns” the overall uncertainties in the robot manipulator dynamics. The stability of the closed-loop system and asymptotic end-effector tracking of a periodic desired trajectory are guaranteed via Lyapunov based analysis methods. Experiments performed on an in-house developed robot manipulator are presented to illustrate the performance and viability of the proposed controller.
URI: https://doi.org/10.1002/asjc.1648
https://hdl.handle.net/11147/7574
ISSN: 1561-8625
1561-8625
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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