Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 16Citation - Scopus: 16Learning Control of Robot Manipulators in Task Space(John Wiley and Sons Inc., 2018) Doğan, Kadriye Merve; Tatlıcıoğlu, Enver; Zergeroğlu, Erkan; Çetin, KamilTwo 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.Conference Object Citation - WoS: 6Citation - Scopus: 7Lyapunov-Based Output Feedback Learning Control of Robot Manipulators(Institute of Electrical and Electronics Engineers Inc., 2015) Doğan, Kadriye Merve; Tatlıcıoğlu, Enver; Zergeroğlu, Erkan; Çetin, KamilThis paper address the output feedback learning tracking control problem for robot manipulators with repetitive desired joint level trajectories. Specifically, an observer-based output feedback learning controller for periodic trajectories with known period have been proposed. The proposed learning controller guarantees semi-global asymptotic tracking despite the existence of parametric uncertainties associated with the robot dynamics and lack of velocity measurements. A learning-based feedforward term in conjunction with a novel observer formulation is designed to obtain the aforementioned result. The stability of the controller-observer couple is guaranteed via Lyapunov based arguments. Numerical studies performed on a two link robot manipulator are also presented to demonstrate the viability of the proposed method. © 2015 American Automatic Control Council.