Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Subject: search.filters.subject.Task space control

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  • Article
    Citation - WoS: 39
    Citation - Scopus: 43
    Self-Adjusting Fuzzy Logic Based Control of Robot Manipulators in Task Space
    (Institute of Electrical and Electronics Engineers Inc., 2021) Yılmaz, Bayram Melih; Tatlıcıoğlu, Enver; Savran, Aydoğan; Alcı, Musa
    End effector tracking control of robot manipulators subject to dynamical uncertainties is the main objective of this work. Direct task space control that aims minimizing the end effector tracking error directly is preferred. In the open loop error system, the vector that depends on uncertain dynamical terms is modeled via a fuzzy logic network and a self-adjusting adaptive fuzzy logic component is designed as part of the nonlinear proportional derivative based control input torque. The stability of the closed loop system is investigated via Lyapunov based arguments and practical tracking is proven. The viability of the proposed control strategy is shown with experimental results. Extensions to uncertain Jacobian case and kinematically redundant robots are also presented. IEEE
  • Conference Object
    Adaptive Actuator Failure Compensation for Cooperating Multiple Manipulator Systems
    (Elsevier, 2003) Keçeci, Emin Faruk; Tang, Xidong; Tao, Gang
    This paper presents adaptive actuator failure compensation for a cooperating multiple manipulator system with uncertain actuator failures in the task space. Advantages of designing control schemes in task spaces are emphasized, applications of task space control in robotics are discussed and a short review on control algorithms for cooperating multiple manipulator systems is given. Dynamic equations of motion of the multiple manipulator system in the task space are derived, and the adaptive actuator failure compensation problem is formulated. A compensation controller structure is proposed, for which adaptive parameter update laws are developed. The adaptive control scheme is able to compensate for the uncertainties arising from both the system parameters and the actuator failures. Based on Lyapunov stability analysis, the closed-loop signal boundedness and the convergence of the tracking error to zero are ensured. © 2003 International Federation of Automatic Control.
  • Article
    Citation - WoS: 16
    Citation - Scopus: 16
    Learning Control of Robot Manipulators in Task Space
    (John Wiley and Sons Inc., 2018) Doğan, Kadriye Merve; Tatlıcıoğlu, Enver; Zergeroğlu, Erkan; Çetin, Kamil
    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.