Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/11

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Author: search.filters.author.Zergeroǧlu, ErkanWoS Q: search.filters.wosQuartile.Q3

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  • Article
    Citation - WoS: 4
    Citation - Scopus: 7
    On Operational Space Tracking Control of Robotic Manipulators With Uncertain Dynamic and Kinematic Terms
    (American Society of Mechanical Engineers, 2019) Çetin, Kamil; Tatlıcıoğlu, Enver; Zergeroǧlu, Erkan
    In this study, a continuous robust-adaptive operational space controller that ensures asymptotic end-effector tracking, despite the uncertainties in robot dynamics and on the velocity level kinematics of the robot, is proposed. Specifically, a smooth robust controller is applied to compensate the parametric uncertainties related to the robot dynamics while an adaptive update algorithm is used to deal with the kinematic uncertainties. Rather than formulating the tracking problem in the joint space, as most of the previous works on the field have done, the controller formulation is presented in the operational space of the robot where the actual task is performed. Additionally, the robust part of the proposed controller is continuous ensuring the asymptotic tracking and relatively smooth controller effort. The stability of the overall system and boundedness of the closed loop signals are ensured via Lyapunov based arguments. Experimental results are presented to illustrate the feasibility and performance of the proposed method.
  • Article
    Citation - WoS: 9
    Citation - Scopus: 11
    Neural Network-Based Repetitive Learning Control of Euler Lagrange Systems: an Output Feedback Approach
    (IEEE, 2018) Tatlıcıoğlu, Enver; Çobanoğlu, Necati; Zergeroǧlu, Erkan
    In this letter, position tracking control problem of a class of fully actuated Euler Lagrange (EL) systems is aimed. The reference position vector is considered to be periodic with a known period. Only position measurements are available for control design while velocity measurements are not. Furthermore, the dynamic model of the EL systems has parametric and/or unstructured uncertainties which avoid it to be used as part of the control design. To address these constraints, an output feedback neural network-based repetitive learning control strategy is preferred. Via the design of a dynamic model independent velocity observer, the lack of velocity measurements is addressed. To compensate for the lack of dynamic model knowledge, universal approximation property of neural networks is utilized where an online adaptive update rule is designed for the weight matrix. The functional reconstruction error is dealt with the design of a novel repetitive learning feedforward term. The outcome is a dynamic model independent output feedback neural network-based controller with a repetitive learning feedforward component. The stability of the closed-loop system is investigated via rigorous mathematical tools with which semi-global asymptotic stability is ensured. © 2017 IEEE.