Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 9Citation - Scopus: 11Neural Network-Based Repetitive Learning Control of Euler Lagrange Systems: an Output Feedback Approach(IEEE, 2018) Tatlıcıoğlu, Enver; Çobanoğlu, Necati; Zergeroǧlu, ErkanIn 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.Conference Object Citation - WoS: 3Citation - Scopus: 4Neural Network Based Repetitive Learning Control of Robot Manipulators(Institute of Electrical and Electronics Engineers Inc., 2017) Çobanoğlu, Necati; Tatlıcıoğlu, Enver; Zergeroğlu, ErkanControl of robot manipulators performing periodic tasks is considered in this work. The control problem is complicated by presence of uncertainties in the robot manipulator's dynamic model. To address this restriction, a model free repetitive learning controller design is aimed. To reduce the heavy control effort, a neural network based compensation term is fused with the repetitive learning controller. The convergence of the tracking error to the origin is ensured via Lyapunov based techniques. Numerical simulations and experiments are performed to demonstrate the viability of the proposed controller.