Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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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.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.