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.Article Citation - WoS: 5Citation - Scopus: 5Robust Output Tracking Control of an Unmanned Aerial Vehicle Subject To Additive State Dependent Disturbance(Institution of Engineering and Technology, 2016) Tanyer, İlker; Tatlıcıoglu, Enver; Zergeroglu, Erkan; Deniz, Meryem; Bayrak, Alper; Özdemirel, BarbarosIn this study, an asymptotic tracking controller is developed for an aircraft model subject to additive, state-dependent, non-linear disturbance-like terms. Dynamic inversion technique in conjunction with robust integral of the sign of the error term is utilised in the controller design. Compared to the previous studies, the need of acceleration measurements of the aircraft have been removed. In addition, the proposed controller design utilises only the output of aircraft dynamics. Lyapunov based analysis is applied to prove global asymptotic convergence of the tracking error signal. Numerical simulation results are presented to illustrate the performance of the proposed robust 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.Conference Object Citation - WoS: 23Citation - Scopus: 23A self tuning RISE controller formulation(Institute of Electrical and Electronics Engineers Inc., 2014) Bıdıklı, Barış; Tatlıcıoğlu, Enver; Zergeroğlu, ErkanIn recent years, controller formulations using robust integral of sign of error (RISE) type feedback have been successfully applied to a variety of nonlinear dynamical systems. The drawback of these type of controllers however, are (i) the need of prior knowledge of the upper bounds of the system uncertainties and (ii) the absence of a proper gain tuning methodology. To tackle the aforementioned weaknesses, in our previous work [1] we have presented a RISE formulation with a time-varying compensation gain to cope for the need of upper bound of the uncertain system. In this study, we have extended our previous design to obtain a fully self tuning RISE feedback formulation. Lyapunov based arguments are applied to prove overall system stability and extensive numerical simulation studies are presented to illustrate the performance of the proposed method. © 2014 American Automatic Control Council.