Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Conference Object A New Continuous Velocity Observer Formulation for a Class of Uncertain Nonlinear Mechanical Systems(Institute of Electrical and Electronics Engineers, 2015) Bayrak, Alper; Tatlıcıoğlu, Enver; Zergeroǧlu, Erkan; Deniz, MeryemIn this study, we present a smooth robust velocity observer for a class of uncertain nonlinear mechanical systems. The smoothness of the observers is guaranteed by utilizing hyperbolic tangent function as opposed to signum-type functions applied in most robust and sliding mode observers found in the literature. The proposed observer does not require a priori knowledge of an upper bound of the uncertain system dynamics and introduces a time-varying observer gain for uncertainty compensation. Practical stability of the observer error is ensured via Lyapunov-type stability analysis. Numerical simulation studies backed up by experimental results are presented to illustrate the performance of the proposed observer.Conference Object Citation - WoS: 4Citation - Scopus: 5Backstepping Control of Electro-Hydraulic Arm(Institute of Electrical and Electronics Engineers, 2018) Bayrak, Alper; Tatlıcıoğlu, Enver; Zergeroǧlu, ErkanIn this study, positioning control of the electro hydraulic systems is considered. Backstepping control strategy is designed by defining an auxiliary error signal. The performance of the controller is investigated by conducting numerical simulations. From the simulation results, it is seen that the control objective achieved successfully. The performance is compared with PI controller via a comparison criteria and it is seen that the backstepping controller has better results in both error and controller performance aspects.Article Citation - WoS: 4Citation - Scopus: 7On Operational Space Tracking Control of Robotic Manipulators With Uncertain Dynamic and Kinematic Terms(American Society of Mechanical Engineers, 2019) Çetin, Kamil; Tatlıcıoğlu, Enver; Zergeroǧlu, ErkanIn 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: 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.Article Citation - WoS: 6Citation - Scopus: 7Neural Network Based Robust Control of an Aircraft(ACTA Press, 2020) Tanyer, İlker; Tatlıcıoğlu, Enver; Zergeroǧlu, ErkanOutput tracking control of an aircraft subject to uncertainties in the dynamic model and additive state-dependent nonlinear disturbancelike terms is aimed. Uncertainties in the aircraft dynamic model yield an uncertain input gain matrix, which is neither positive definite nor symmetric and an uncertain term in the error dynamics. To deal with the uncertain input gain matrix, a decomposition method is utilized to put error dynamics in a form where an uncertain positive definite matrix multiplies the auxiliary error but this results in the control input to be pre-multiplied first with a unity upper triangular matrix which is uncertain and then with a known diagonal matrix. A novel controller composed of a neural network compensation term and an integral of signum of error is designed. A novel Lyapunov type stability analysis is utilized to prove global asymptotic tracking of output of a reference model. Extensive numerical simulations are presented to demonstrate the efficacy of the proposed controller where robustness to variation of initial states and a comparison with a robust controller are also shown. © 2020 Acta Press. All rights reserved.