Argüz, Serdar Hakan

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Profile URL
https://hdl.handle.net/20.500.14365/7447
Name Variants
Arguz, Serdar Hakan
Job Title
Email Address
serdar.arguz@ieu.edu.tr
Main Affiliation
05.11. Mechatronics Engineering
Status
Former Staff
Website
ORCID ID
https://orcid.org/0000-0002-3611-0733
Scopus Author ID
57806765200
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
GRT-4158-2022

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3

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11

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10

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3

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1

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WoS Citation Count

10

Scopus Citation Count

11

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2

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2

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0

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0

WoS Citations per Publication

3.33

Scopus Citations per Publication

3.67

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2

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0

JournalCount
2022 8Th Internatıonal Conference on Control, Decısıon And Informatıon Technologıes (Codıt'22)1
9th 2023 International Conference on Control, Decision and Information Technologies, CoDIT 20231
Robotıca1
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  • Thumbnail Image
    Conference Object
    Citation - WoS: 3
    Citation - Scopus: 3
    Experimental Evaluation of the Success of Peg-In Tasks Learned From Demonstration
    (IEEE, 2022) Arguz, Serdar Hakan; Ertugrul, Seniz; Altun, Kerem
    Industrial robots are traditionally programmed by hard-coding the desired motion into them. That approach, however, costs significant time and effort and shows little to no promise in transferring human skills to robots. Programming by demonstration (PbD) is an alternative approach that allows robots to learn tasks from demonstrations. Because of its several advantages over the traditional method, PbD is particularly suited for tasks encountered in assembly operations, the most typical of which is the peg-in-hole task. A successful PbD implementation for a peg-in-hole task requires that the peg should still be inserted into the hole even under situations that are not encountered during the demonstrations. Previous research in the field shows that the success rate of a peg-in-hole task under such cases varies greatly. In this study, we use a UR5 manipulator to experimentally investigate how the success rate of a peg-in-hole task changes with respect to the novelty of the task, quantified in terms of the distance of the hole to its original position. It is found that the success ratio decreases as the novelty of the task increases. To increase the performance, the use of strategies that alter the robot's motion dynamically in the run time is suggested for future work.
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    Article
    Citation - WoS: 7
    Citation - Scopus: 6
    Nonlinear Model Identification and Statistical Verification Using Experimental Data With a Case Study of the Ur5 Manipulator Joint Parameters
    (Cambridge Univ Press, 2023) Abedinifar, Masoud; Ertugrul, Seniz; Arguz, Serdar Hakan
    The identification of nonlinear terms existing in the dynamic model of real-world mechanical systems such as robotic manipulators is a challenging modeling problem. The main aim of this research is not only to identify the unknown parameters of the nonlinear terms but also to verify their existence in the model. Generally, if the structure of the model is provided, the parameters of the nonlinear terms can be identified using different numerical approaches or evolutionary algorithms. However, finding a non-zero coefficient does not guarantee the existence of the nonlinear term or vice versa. Therefore, in this study, a meticulous investigation and statistical verification are carried out to ensure the reliability of the identification process. First, the simulation data are generated using the white-box model of a direct current motor that includes some of the nonlinear terms. Second, the particle swarm optimization (PSO) algorithm is applied to identify the unknown parameters of the model among many possible configurations. Then, to evaluate the results of the algorithm, statistical hypothesis and confidence interval tests are implemented. Finally, the reliability of the PSO algorithm is investigated using experimental data acquired from the UR5 manipulator. To compare the results of the PSO algorithm, the nonlinear least squares errors (NLSE) estimation algorithm is applied to identify the unknown parameters of the nonlinear models. The result shows that the PSO algorithm has higher identification accuracy than the NLSE estimation algorithm, and the model with identified parameters using the PSO algorithm accurately calculates the output torques of the joints of the manipulator.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Nonlinear Model Identification of a Ball and Beam Mechanism Using Experimental Data
    (Institute of Electrical and Electronics Engineers Inc., 2023) Abedinifar, M.; Ertuğrul, Şeniz; Argüz, S.H.
    A ball and beam mechanism is widely utilized in laboratory experiments to demonstrate the behavior of more complex systems. In this research, the phenomena such as nonlinear frictions, dead-zone and time-delay in the ball and beam mechanism's mathematical model is investigated. The following procedures are taken to construct a credible mathematical model of the system for this purpose. Firstly, the ball and beam mechanism's mathematical model, which includes different probable physically meaningful nonlinearities, is simulated using MATLAB\Simulink. Then, the Particle Swarm Optimization (PSO) algorithm is coded to determine the exact nonlinear model of a ball and beam system using the experimental data. Third, the accuracy of the results obtained from the PSO algorithm is tested using the hypothesis test and the confidence interval test. According to the statistical tests, the PSO algorithm is highly accurate in determining the parameters of the actual model of the system. © 2023 IEEE.