Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Language: search.filters.language.enSubject: search.filters.subject.Nonlinear model identification

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  • 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, S.; Argüz, S.H.; 01. Izmir Institute of Technology
    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.
  • 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 University Press, 2022) Abedinifar, Masoud; Ertuğrul, Şeniz; Argüz, Serdar Hakan; 01. Izmir Institute of Technology
    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.