Browsing by Author "Ertugrul, Seniz"

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Citation - Scopus: 2
Comparative Study of Identification Using Nonlinear Least Squares Errors and Particle Swarm Optimization Algorithms for a Nonlinear Dc Motor Model
(Springer Science and Business Media Deutschland GmbH, 2021-08-24) Abedinifar M.; Ertugrul S.; Abedinifar, Masoud; Ertugrul, Seniz
For an accurate dynamic analysis of the real-world systems, there is an extensive demand for developing the mathematical models. An accurate mathematical model can be used for optimization, fault diagnosis, controller design, etc. Many studies have been performed for developing the mathematical models of the real-world systems. They commonly utilize linear models while ignoring the possible existing nonlinearities in the model. However, having a general mathematical model including nonlinearities has great significance in performance analysis and proper control of the systems. In this paper, two algorithms including Nonlinear Least Squares Errors (NLSE) and Particle Swarm Optimization (PSO) are utilized for model identification. For this aim, the nonlinear model of a Direct Current (DC) motor is used as a case study to compare the performance of the two algorithms. In the first step, a white-box mathematical model of the DC motor including the nonlinear friction terms is developed. Then, the artificial data is generated through the developed model with the real parameters of a DC motor. Finally, NLSE and PSO algorithms are carried out to determine the unknown parameters of the nonlinear model through generated artificial data. All unknown parameters of the model are identified at the same time. The results of the two algorithms are evaluated and compared. It is shown that the PSO algorithm determines the model parameters more accurately compared to the NLSE algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Experimental Evaluation of the Success of Peg-in-Hole Tasks Learned from Demonstration
(Institute of Electrical and Electronics Engineers Inc., 2022-05-17) Arguz, Serdar Hakan; Altun, Kerem; Ertugrul, Seniz
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.
Fault Diagnosis of an Electrohydraulic System by Using Fuzzy C-Means Clustering
(Springer International Publishing Ag, 2024) Guner, Hakan; Ertugru, Seniz; Tayyar, Gokhan Tansel; Ertugrul, Seniz
Hydraulic systems typically operate under harsh conditions, such as in the heavy industry and military domain. Early diagnosis of single or multiple faults is very important to keep the system in safe working conditions. To generate different faults, an exhaustive simulation stage is required before validating the study with an experimental setup. Therefore, in this study, an electrohydraulic system model was first created using the Matlab-Simscape hydraulic system library. After the simulation stage, a data-based fault diagnosis method was applied through certain statistical feature calculations using time, frequency, and time-based frequency of the data collected using the Diagnostic Feature Designer Toolbox under theMatlab program, which allows the use of all signal and data-based debugging, identification, and classification methods under the same platform. Based on the features ranked by the Diagnostic Feature Designer Toolbox, the best fault model fits were investigated by clustering with Fuzzy C-Means.
Integrated Drive Train and Structural Optimization for a Dynamic System: An Evolving Conceptual Design Algorithm
(Institute of Electrical and Electronics Engineers Inc., 2022-05-17) Gulec, Musa Ozgun; Ertugrul, Seniz
Selecting the most suitable motor sizes, gear boxes and structure under certain constraints or desired values such as payload, speed, deflections, total weight, etc. for a dynamic system is an exhaustive and time-consuming iterative process. To overcome this problem, a newevolving conceptual design algorithm is developed. The suggested algorithm can be used for the conceptual design of any dynamic system including drive-train and structural optimization. To illustrate the suggested methodology, a robot manipulator, having 3 degrees of freedom, is selected as a case study. The objective function is minimizing the robot mass while satisfying the desired dynamic requirements and constraints of link deflections. A dynamic simulation environment for flexible body motion, containing 3 DOF robot manipulator drive-trains and flexible links, is developed in an evolving optimization loop. The lumped parameter estimation method is used to model the flexibility of uniform links in Simmechanics by allowing the estimation of deflections caused by the dynamic motion. Thus, both dynamic and structural simulations are made simultaneously in Simmechanics with no additional software. Hence, drive-trains and thickness of all links are simultaneously optimized by using the suggested evolving conceptual design algorithm.
Investigation of Backlash and Friction Nonlinearities in a 1-DoF Electromechanical System Based on Experimental Data
(2025-12-25) Ertugrul, Seniz; Abedinifar, Masoud
The characterization of nonlinearities, specifically backlash and friction, in one-degree-of-freedom (1-DoF) electromechanical systems is essential for achieving high-precision control. This study presents a systematic investigation into the identification of these phenomena using a white-box modeling approach. An experimental platform, consisting of a brushed DC motor with a gearbox and a 3D-printed L-shaped load arm, was developed to generate input-output data from sinusoidal voltage excitations. A comprehensive nonlinear model, developed in MATLAB/Simulink, incorporated electrical dynamics, Coulomb and viscous friction, gravitational torque, and backlash dead-zone effects. Two complementary parameter identification methods, Nonlinear Least Squares Errors (NLSE) estimation and a Genetic Algorithm (GA), were applied to estimate the model's unknown parameters. Results demonstrated that both approaches successfully captured the dominant system dynamics; however, NLSE achieved superior accuracy in both identification (RMSE = 0.13 rad/s, R2 = 0.99) and verification (RMSE = 0.16 rad/s, R2 = 0.96) phases, compared to GA (RMSE = 0.21-0.22 rad/s, R2 = 0.94-0.97). These findings demonstrate that, with identical initialization and constraints of system parameters, a physics-based white-box model combined with NLSE provides a more reliable and precise characterization of combined backlash and friction nonlinearities than GA for the investigated 1-DoF electromechanical system and excitation conditions.
Non-Linear Friction Force Estimation for Ball and Beam Mechanism Using R-Pinn
(IEEE, 2025-03-17) Kaya, Ozan; Ertugrul, Seniz; Abedinifar, Masoud; Egeland, Olav
Different friction forces or torques are affecting the system's performance and control. Friction forces occur due to bearings, gearboxes, or any other contacts in the system. Researchers have reported different forms of friction, such as stiction, viscous and Stribeck effects, pre-sliding displacement, stick-slip effects, hysteresis (or frictional lag), etc. Developing a mathematical model to describe the underlying dynamics of a complex system may become necessary to design either a modelbased controller or at least compensate for the non-linear effects of friction forces. For this reason, either test set-ups or datadriven techniques might be used. In this study, the RecurrentPhysics Informed Neural Network is studied to determine the friction forces and model the Ball and beam system. While PINN provides faster results to model non-linear systems with noisy and small data sizes, Recurrent Neural Network architecture is fruitful for modeling time-dependent systems. Thus, R-PINN is trained with noisy signals for system response and friction model of the ball and beam system. Despite noisy signals and nonlinearity in the system, R-PINN is promising in modeling the system response and estimating the friction model.