Browsing by Author "Abedinifar, Masoud"

Now showing 1 - 3 of 3

Citation - WoS: 5
Citation - Scopus: 7
Design Optimization of a Solenoid Actuator Using Particle Swarm Optimization Algorithm With Multiple Objectives
(Sage Publications Ltd, 2022) Abedinifar, Masoud; Ertugrul, Seniz; Tayyar, Gokhan Tansel
Solenoid actuators are well-known components that convert electromagnetic energy into mechanical energy. For control purposes, it is requested to have a high magnetic force that stays almost constant in the working region of the actuator. To meet these requirements, it is necessary to have an optimal geometrical design of the actuator. In this study, the following steps are performed to optimize the geometry of the solenoid actuator. The Finite Element Analysis (FEA) is performed, and the results of the simulation is verified with the experimental data. The effect of all geometrical parameters on the characteristics of the magnetic force is investigated. The parameters that highly affect the magnetic force are chosen as design optimization parameters. Then, the Particle Swarm Optimization (PSO) algorithm is realized to find optimal parameters. The algorithm consists of two objective functions being combined into a single objective function. It includes a higher and more consistent magnetic force in the effective working region of the solenoid. Finally, the solenoid actuator with optimized parameters is manufactured, and the results are compared. They show that the optimized solenoid actuator satisfies one of the objective functions, and magnetic force stays almost constant in the working region of the solenoid actuator.
Non-Linear Friction Force Estimation for Ball and Beam Mechanism Using R-Pinn
(IEEE, 2025) 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.
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.