Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 1Citation - Scopus: 1Torque-Current Relationship of an Mr Brake for Its Open-Loop Control(Ieee-inst Electrical Electronics Engineers inc, 2024) Kucukoglu, Sefa Furkan; Bozelli, Muhammed Rza; Dede, Mehmet Ismet CanActive and semiactive actuators have been widely preferred for designing an actuation system for kinesthetic-type haptic devices. Among them, magnetorheological fluid-based brakes (MR brakes) offer potent properties, such as high torque/inertia ratio and less power consumption. However, one of the most critical issues to be resolved is their hysteresis behavior. Various methods for modeling the input/output relationship with hysteresis behavior exist. However, hysteresis compensation approaches, i.e., torque-current hysteresis model, are not widely studied for MR Brakes. Therefore, a hysteresis compensation model approach to account for the nonlinear behavior of MR Brake is proposed, and the model is experimentally validated in this article. The model consists of multiple splines and an algorithm that uses these splines in hysteresis compensation. Being relatively simple and easily implementable are the distinguished features of the presented model since an optimization method is not required. Furthermore, the performance of the proposed method is compared with two methods, torque-to-current mapping and inverse Prandtl-Ishlinskii method. The obtained experimental results are investigated with three performance metrics. Finally, the effect of the operational speed on the performance of the hysteresis compensation model is also discussed.Conference Object Citation - WoS: 1Citation - Scopus: 1Modeling a Magneto-Rheological Fluid-Based Brake Via a Neural Network Method(Springer international Publishing Ag, 2022) Kucukoglu, Sefa Furkan; Dede, Mehmet Ismet Can; Ceccarelli, MarcoIdentifying the model of a magneto-rheological (MR) fluid-based brake is extremely important for designing and controlling a haptic device with hybrid actuation. Therefore, in this study, an Elman Recurrent Neural Network (ERNN) is designed to understand and model a characterization of an MR fluid-based rotational brake. Three important factors that affect the MR brake's performance are chosen as inputs: current, speed, and the first derivative of the input current. The proposed network is trained, and the performance of the network is tested with three different experimental scenarios. Then, the effect of these inputs on the system is investigated. According to the results, it can be said that the designed ERNN is a good candidate for modelling an MR brake.