Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/12476
Title: Modeling a Magneto-Rheological Fluid-Based Brake Via a Neural Network Method
Authors: Kucukoglu, Sefa Furkan
Dede, Mehmet Ismet Can
Ceccarelli, Marco
Keywords: Magneto-Rheological Fluid-Based Brake
Elman Recurrent Neural Network
Haptic Device
Hybrid Actuation System
Publisher: Springer international Publishing Ag
Series/Report no.: Mechanisms and Machine Science
Abstract: Identifying 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.
URI: https://doi.org/10.1007/978-3-031-10776-4_25
ISBN: 9783031107757
9783031107764
ISSN: 2211-0984
2211-0992
Appears in Collections:Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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