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Title: Modeling a magneto-rheological fluid-based brake via a neural network method
Authors: Küçükoğlu, Sefa Furkan
Dede, Mehmet İsmet Can
Ceccarelli, Marco
Keywords: Haptic devices
Hybrid actuation system
Elman recurrent neural network
Magneto-Rheological (MR)
Publisher: Springer
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.
Appears in Collections:Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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