制动器
磁流变液
人工神经网络
流变学
电流(流体)
汽车工程
磁电机
工程类
计算机科学
控制理论(社会学)
控制工程
模拟
机械工程
材料科学
人工智能
磁铁
控制(管理)
电气工程
阻尼器
复合材料
作者
Sefa Furkan Küçükoğlu,Mehmet İsmet Can Dede,Marco Ceccarelli
出处
期刊:Mechanisms and machine science
日期:2022-01-01
卷期号:: 211-218
标识
DOI:10.1007/978-3-031-10776-4_25
摘要
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.
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