Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm

Abstract: Magnetorheological (MR) fluid is among the smart materials that can change its default properties with the influence of a magnetic field. Typical application of an MR fluid based device involves an adjustable damper which is commercially known as an MR fluid damper. It is used in vibration control as an isolator in vehicles and civil engineering applications. As part of the device development process, proper understanding of the device properties is essential for reliable device performance analysis. This study introduce an accurate and fast prediction model to analyse the dynamic characteristics of the MR fluid damper. This study proposes a new modelling technique called Extreme Learning Machine (ELM) to predict the dynamic behaviour of an MR fluid damper hysteresis loop. This technique was adopted to overcome the limitations of the existing models using Artificial Neural Networks (ANNs). The results indicate that the ELM is extremely faster than ANN, with the capability to produce high accuracy prediction performance. Here, the hysteresis loop, which represents the relationship of force-displacement for the MR fluid damper, was modelled and compared using three different activation functions, namely, sine, sigmoid and hard limit. Based on the results, it was found that the prediction performance of ELM model using the sigmoid activation functions produced highest accuracy, and the lowest Root Mean Square Error (RMSE).

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• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm ; volume:11 ; number:1 ; year:2021 ; pages:584-591 ; extent:8 ; Open engineering ; 11, Heft 1 (2021), 584-591 (gesamt 8)

Contributors, Places and Time

• Creator: Saharuddin, K. D. ; Ariff, M. H. M. ; Mohmad, K. ; Bahiuddin, I. ; Ubaidillah ; Mazlan, S. A. ; Nazmi, N. ; Fatah, A. Y. A.

Further information

• DOI: 10.1515/eng-2021-0053
• URN: urn:nbn:de:101:1-2412141727501.201972933138
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.