Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106971
Title: Modeling of a magneto-rheological (MR) damper using genetic programming
Authors: Tai, Kang
Raj, Varun
Singru, Pravin
Raizada, Ayush
Krishnakumar, Vishnuvardhan
Garg, Akhil
Keywords: Engineering::Mechanical engineering
MR Damper
Genetic Programming
Issue Date: 2017
Source: Singru, P., Raizada, A., Krishnakumar, V., Garg, A., Tai, K., & Raj, V. (2017). Modeling of a magneto-rheological (MR) damper using genetic programming. Journal of Vibroengineering, 19(5), 3169-3177. doi:10.21595/jve.2017.17828
Series/Report no.: Journal of Vibroengineering
Abstract: This paper is based on the experimental study for design and control of vibrations in automotive vehicles. The objective of this paper is to develop a model for the highly nonlinear Magneto-Rheological (MR) damper to maximize passenger comfort in an automotive vehicle. The behavior of the MR damper is studied under different loading conditions and current values in the system. The input and output parameters of the system are used as a training data to develop a suitable model using Genetic Algorithm. To generate the training data, a test rig similar to a quarter car model was fabricated to load the MR damper with a mechanical shaker to excite it externally. With the help of the test rig the input and output parameter data points are acquired by measuring the acceleration and force of the system at different points with the help of an impedance head and accelerometers. The model is validated by measuring the error for the testing and validation data points. The output of the model is the optimum current that is supplied to the MR Damper, using a controller, to increase the passenger comfort by minimizing the amplitude of vibrations transmitted to the passenger. Besides using this model for cars, bikes and other automotive vehicles it can also be modified by re-training the algorithm and used for civil structures to make them earthquake resistant.
URI: https://hdl.handle.net/10356/106971
http://hdl.handle.net/10220/49006
ISSN: 1392-8716
DOI: 10.21595/jve.2017.17828
Rights: © 2017 JVE International Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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