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Advances in Acoustics and Vibration
Volume 2016 (2016), Article ID 7027259, 6 pages
Research Article

Development of an Experimental Model for a Magnetorheological Damper Using Artificial Neural Networks (Levenberg-Marquardt Algorithm)

Birla Institute of Technology and Science-Pilani, K.K. Birla Goa Campus, Goa 403726, India

Received 28 April 2016; Revised 15 July 2016; Accepted 18 July 2016

Academic Editor: Toru Otsuru

Copyright © 2016 Ayush Raizada et al. 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.


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 magnetorheological (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 Artificial Neural Networks. 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 retraining the algorithm and used for civil structures to make them earthquake resistant.