Dynamic Behavior Prediction of Magnetorheological Fluid Dampers using Neural Networks

  • Amr Atef Abd Elwahed
  • Hassan Metered
  • Hany Monieb
Keywords: Magnetorheological Dampers, Neural Networks, Nonparametric Identification, Nonlinear Systems


Parametric and non-parametric models have been studied to predict the behavior of magnetorheological (MR) fluid dampers by a lot of researchers due to their nonlinear dynamics. In this paper, the direct and inverse dynamic identification for MR fluid dampers using recurrent neural networks are investigated to demonstrate the more accurate and efficient model. The effect of neural networks construction on the prediction quality of dynamic performance of MR damper is introduced in details. The trained direct identification neural network model can be used to predict the damping force of the MR fluid damper on line and the inverse dynamic neural network model can be used to generate the command voltage applied to the damper coil through supervised learning. The architectures and the learning techniques of direct and inverse neural network models for MR fluid dampers are introduced and simulation results are discussed. Finally, the trained neural network models are used to predict the damping force of the MR fluid damper accurately and precisely. Moreover, validation results for the neural network models are proposed and used to evaluate their performance. Validation results with several data sets indicate that the proposed direct and inverse identification models using recurrent neural networks can be used to predict the dynamic performance of MR fluid dampers perfectly.


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How to Cite
Elwahed, A. A. A., Metered, H., & Monieb, H. (2024). Dynamic Behavior Prediction of Magnetorheological Fluid Dampers using Neural Networks. European Journal of Science, Innovation and Technology, 4(2), 249-262. Retrieved from https://ejsit-journal.com/index.php/ejsit/article/view/411