Experimental ANN-based modeling of an adjustable damper

Abstract

A model for a Magneto-Rheological (MR) damper based on Artificial Neural Networks (ANN) is proposed. The design of the ANN model is focused to get the best architecture that manages the trade-off between computing cost and performance. Experimental data provided from two commercial MR dampers with different properties have been used to validate the performance of the proposed ANN model in comparison with the classical parametric model of Bingham. Based on the Root Mean Square Error index, an average error of 7.2 % is obtained by the ANN model, by taking into account 5 experiments with 10 replicas each one; while the Bingham model has 13.8 % of error.