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A model for a Magneto-Rheological ( MR) damper based on Artifical Neural Networks ( ANN) is proposed. TheANN model does not require regressors in the input and output vector, i.e. is considered static. Only one sensor is used to achieve a reliable MR damper model which is compared with experimental data provided from twoMRdampers with different properties. The RMSof the error is used to measure the model accuracy; from bothMR dampers, an average value of 7 .1% of total error in the force signal is obtained by taking into account 5 different experiments. The ANN model, which represents the nonlinear behavior of an MR damper, is used in a suspension control system of a Quarter of Vehicle ( QoV) in order to evaluate the comfort of passengers maintaining the road holding. A control technique with the MR damper model is compared with a passive suspension system. Simulation results show the effectiveness of a semiactive suspension versus the passive one. The RMSof the comfort signal improves 7.4% with the MR damper while the road holding gain in the frequency response shows that the safety in the vehicle can be increased until 40.4% with the semiactive suspension system. The accurate MR damper model validates a realistic QoV response compliance.

MR Damper Identification using ANN based on 1-Sensor - A Tool for Semiactive Suspension Control Compliance