MR damper identification using ANN based on 1-sensor a tool for semiactive suspension control compliance

A model for a Magneto-Rheological (MR) damper based on Artifical Neural Networks (ANN) is proposed. The ANN 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 two MR dampers with different properties. The RMS of the error is used to measure the model accuracy; from both MR 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 RMS of 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.