In this paper, the performance of automotive ride comfort using Bouc-Wen type magneto-rheological (MR) fluid damper is studied using a two degree of freedom quarter car model. The sliding mode control is used to force the MR damper to follow the dynamics of ideal sky-hock model. The model is tested on two excitations, the first is a road hump with severe peak amplitude and the second is a statistical random road. The results are generated and presented in time and frequency domains using Matlab/Simulink software. Comparison with the fully active, ideal semi-active and conventional passive suspension systems are given as a root mean square values. Simulation results, for the designed controller, show that with the controllable MR damper has a significant improvement for the vehicle road holding then its lateral stability as well as road damage in comparison with passive, fully active and ideal semi-active suspension systems.
In this work, the effects of component non-linearities on the ride performance of a hydro-pneumatic slow-active suspension system are studied theoretically. Based on the quarter car linear model, linear optimal control theory is used to calculate the feedback and feedforward gains. These gains are used in both linear and non-linear models with and without preview control. The Pade approximation technique is used to represent the preview time resulting from a preview sensor mounted on the vehicle front bumper to measure the road irregularities ahead of the front wheel. The results on a typical major road showed that at similar r.m.s. values of suspension working space, the non-linear slow-active system with preview provided a 28 per cent improvement in ride comfort and a 17 per cent reduction in dynamic tyre load compared with a passive system. However, the inclusion of non-linear effects of the components increases the ride comfort acceleration by 10 per cent and suspension working space by 12 per cent compared to the equivalent linear model at approximately equal values of r.m.s. dynamic tyre load.
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