Performance of the Machine Learning on Controlling the Pneumatic Suspension of Automobiles on the Rigid and Off-Road Surfaces

To enhance the ride comfort and control performance of the semi-active pneumatic suspension system (PSS) of automobiles on the different road surfaces, a machine learning method (MLM) developed on the optimal control rules of the fuzzy logic control is proposed for the semi-active PSS. A nonlinear dynamic model of the automobile with eight degrees of freedom (DOF) is established to compute the results. The root mean square (RMS) accelerations of the vertical driver’s seat and the pitching angle and rolling angle of the automobile are selected to evaluate the ride comfort of the automobile on the rigid road and off-road terrain surfaces. The research results show that the off-road terrain surfaces remarkably affect the ride comfort of the automobile, especially at a high moving speed range of the automobile over 17.5 m/s. The performance of the MLM in improving the ride comfort of the automobile is better than the fuzzy logic control under various simulation conditions. Particularly, the RMS accelerations of the vertical driver’s seat and the pitching angle and rolling angle of the automobile with the MLM are smaller than that of the fuzzy logic control by 14.6%, 9.6%, and 5.3% on the rigid road surfaces and reduced by 14.9%, 8.7%, and 9.8% on the soil terrain of off-road terrain surfaces, respectively. However, the research results also indicate that the performance of the MLM significantly depends on the data map of the learning process. Thus, to further enhance the performance of the MLM, the data map for the machine learning process should be expanded under different operating conditions of the automobile.