Passive suspensions have been developed to a high level of sophistication but in order to further improve vehicle performance it is important to fully understand the dynamic behaviour of the suspension components using mathematical simulation. One of the most important components in a vehicle suspension system is the strut. Since it is highly non-linear it is difficult to predict its performance characteristics using a mathematical model. However, neural networks have been successfully used as universal 'black-box' models in the identification and control of non-linear systems. In this paper this approach has been used to model a novel gas strut and the neural network was trained with experimental data obtained in the laboratory from simulated road profiles. The results obtained from the neural network demonstrated good agreement with the experimental results over a wide range of operating conditions. In contrast, a linearised mathematical model using least square estimates of system parameters was shown to perform badly due to the highly non-linear nature of the system. A quarter-car mathematical model was developed in order to compare the linearised model with the model that used a neural network to predict strut behaviour. It was shown that the two models produced significantly different predictions of ride performance and it was argued that the neural network was preferable as it included the effects of non-linearities. Although the neural network model does not provide a good physical understanding of the component behaviour it is argued that it is a useful tool for assessing vehicle ride and NVH performance due to its good computational efficiency and accuracy.
|Journal||Proceedings of the ASME International Mechanical Engineering Congress|
|Publication status||Published - 2004|