A neural-network-based model for the dynamic simulation of the tire/suspension system while traversing road irregularities.
This paper deals with the simulation of the tire/suspension dynamics by using recurrent neural networks (RNNs). RNNs are derived from the multilayer feedforward neural networks, by adding feedback connections between output and input layers. The optimal network architecture derives from a parametric analysis based on the optimal tradeoff between network accuracy and size. The neural network can be trained with experimental data obtained in the laboratory from simulated road profiles (cleats). The results obtained from the neural network demonstrate good agreement with the experimental results over a wide range of operation conditions. The NN model can be effectively applied as a part of vehicle system model to accurately predict elastic bushings and tire dynamics behavior. Although the neural network model, as a black-box model, does not provide a good insight of the physical behavior of the tire/suspension system, it is a useful tool for assessing vehicle ride and noise, vibration, harshness (NVH) performance due to its good computational efficiency and accuracy.
Guarneri
Paolo
P
Department of Mechanical Engineering, Politecnicodi Milano, Technical University, 20156 Milano, Italy.
Rocca
Gianpiero
G
Gobbi
Massimiliano
M
D000465:Algorithms; D001185:Artificial Intelligence; D001336:Automobiles; D003198:Computer Simulation; D017076:Computer-Aided Design; D004867:Equipment Design / Q000379:methods*; D019544:Equipment Failure Analysis / Q000379:methods*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*