Data Modeling of Multi-Axle Special Vehicles and Lateral Dynamics Applications

The dynamic model of multi-axle special vehicles has strong nonlinearity. The modeling method based on physical laws requires precise model parameters and complex mathematical equations to reflect the characteristics of vehicle dynamics. In the absence of accurate prior physical parameter information of the vehicle and dynamic function relationship, to improve the fidelity of vehicle dynamics modeling, a data modeling method based on neural networks is proposed for the lateral dynamic behavior of a five-axle special vehicle. At the same time, it is used as an input to predict the state of the next moment, and the recursive update of data modeling is realized; for the closed-loop network model, a training strategy is designed for the closed-loop structure, and intermediate variables are introduced into the network model, so that the network still maintains the closed-loop structure during the training phase; the network module adopts a combination of Gate Recurrent Unit (GRU) and Full Neural Networks (FNN); the data set is generated by the TruckSim simulation model that has been verified by real vehicles. The results show that it is difficult for physical modeling to accurately predict vehicle state information without accurate prior vehicle information, and the data model has better fidelity. The closed-loop training method can make the network with a closed-loop structure have better fidelity. The maximum absolute errors of the prediction of lateral velocity and yaw velocity are only 0. 079 km / h and 0. 342° / s; compared with the results of open-loop training, the maximum errors are reduced by 58. 40% and 49. 48%. © 2023 China Ordnance Society. All rights reserved.