@inproceedings{1feaa957ab794a9f93420cbc2dce1d48,
title = "Four Wheel Steering Vehicles Stability Control Based on Adaptive Radial Basis Function Neural Network",
abstract = "Due to the nonlinear and strong coupling characteristics of four-wheel steering vehicles, there are poor control accuracy and robustness in traditional control methods. This paper proposes a hierarchical controller of lateral stability based on mechanical-differential combined distributed drive four-wheel steering vehicle. The upper controller trains the RBF neural network offline based on the ideal sideslip angle, the parameters adaptively adjusted online according to the error and calculates the ideal rear wheel angle by using the front wheel angle in real time. In addition, a lower controller is established for the differential torque distribution of the hub motor on both sides with the PI controller. Finally, the performance is evaluated via MATLAB/Simulink. The simulation results prove that the four-wheel steering strategy based on adaptive RBF neural network controller (ADP-RBF) can effectively improve steering mobility at low speed and the lateral stability at high speed of the vehicle.",
keywords = "Adaptive RBF neural network controller, Distributed drive, Four-wheel steering vehicle, Stability control",
author = "Qi Li and Junqiu Li and Sifan Wang and Xiaopeng Zhang and Jiwei Liu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd Chinese Control and Decision Conference, CCDC 2021 ; Conference date: 22-05-2021 Through 24-05-2021",
year = "2021",
doi = "10.1109/CCDC52312.2021.9602099",
language = "English",
series = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1140--1145",
booktitle = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",
address = "United States",
}