A road adhesion coefficient-tire cornering stiffness normalization method combining a fractional-order multi-variable gray model with a LSTM network and vehicle direct yaw-moment robust control

Frontiers in Neurorobotics

Neuroscience

Zhigen Nie¹  Wenhuan Feng²  Yufeng Lian³  Shuaishi Liu³ 

[1] Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China;School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, Jilin, China;School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, Jilin, China;Institute of Robotics and Engineering, Changchun University of Technology, Changchun, Jilin, China;

关键词: direct yaw-moment control;    fractional-order multi-variable gray model;    LSTM network;    normalization method;    road adhesion coefficient;    tire cornering stiffness;   

DOI  :  10.3389/fnbot.2023.1229808

 received in 2023-05-27, accepted in 2023-07-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

A normalization method of road adhesion coefficient and tire cornering stiffness is proposed to provide the significant information for vehicle direct yaw-moment control (DYC) system design. This method is carried out based on a fractional-order multi-variable gray model (FOMVGM) and a long short-term memory (LSTM) network. A FOMVGM is used to generate training data and testing data for LSTM network, and LSTM network is employed to predict tire cornering stiffness with road adhesion coefficient. In addition to that, tire cornering stiffness represented by road adhesion coefficient can be used to built vehicle lateral dynamic model and participate in DYC robust controller design. Simulations under different driving cycles are carried out to demonstrate the feasibility and effectiveness of the proposed normalization method of road adhesion coefficient and tire cornering stiffness and vehicle DYC robust control system, respectively.

【 授权许可】

Unknown   
Copyright © 2023 Lian, Feng, Liu and Nie.