Lian Yufeng, Feng Wenhuan, Liu Shuaishi, Nie Zhigen
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.
Front Neurorobot. 2023 Aug 9;17:1229808. doi: 10.3389/fnbot.2023.1229808. eCollection 2023.
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.
提出了一种道路附着系数和轮胎侧偏刚度的归一化方法，为车辆直接横摆力矩控制（DYC）系统设计提供重要信息。该方法基于分数阶多变量灰色模型（FOMVGM）和长短期记忆（LSTM）网络实现。利用FOMVGM为LSTM网络生成训练数据和测试数据，并采用LSTM网络根据道路附着系数预测轮胎侧偏刚度。此外，由道路附着系数表示的轮胎侧偏刚度可用于建立车辆横向动力学模型，并参与DYC鲁棒控制器设计。分别进行了不同驾驶循环下的仿真，以验证所提出的道路附着系数和轮胎侧偏刚度归一化方法以及车辆DYC鲁棒控制系统的可行性和有效性。
