Research on Vehicle Active Steering Stability Control Based on Variable Time Domain Input and State Information Prediction

Zepeng Gao, Jianbo Feng, Chao Wang, Yu Cao, Bonan Qin, Tao Zhang (), Senqi Tan, Riya Zeng, Hongbin Ren, Tongxin Ma, Youshan Hou and Jie Xiao
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Zepeng Gao: China North Vehicle Research Institute, Beijing 100072, China
Jianbo Feng: Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Chao Wang: China North Vehicle Research Institute, Beijing 100072, China
Yu Cao: China North Vehicle Research Institute, Beijing 100072, China
Bonan Qin: China North Vehicle Research Institute, Beijing 100072, China
Tao Zhang: China North Vehicle Research Institute, Beijing 100072, China
Senqi Tan: China North Artificial Intelligence & Innovation Research Institute, Beijing 100072, China
Riya Zeng: China North Vehicle Research Institute, Beijing 100072, China
Hongbin Ren: Beijing Institute of Technology, Beijing 100081, China
Tongxin Ma: China North Vehicle Research Institute, Beijing 100072, China
Youshan Hou: China North Vehicle Research Institute, Beijing 100072, China
Jie Xiao: China North Vehicle Research Institute, Beijing 100072, China

Sustainability, 2022, vol. 15, issue 1, 1-18

Abstract: The controller design of vehicle systems depends on accurate reference index input. Considering information fusion and feature extraction based on existing data settings in the time domain, if reasonable input is selected for prediction to obtain accurate information of future state, it is of great significance for control decision-making, system response, and driver’s active intervention. In this paper, the nonlinear dynamic model of the four-wheel steering vehicle system was built, and the Long Short-Term Memory (LSTM) network architecture was established. On this basis, according to the real-time data under different working conditions, the information correction calculation of variable time-domain length was carried out to obtain the real-time state input length. At the same time, the historical state data of coupled road information was adopted to train the LSTM network offline, and the acquired real-time data state satisfying the accuracy was used as the LSTM network input to carry out online prediction of future confidence information. In order to solve the problem of mixed sensitivity of the system, a robust controller for vehicle active steering was designed with the sideslip angle of the centroid of 0, and the predicted results were used as reference inputs for corresponding numerical calculation verification. Finally, according to the calculated results, the robust controller with information prediction can realize the system stability control under coupling conditions on the premise of knowing the vehicle state information in advance, which provides an effective reference for controller response and driver active manipulation.

Keywords: information fusion and feature extraction; non-linear dynamic model; variable time domain information correction; information online prediction; robust controller design (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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