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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zepeng Gao & Jianbo Feng & Chao Wang & Yu Cao & Bonan Qin & Tao Zhang & Senqi Tan & Riya Zeng & Hongbin Ren & Tongxin Ma & Youshan Hou & Jie Xiao, 2022. "Research on Vehicle Active Steering Stability Control Based on Variable Time Domain Input and State Information Prediction," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
  • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:114-:d:1010564
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    References listed on IDEAS

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    1. Minhee Kang & Wooseop Lee & Keeyeon Hwang & Young Yoon, 2022. "Vision Transformer for Detecting Critical Situations and Extracting Functional Scenario for Automated Vehicle Safety Assessment," Sustainability, MDPI, vol. 14(15), pages 1-19, August.
    2. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    3. Jie Tian & Jie Ding & Yongpeng Tai & Ning Chen, 2018. "Hierarchical Control of Nonlinear Active Four-Wheel-Steering Vehicles," Energies, MDPI, vol. 11(11), pages 1-14, October.
    4. Sudhir Kumar Rajput & Jagdish Chandra Patni & Sultan S. Alshamrani & Vaibhav Chaudhari & Ankur Dumka & Rajesh Singh & Mamoon Rashid & Anita Gehlot & Ahmed Saeed AlGhamdi, 2022. "Automatic Vehicle Identification and Classification Model Using the YOLOv3 Algorithm for a Toll Management System," Sustainability, MDPI, vol. 14(15), pages 1-15, July.
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