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  • SAGE Publications  (2)
  • Chen, Long  (2)
  • Xie, Ju  (2)
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  • SAGE Publications  (2)
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  • 1
    Online Resource
    Online Resource
    Modeling adaptive preview time of driver model for intelligent vehicles based on deep learning
    SAGE Publications ; 2022
    In:  Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering Vol. 236, No. 2 ( 2022-02), p. 355-369
    Details
    In: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, SAGE Publications, Vol. 236, No. 2 ( 2022-02), p. 355-369
    Abstract: In order to improve the adaptability and tracking performance of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the real driver in the driver–vehicle–road closed-loop system, a kind of adaptive preview time model for intelligent vehicle driver model is proposed. This article builds the intelligent vehicle driver model based on optimal preview control theory and the basic preview time is identified to minimize path error under various conditions based on particle swarm optimization. Then, the ideal compensation preview time is constructed in various conditions and the appropriate factors affecting compensation preview time are filtered out according to correlation analysis. Moreover, the architecture and training procedure of deep network is specified for compensation preview time prediction. Finally, the adaptive preview time is modeled by combining the basic preview time with the compensation preview time and the validity of adaptive preview time model is verified by the driver–vehicle–road closed-loop system under normal and aggressive driving conditions. The results show that the proposed adaptive preview time model can help intelligent vehicles better adapt complex driving conditions and effectively improve the path-following performance.
    Type of Medium: Online Resource
    ISSN: 0959-6518 , 2041-3041
    URL: Article
    DOI: 10.1177/09596518211028372
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2024903-2
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Adaptive coordination control strategy for path-following of DDAEV with neural network based moving weight coefficients
    SAGE Publications ; 2023
    In:  Transactions of the Institute of Measurement and Control Vol. 45, No. 7 ( 2023-04), p. 1282-1297
    Details
    In: Transactions of the Institute of Measurement and Control, SAGE Publications, Vol. 45, No. 7 ( 2023-04), p. 1282-1297
    Abstract: This paper presents an adaptive coordination control strategy for path-following of distributed drive autonomous electric vehicles (DDAEV). A model predictive control (MPC) algorithm is used to realize path-following through autonomous steering, where the prediction time is adaptive in relation to different driving conditions. Due to the dynamic characteristic of distributed drive vehicle, the differential torque control is also utilized based on the deviation of path to realize path-following. In order to make full use of the advantages of these two path-following methods, coordination control of autonomous steering and differential steering is adopted to improve the transient response speed, flexibility of steering system and path-following performance by setting moving weight coefficients based on neural network. Results of CarSim-Simulink co-simulation and real vehicle experiment both verify that the proposed coordination control can obtain steering flexibility, as well as tracking accuracy and reliability under various driving conditions.
    Type of Medium: Online Resource
    ISSN: 0142-3312 , 1477-0369
    URL: Article
    DOI: 10.1177/01423312221135255
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
    detail.hit.zdb_id: 2025882-3
    SSG: 3,2
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    Online Resource
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