To enhance intelligent vehicle path tracking accuracy and adaptability across different road conditions and speeds, this paper introduces a parameter-adaptive MPC method combined with a PSO-BP neural network. The MPC algorithm was formulated with path tracking accuracy and control increment as the key components of its cost function. The PSO-BP neural network was employed to dynamically adjust the weights of the MPC cost function in real time. The controller was implemented using a co-simulation framework built with CarSim and MATLAB/Simulink. Simulation results under different road adhesion levels and vehicle speeds demonstrated the proposed algorithm's effectiveness.


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    Titel :

    Intelligent Vehicle Path Tracking Control based on Adaptive Model Predictive Control


    Beteiligte:
    Yu, Yu (Autor:in) / Zhao, Shuen (Autor:in) / Wang, Weiling (Autor:in) / Shen, Xingkui (Autor:in)


    Erscheinungsdatum :

    25.10.2024


    Format / Umfang :

    1040490 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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