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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    Title :

    Intelligent Vehicle Path Tracking Control based on Adaptive Model Predictive Control


    Contributors:
    Yu, Yu (author) / Zhao, Shuen (author) / Wang, Weiling (author) / Shen, Xingkui (author)


    Publication date :

    2024-10-25


    Size :

    1040490 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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