Traditional parameter tuning of cascade controllers heavily depends on practical experiments and expert experience, which can be both time-consuming and complex. This study proposes a feedforward and cascade feedback PID control scheme to address vehicle trajectory-following challenges and develops a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for adaptive tuning of controller parameters. A range of scenarios and control strategies are simulated and evaluated through joint simulations in Matlab/Simulink and Carsim. The simulation results demonstrate that the proposed method effectively adjusts controller coefficients online through learning, reduces calibration time, and enhances trajectory-following performance and adaptability compared to alternative control approaches.


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    Titel :

    Adaptive Cascade Control for Vehicle Trajectory Following Based on Deep Reinforcement Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wong, Pak Kin (Herausgeber:in) / Xu, Jun (Herausgeber:in) / Lu, Yong (Autor:in) / Yang, Diange (Autor:in) / Tian, He (Autor:in) / Jiang, Kun (Autor:in) / Li, Runfeng (Autor:in)

    Kongress:

    International Conference on Electric Vehicle and Vehicle Engineering ; 2024 ; Zhuhai, China December 20, 2024 - December 22, 2024



    Erscheinungsdatum :

    01.07.2025


    Format / Umfang :

    6 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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