Controlling a vehicle’s powerslide motion in the presence of a human driver is a challenging control task, but one that may have a significant impact on vehicle safety, for example, during rapid evasive manoeuvres. Reinforcement Learning, a data-driven optimal control strategy, has gained increasing attention in recent years, demonstrating its effectiveness in successfully controlling various nonlinear systems. In this work, a novel powerslide controller is designed for an all-wheel drive battery electric vehicle with individually driven front and rear axles and a human driver in closed-loop using Reinforcement Learning. The performance of the proposed controller is analysed, and its robustness to steering disturbances and changes in road friction is demonstrated.


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    Titel :

    Powerslide Control with Deep Reinforcement Learning


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Mastinu, Giampiero (Herausgeber:in) / Braghin, Francesco (Herausgeber:in) / Cheli, Federico (Herausgeber:in) / Corno, Matteo (Herausgeber:in) / Savaresi, Sergio M. (Herausgeber:in) / Jaumann, Florian (Autor:in) / Schuster, Tobias (Autor:in) / Unterreiner, Michael (Autor:in) / Gräber, Torben (Autor:in) / Edelmann, Johannes (Autor:in)

    Kongress:

    Advanced Vehicle Control Symposium ; 2024 ; Milan, Italy September 01, 2024 - September 05, 2024



    Erscheinungsdatum :

    04.10.2024


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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