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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    Title :

    Powerslide Control with Deep Reinforcement Learning


    Additional title:

    Lect.Notes Mechanical Engineering



    Conference:

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



    Publication date :

    2024-10-04


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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