This paper is concerned with the application of reinforcement learning to the dynamic ride control of an active vehicle suspension system. The study makes key extensions to earlier simulation work to enable on-line implementation of the learning automation methodology using an actual vehicle. Extensions to the methodology allow safe and continuous learning to take place on the road, using a limited instrumentation set. An important new feature is the use of a moderator to set physical limits on the vehicle states. It is shown that the addition of the moderator has little direct effect on the system's ability to learn, and allows learning to take place continuously even when the successful synthesis of a semi-active ride controller is demonstrated.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Moderated reinforcement learning of active and semi-active vehicle suspension control laws


    Additional title:

    Mäßig verstärktes Lernverfahren bei der Regelung aktiver und semiaktiver Fahrzeugaufhängungen


    Contributors:
    Frost, G.P. (author) / Gordon, T.J. (author) / Howell, M.N. (author) / Wu, Q.H. (author)


    Publication date :

    1996


    Size :

    9 Seiten, 9 Bilder, 4 Tabellen, 13 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





    Semi-active suspension and vehicle

    ZHAO NANNAN / CUI YOUGANG / GAO YUAN | European Patent Office | 2023

    Free access

    Semi-active suspension and semi-active suspension control method and device

    CHEN LIN / YANG LI / TAN KANLUN et al. | European Patent Office | 2024

    Free access

    Semi-active suspension control system

    LU HAO / ZHANG CHENG / LIU MINGRUI et al. | European Patent Office | 2025

    Free access

    Microprocessor adaptive control for vehicle semi-active suspension damping

    Yiming,Z. / Zhihua,Z. / Beijing Inst.of Techn.,CN | Automotive engineering | 1992