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    Data-driven Modeling Approach for Control Design of a Variable-Geometry Suspension System

    Data-driven Modeling Approach for Control Design of a Variable-Geometry Suspension System


    The paper presents a data-driven modeling approach for a variable-geometry suspension (VGS) system. In the optimization process, a learning-based algorithm is used to select the relevant variables from the measured dataset. The dynamics of the VGS as a polytopic Linear Parameter Varying (LPV) system are formed. The optimized model is written into a polytopic form, which is the basis of the control design. Using the resulting model a controller for achieving steering functionality is designed. The effectiveness of the control method on a VGS test-bed using Hardware-in-the-Loop (HiL) simulation is demonstrated.

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    Titel :

    Data-driven Modeling Approach for Control Design of a Variable-Geometry Suspension System

    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering

    Beteiligte:
    Orlova, Anna (Herausgeber:in) / Cole, David (Herausgeber:in) / Fényes, Dániel (Autor:in) / Németh, Balázs (Autor:in) / Gáspár, Péter (Autor:in)
    Kongress:

    The IAVSD International Symposium on Dynamics of Vehicles on Roads and Tracks ; 2021 August 17, 2021 - August 19, 2021

    Erschienen in:

    Advances in Dynamics of Vehicles on Roads and Tracks II ; Kapitel : 58 ; 585-594

    Lecture Notes in Mechanical Engineering

    Erscheinungsdatum :

    2022-08-06

    Format / Umfang :

    10 pages

    ISBN :

    978-3-031-07305-2 , 978-3-031-07304-5

    ISSN :

    2195-4364 , 2195-4356

    DOI :

    https://doi.org/10.1007/978-3-031-07305-2_58

    Medientyp :

    Aufsatz/Kapitel (Buch)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

    Schlagwörter :

    Data-driven modeling and control , Variable-geometry suspension Engineering , Automotive Engineering , Complexity , Transportation Technology and Traffic Engineering

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