In this paper, a neural network-based feedback linearization method is presented for automated vehicle application purposes. The main idea behind this algorithm is to match the dynamics of the nonlinear system to a linear model. The linearization is achieved by the data-driven training process of a neural network in the control structure. Through the proposed feedback linearization the nonlinear dynamical properties of the vehicle can be effectively handled in the control design process. It can provide advanced functionalities for the automated vehicle, e.g. maneuvering in critical situations. Moreover, the benefit of the presented method is that the design of the control system without the exact formulation of the nonlinear vehicle dynamics can be performed. The effectiveness of the method through the control design for achieving path following functionality is presented.


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    Titel :

    Control Design Framework for Automated Vehicles Using an Advanced Feedback Linearization


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Orlova, Anna (Herausgeber:in) / Cole, David (Herausgeber:in) / Hegedűs, Tamás (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



    Erscheinungsdatum :

    06.08.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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