Automated drifting presents a challenge problem for vehicle control, requiring models and control algorithms that can precisely handle nonlinear, coupled tire forces at the friction limits. We present a neural network architecture for predicting front tire lateral force as a drop-in replacement for physics-based approaches. With a full-scale automated vehicle purpose-built for the drifting application, we deploy these models in a nonlinear model predictive controller tuned for tracking a reference drifting trajectory, for direct comparisons of model performance. The neural network tire model exhibits significantly improved path tracking performance over the brush tire model in cases where front-axle braking force is applied, suggesting the neural network’s ability to express previously unmodeled, latent dynamics in the drifting condition.


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    Titel :

    Neural Network Tire Force Modeling for Automated Drifting


    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) / Broadbent, Nicholas Drake (Autor:in) / Weber, Trey (Autor:in) / Mori, Daiki (Autor:in) / Gerdes, J. Christian (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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