This paper presents a novel approach to address the lateral control issue in trajectory tracking for autonomous cars. Traditional model-free adaptive control algorithms have some limitations, prompting the development of an enhanced data-driven model-free adaptive control scheme. More precisely, inside a specified trajectory, the vehicle's issue of lateral control is converted into a problem of tracking the preview-deviation-angle. A dynamic linearized data model is developed for the lateral control of autonomous cars, taking into account the input and output data. To enhance the steering angle control rate, a time-varying proportional control term is incorporated. An enhanced trajectory tracking control technique for the MFAC system is proposed, followed by a demonstration of its convergence. The simulation findings demonstrate that the IMFAC scheme exhibits a superior output response compared to both MFAC and PID in both low-speed and highspeed scenarios. Furthermore, the IMFAC system is capable of achieving autonomous vehicle path tracking.


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    Titel :

    Trajectory tracking control of autonomous vehicles based on improved model-free adaptive control


    Beteiligte:
    Anwar Ali, Zain (Herausgeber:in) / Chihi, Inès (Herausgeber:in) / Wang, Wanting (Autor:in) / Wang, Siming (Autor:in) / Han, Taoguo (Autor:in)

    Kongress:

    Third International Conference on Testing Technology and Automation Engineering (TTAE 2023) ; 2023 ; Xi-an, China


    Erschienen in:

    Proc. SPIE ; 13079


    Erscheinungsdatum :

    15.03.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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