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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    Title :

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


    Contributors:
    Anwar Ali, Zain (editor) / Chihi, Inès (editor) / Wang, Wanting (author) / Wang, Siming (author) / Han, Taoguo (author)

    Conference:

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


    Published in:

    Proc. SPIE ; 13079


    Publication date :

    2024-03-15





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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