In this paper, a model predictive control (MPC) based trajectory tracking scheme utilizing steering wheel and braking or acceleration pedal is proposed for intelligent vehicles. The control objective is to track a desired trajectory which is obtained from the trajectory planner. The proposed control is based on a simplified third-order vehicle model, which consists of longitudinal vehicle dynamics along with a commonly used bicycle model. A nonlinear model predictive control (NMPC) is adopted in order to follow a given path by controlling front steering, braking and traction, while fulfilling various physical and design constraints. In order to reduce the computational burden, the NMPC is converted to a linear time-varying (LTV) MPC based on successive online linearization of the nonlinear system model. Two different test conditions have been used to verify the effectiveness of the proposed approaches through simulations using Matlab and CarSim. The results show that the desired speed and path are well tracked.


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    Titel :

    MPC-Based Trajectory Tracking Control for Intelligent Vehicles


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Zhu, Bing (Autor:in) / Zhang, Sumin (Autor:in) / Ming, Tingyou (Autor:in) / Deng, Weiwen (Autor:in)

    Kongress:

    SAE 2016 World Congress and Exhibition ; 2016



    Erscheinungsdatum :

    2016-04-05




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch


    Schlagwörter :


    MPC-Based Trajectory Tracking Control for Intelligent Vehicles

    Ming, Tingyou / Deng, Weiwen / Zhang, Sumin et al. | British Library Conference Proceedings | 2016



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    Chen, ShiChang / Yin, Zhishuai / Yu, Jia et al. | British Library Conference Proceedings | 2022


    LSTM-Based Trajectory Tracking Control for Autonomous Vehicles

    Yu, Jia / Zhang, Ming / Chen, ShiChang et al. | SAE Technical Papers | 2022