This study focuses on the anti-rollover control problem for autonomous semi-trailer tank trucks and proposes an anti-rollover path tracking control algorithm suitable for autonomous driving scenarios. A simplified semi-trailer tank truck model is established in the controller, modeling liquid as a single pendulum considering both lateral and roll inputs, and an anti-rollover path tracking algorithm that utilizes it is developed based on multi-constraint model predictive control (MPC). The equivalent lateral load transfer rate (LTR), liquid sloshing angle, and angular velocity are used as constraints to achieve multi-objective optimization for path tracking, sloshing suppression, and rollover prevention. A vehicle-fluid coupling co-simulation platform based on computational fluid dynamics (CFD) is built to verify the control performance under extreme scenarios of left turning, high-speed single-lane change (SLC), and short-distance double-lane change (DLC). Additionally, the similarity principle for experimental validation using a down-scale model tank truck is derived, and experiments are conducted on the model semi-trailer tank truck under the DLC scenario. Through simulation and experimental verification, the proposed anti-rollover path tracking algorithm demonstrates acceptable tracking performance while ensuring that no rollover is carried out under extreme conditions, limiting the angle of $|LTR|\lt 0.75$ and sloshing within $- 20\circ $ - $20\circ $ , reducing the angle of sloshing by a maximum of 42% in the experiment. Moreover, the real-time capability of the proposed algorithm meets the requirements for practical applications with a peak time consumption of one control step less than $20~ms$ in the controller tested.


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    Title :

    Anti-Rollover Path Tracking Control for an Autonomous Semi-Trailer Tank Truck


    Contributors:
    Qi, Xiaojing (author) / Luo, Yugong (author) / Gao, Bolin (author) / Zhong, Wei (author)


    Publication date :

    2025-07-01


    Size :

    5671989 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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