A novel robust model-free adaptive control (R-DMFAC) algorithm is proposed to address the path tracking control problem of autonomous vehicles in the presence of external measurement disturbances. First, the preview-deviation-yaw angle based tracking method is proposed, which transforms the path tracking problem into the preview-deviation-yaw angle control problem. Second, a novel dynamic linearization technique is employed to convert the nonlinear dynamical model, based on preview-deviation-yaw angle, into a linear data model with pseudo partial derivative (PPD), and the proposed algorithm (PFDL-EMFAC) is designed based on this data model. Furthermore, a measurement disturbance suppression scheme is designed by introducing the decreasing factor. Notably, implementing the algorithm does not involve any model information; it is a purely data-driven control algorithm. Finally, the joint simulation results of MATLAB-Panosim platform demonstrate that the maximum tracking error of the autonomous vehicle controlled by the R-DMFAC in different scenarios can be reduced to 0.5-0.7 m, verifying the effectiveness of the control algorithm.


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    Title :

    Path tracking control of autonomous vehicle under the measurement disturbance via a novel robust model free adaptive control algorithm


    Contributors:
    Shida Liu (author) / Guang Lin (author) / Honghai Ji (author) / Li Wang (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





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