@article{Liu2024, 
author = {Shida Liu and Guang Lin and Honghai Ji and Li Wang},
title = {Path tracking control of autonomous vehicle under the measurement disturbance via a novel robust model free adaptive control algorithm},
year = {2024},
journal = {Journal of Highway and Transportation Research and Development (English Edition)},
volume = {18},
number = {3},
pages = {68-75},
keywords = {traffic engineering, path tracking, autonomous vehicle, data-driven control, model free adaptive control},
url = {https://www.sciopen.com/article/10.26599/HTRD.2024.9480025},
doi = {10.26599/HTRD.2024.9480025},
abstract = {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.}
}