Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Path tracking control of autonomous vehicle under the measurement disturbance via a novel robust model free adaptive control algorithm
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.
Path tracking control of autonomous vehicle under the measurement disturbance via a novel robust model free adaptive control algorithm
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.
Path tracking control of autonomous vehicle under the measurement disturbance via a novel robust model free adaptive control algorithm
Shida Liu (Autor:in) / Guang Lin (Autor:in) / Honghai Ji (Autor:in) / Li Wang (Autor:in)
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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