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 tra...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2024-09-01
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| Series: | Journal of Highway and Transportation Research and Development |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/HTRD.2024.9480025 |
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| Summary: | 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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| ISSN: | 2095-6215 |