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Intelligent Vehicle Path Tracking Control Method Based on Curvature Optimisation.

Authors :
Ye, Qing
Gao, Chaojun
Zhang, Yao
Sun, Zeyu
Wang, Ruochen
Chen, Long
Source :
Sensors (14248220). May2023, Vol. 23 Issue 10, p4719. 24p.
Publication Year :
2023

Abstract

In this study, an intelligent vehicle (IV) path tracking control method based on curvature optimisation is proposed to reduce the comprehensive performance conflict of the system. This system conflict is caused by the mutual restriction between the path tracking accuracy and the body stability during the movement of the intelligent automobile. First, the working principle of the new IV path tracking control algorithm is briefly introduced. Then, a three-degrees-of-freedom vehicle dynamics model and a preview error model considering vehicle roll are established. In addition, a path tracking control method based on curvature optimisation is designed to solve the deterioration of vehicle stability even when the path tracking accuracy of the IV is improved. Finally, the effectiveness of the IV path tracking control system is validated through simulations and the Hardware in the Loop (HIL) test with various conditions forms. Results clearly show that the optimisation amplitude of the IV lateral deviation is up to 84.10%, and the stability is improved by approximately 2% under the vx = 10 m/s and ρ = 0.15 m−1 condition; the optimisation amplitude of the lateral deviation is up to 66.80%, and the stability is improved by approximately 4% under the vx = 10 m/s and ρ = 0.2 m−1 condition; the body stability is improved by 20–30% under the vx = 15 m/s and ρ = 0.15 m−1 condition, and the boundary conditions of body stability are triggered. The curvature optimisation controller can effectively improve the tracking accuracy of the fuzzy sliding mode controller. The body stability constraint can also ensure the smooth running of the vehicle in the optimisation process. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14248220
Volume :
23
Issue :
10
Database :
Academic Search Index
Journal :
Sensors (14248220)
Publication Type :
Academic Journal
Accession number :
163987248
Full Text :
https://doi.org/10.3390/s23104719