期刊
ELECTRONICS
卷 11, 期 19, 页码 -出版社
MDPI
DOI: 10.3390/electronics11193119
关键词
intelligent vehicle; trajectory tracking; friction coefficient estimation; recursive least squares; model predictive control
资金
- Tianjin Artificial Intelligence Innovation Fund [17YDLJGX00020]
This paper proposes an autonomous vehicle trajectory tracking system that considers road friction. It improves upon traditional recursive least squares (RLS) method to accurately identify the friction coefficient. The identified results are used in the model predictive controller (MPC) to enhance tire slip angle constraints and achieve excellent tracking performance for the intelligent vehicle.
This paper proposes an autonomous vehicle trajectory tracking system that fully considers road friction. When an intelligent vehicle drives at high speed on roads with different friction coefficients, the difficulty of its trajectory tracking control lies in the fast and accurate identification of road friction coefficients. Therefore, an improved strategy is designed based on traditional recursive least squares (RLS), which is utilized for accurate identification of the friction coefficient. First, the tire force and slip rate required for the estimation of the road friction coefficient by constructing the vehicle dynamics model and tire effective model are calculated. In this paper, a variable forgetting factor recursive least squares (VFF-RLS) method is proposed for the construction of the friction coefficient estimator. Second, the identified results are output to the model predictive controller (MPC) constructed in this paper as a way to improve tire slip angle constraints, to realize the trajectory tracking of the intelligent vehicle. Finally, the joint simulation test results of Carsim and Matlab/Simulink show that the trajectory tracking system based on the VFF-RLS friction coefficient estimator has outstanding tracking performance.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据