基于轮胎力预判与拟合的轨迹跟踪控制

doi:10.13229/j.cnki.jdxbgxb20190630

Abstract

Abstract:

The complex interaction process between the tire and the ground determines that the inverse solution of the model does not exist adequately. It is impossible to directly solve the vehicle model, in which tire characteristics are considered, to obtain the desired control amount from the trajectory. To solve this problem, a trajectory tracking control strategy based on tire force prediction and fitting is proposed. According to the physical boundary, the trajectory tracking control is decomposed into two layers. The upper layer control uses a terminal sliding mode control algorithm. To realize the pre-judgment of the desired front wheel lateral force, the body dynamics is solved according to the target curvature. The lower layer control uses the system identification method. According to the feature point data, the desired steering angle is obtained by fitting the characteristics of the tire force through the spline function. A 12-DOF vehicle dynamics model with body and tire is established. According to the test value of sliding rate required to maintain different speeds, a semi-empirical sliding mode longitudinal control strategy is established to control the vehicle speed. A contrast algorithm is constructed with neural network algorithm. In the step curvature condition for testing stability and the multi-variable curvature condition for simulating continuous turning roads, the effectiveness and global robustness of the proposed algorithm are verified.

Key words: vehicle engineering, trajectory tracking, layered control, tire force prediction, characteristic fitting

CLC Number:

U461.1

Ji-qing CHEN,Qing-sheng LAN,Feng-chong LAN,Zhao-lin LIU. Trajectory tracking control based on tire force prediction and fitting[J].Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1565-1573.

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项目	恒定曲率区间
项目	1	2	3	4
恒定曲率值	0.040	0.045	0.030	0.040
神经网络	-0.0025	-0.0053	-0.0073	-0.0025
特性拟合	-0.0022	0.0040	-0.0037	-0.0022
改善程度/%	12.0	24.5	49.3	12.0

项目	阶跃点
项目	a	b	c	d
曲率阶跃量	0.040	0.005	-0.015	0.010
神经网络	0.0071	0.0089	0.0130	0.0099
特性拟合	0.0035	0.0031	0.0027	0.0020
改善程度/%	50.9	64.9	78.6	79.9

类别	极值/m	均值/m	方差/m	运行时长/s
神经网络	0.0225	0.0043	0.0032	12.58
特性拟合	0.0047	0.0022	0.0015	11.90
改善程度/%	79.1	50.3	53.2	5.4

Trajectory tracking control based on tire force prediction and fitting

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