Model Predictive Control of Unmanned Vehicle Trajectory Tracking Based on Gaussian Process Regression

doi:10.12382/bgxb.2024.0904

Abstract

Abstract:

Trajectory tracking is a crucial functionality of the autonomous driving control system.The vehicle dynamics model has a significant impact on trajectory tracking performance,however,there is a conflict between model complexity and solving efficiency,often leading to insufficient tracking accuracy under nonlinear conditions.To address this challenge,this paper proposes a model predictive control method based on Gaussian process regression (GPR) for trajectory tracking.A simplified model is used to ensure solving efficiency,and GPR model is employed to compensate for the vehicle model,thereby enhancing the trajectory tracking performance.First,a vehicle state fusion estimation method based on the single-track dynamics model is developed to obtain the GPR compensation model.A trajectory tracking error model is developed.Based on the trajectory tracking error from vehicle dynamics model,the iterative equation for GPR error compensation within the predictive horizon is derived to dynamically compensate for model errors in the vehicle state prediction for achieving the trajectory tracking control.Finally,a real-vehicle validation platform is constructed to validate the proposed method under typical driving conditions.The proposed method is compared with other predictive control methods without GPR compensation.The results show the proposed method achieves a significant improvement in trajectory tracking accuracy.Specifically,the lateral and heading errors are reduced by 33.3% and 27.9%,respectively.Furthermore,the vehicle comfort performance is also improved,and the mean lateral acceleration and yaw rate are reduced by 17.1% and 21.7%,respectively.

Key words: Gaussian process regression, model predictive control, trajectory tracking, autonomous driving

CLC Number:

TP275

LI Qin, HE Hongwen, HU Manjiang. Model Predictive Control of Unmanned Vehicle Trajectory Tracking Based on Gaussian Process Regression[J]. Acta Armamentarii, 2025, 46(8): 240904-.

Figures/Tables 12

References 21

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参数	描述	数值
m/kg	整车质量	2063.4
l_f/mm	前半轴距	1351.8
l_r/mm	后半轴距	1548.2
C_f/(N·rad^-1)	前轮等效侧偏刚度	100640
C_r/(N·rad^-1)	后轮等效侧偏刚度	100640
R_tire/mm	等效轮胎滚动半径	366
I_z/(kg·m²)	绕Z轴的转动惯量	3347.8

参数	描述	数值
m/kg	整车质量	2063.4
l_f/mm	前半轴距	1351.8
l_r/mm	后半轴距	1548.2
C_f/(N·rad^-1)	前轮等效侧偏刚度	100640
C_r/(N·rad^-1)	后轮等效侧偏刚度	100640
R_tire/mm	等效轮胎滚动半径	366
I_z/(kg·m²)	绕Z轴的转动惯量	3347.8

估计方法	测试工况	β_RMSE/(°)	ω_RMSE/((°)·s^-1)
EKF	蛇形	0.4863	2.6734
EKF	U-Turn	0.1934	1.1264
GPR-EKF	蛇形	0.2528	0.4836
GPR-EKF	U-Turn	0.1403	0.3838

估计方法	测试工况	β_RMSE/(°)	ω_RMSE/((°)·s^-1)
EKF	蛇形	0.4863	2.6734
EKF	U-Turn	0.1934	1.1264
GPR-EKF	蛇形	0.2528	0.4836
GPR-EKF	U-Turn	0.1403	0.3838

线程	99%分位耗时	平均耗时	最大耗时
GPR	7.859	6.453	9.370
MPC	1.021	0.976	1.350