Lateral control using LQR for intelligent vehicles based on the optimal front-tire lateral force
CHEN Liang1, QIN Zhaobo1, KONG Weiwei2, CHEN Xin1
1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China; 2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
Abstract：A lateral control method for intelligent vehicles is developed based on the optimal front-tire lateral force to improve the lateral stability and the path tracking accuracy of intelligent vehicles going around large curvature turns. A linear quadratic regulator (LQR) controller using feedforward and feedback control is used to determine the desired front-tire lateral force in real time to reduce the tracking error. The control input is then converted into the desired steering angle based on the brush tire model. This method properly retains the nonlinear characteristics of the vehicle and tire models. The LQR controller is verified via simulations on PreScan. The results show that this LQR controller not only reduces the path tracking error relative to the general LQR method, but also ensures lateral stability of the vehicle.
陈亮, 秦兆博, 孔伟伟, 陈鑫. 基于最优前轮侧偏力的智能汽车LQR横向控制[J]. 清华大学学报（自然科学版）, 2021, 61(9): 906-912.
CHEN Liang, QIN Zhaobo, KONG Weiwei, CHEN Xin. Lateral control using LQR for intelligent vehicles based on the optimal front-tire lateral force. Journal of Tsinghua University(Science and Technology), 2021, 61(9): 906-912.
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2021, Vol. 61 
