关键词: 智能汽车, 轨迹跟踪, 变论域, 自适应, 预测控制

Abstract: To improve the real-time and adaptive performance of intelligent automobile trajectory tracking, a method of variable universe adaptive fuzzy model predictive control (VUFAMPC) was proposed. Firstly,the strong tracking square root cubature kalman filter (ST-SRCKF) was used to estimate tire lateral force to obtain real-time tire lateral stiffness values. Secondly, according to the traditional method of model predictive control of trajectory tracking, a variable universe fuzzy control(VUFC) was used to design the variable universe fuzzy model predictive controller (VUFMPC) and combined it with ST-SRCKF, the estimated tire lateral stiffness was used as the parameters for VUFMPC to achieve adaptive correction of controller parameters, thereby obtaining VUFAMPC. Last, comparative analysis and verification through HIL experiment. The experiment result show that compared to FMPC and AMPC,the overshoot of VUFAMPC reduce by 11.1% and 18.8%, transition time reduce by 46.6% and 67.9%; the trajectory tracking tests shows:the maximum tracking error optimize by 73.9%,68.7%,24.8%, the average tracking error optimize by 72.3%,56.1%,28%. The conclusion indicates that VUFAMPC has better real-time and adaptive performance, while effectively balancing the accuracy of trajectory tracking and driving stability.

Key words: intelligent automobile, trajectory tracking, variable universe, adaptive, predictive control