Abstract: In the context of intelligent vehicle path tracking, the model predictive control (MPC) method with a fixed prediction time domain often suffers from poor local path tracking accuracy and stability. To tackle these issues, an adaptive fuzzy model predictive control (AFMPC) algorithm was designed, which optimized and adjusted the prediction step size of the controller online according to the longitudinal vehicle speed and the curvature of the reference path. Simulink/CarSim were used to conduct a double lane change test at high, medium, and low speeds. The results show that the AFMPC controller achieves overall better path-tracking accuracy than the fixed-parameter MPC. Compared with the optimal fixed-parameter controller under each operating condition, its lateral average error is significantly reduced by 12.25% in the low-speed scenario, remains nearly unchanged (a 0.37% decrease) in the high-speed scenario, and is comparable to that of the optimal fixed-parameter MPC in the medium-speed scenario. Meanwhile, the vehicle yaw rate and centroid sideslip angle maintain a level comparable to those of the fixed-parameter MPC under all three operating conditions. Therefore, adjusting the prediction horizon in real-time according to changes in road and vehicle conditions is beneficial for improving the driving accuracy and stability of intelligent vehicles.

Key words: vehicle engineering; path tracking; lateral control; tire parameter estimation; adaptive fuzzy model predictive control

摘要： 为解决智能车辆路径跟踪过程中，固定预测时域模型下存在的局部路径跟踪准确性和稳定性较差的问题，设计了一种基于自适应模糊模型预测控制（AFMPC）的算法，该方法根据纵向车速和参考路径曲率对控制器预测步长进行在线优化调节，运用Simulink/CarSim在高、 中、 低3种车速下进行双移线测试。结果表明：AFMPC控制器在路径跟踪精度上整体优于固定参数模型预测控制（MPC），与每种工况下最优固定参数的控制器相比，其横向平均误差在低速工况下显著降低12.25%，高速工况时基本持平（降幅0.37%），中速工况时与最优固定参数MPC性能相当； 同时，车辆横摆角速度与质心侧偏角在3种工况下均保持与固定参数MPC相当的水平。因此，根据车路条件变化实时调整预测时域有利于提高智能车辆的行驶精确性和稳定性。

关键词: 车辆工程； 路径跟踪； 横向控制； 轮胎参数估计； 自适应模糊模型预测控制