为了提高无人车在不同路面附着系数和车速下的轨迹跟踪控制性能，提出一种自适应时域模型预测控制（model predictive control, MPC）算法。首先，基于三自由度车辆动力学模型设计MPC轨迹跟踪控制器。其次，引入融合准反射学习和高斯变异的粒子群优化算法（particle swarm optimization, PSO）对时域参数优化，获得不同工况下的离线最优时域数据集。然后，利用自适应神经模糊推理系统（adaptive network-based fuzzy inference system, ANFIS）对数据集训练，得到能够自适应调整时域的控制系统。最后，通过Carsim和Simulink联合仿真和实车验证。结果表明：自适应时域MPC控制器在不同工况下的轨迹跟踪精度和稳定性均得到了较大幅度的提高，且该算法具有较好的实用性。

In order to improve the trajectory tracking control performance of unmanned vehicles under different road adhesion coefficients and vehicle speeds, an adaptive horizon model predictive control (MPC) algorithm was proposed. First, the MPC trajectory tracking controller was designed based on a three-degree-of-freedom vehicle dynamics model. Second, a particle swarm optimization (PSO) algorithm incorporating quasi-reflective-based learning and Gaussian variation was introduced to optimize the horizon parameters and obtain the offline optimal horizon data sets under different operating conditions. Then, the adaptive network-based fuzzy inference system (ANFIS) was utilized to train the dataset to obtain a control system that can adaptively adjust the horizon. Finally, it was verified by Carsim and Simulink joint simulation and real vehicle verification. The results show that the trajectory tracking accuracy and stability of the MPC controller with adaptive horizon under different working conditions are improved substantially, which shows that the algorithm has good practicality.