A new multi-model predictive control (MMPC) algorithm was proposed and applied in an intelligent vehicle lateral tracking control system in this paper, which is better to adapt the intelligent vehicle lateral tracking control under complex multi-conditions. First, the Gustafson–Kessel algorithm was used for the cluster analysis based on the vehicle test data to obtain the clustering center and train the sample data of each typical steering condition. Then, a multi-model structure was constructed by least squares support vector machines, and the sub-models of each category were taken as the prediction model for the application of MPC. Hence, the objective function of multi-objective optimization can be established and the multi-objective optimization problem was solved by the non-dominated sorted genetic algorithm-II algorithm to obtain the optimal control quantity. Finally, the MMPC-based intelligent vehicle lateral tracking control system was used to control the vehicle lateral tracking under three steering conditions, including straight line, normal right turn, and U-turn, through a simulation study in the MATLAB/Simulink environment. By comparing the vehicle trajectory, steering wheel angle, lateral deviation, and lateral angle, the performance of the proposed control method was verified. The experimental analysis results show that the proposed method can track the steering wheel angle of the vehicle reference trajectory under various working conditions. The vehicle lateral deviation value can be controlled in the range of (−1.0 m, 0.5 m). The high-precision lateral tracking control ensures that the yaw rate of the vehicle can track the yaw velocity under the reference driving track and guarantees the driving stability of intelligent vehicles.

中文翻译：

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基于多模型预测的智能车辆横向跟踪控制

提出了一种新的多模型预测控制算法，并将其应用于智能车辆横向跟踪控制系统中，更好地适应了复杂多条件下的智能车辆横向跟踪控制。首先，将Gustafson-Kessel算法用于基于车辆测试数据的聚类分析，以获得聚类中心并训练每种典型转向条件的样本数据。然后，通过最小二乘支持向量机构建多模型结构，并将每个类别的子模型作为预测模型，用于MPC的应用。因此，可以建立多目标优化的目标函数，并通过非支配排序遗传算法-II算法解决多目标优化问题，以获得最优控制量。最后，通过基于MATLAB / Simulink环境的仿真研究，基于MMPC的智能车辆横向跟踪控制系统被用于在三种转向条件下控制车辆横向跟踪，包括直线，正常右转和U形转弯。通过比较车辆的轨迹，方向盘角度，横向偏差和横向角度，验证了所提出的控制方法的性能。实验分析结果表明，该方法能够在各种工况下跟踪车辆参考轨迹的方向盘转角。车辆横向偏差值可以被控制在（-1.0m，0.5m）的范围内。高精度的横向跟踪控制，确保了车辆的横摆率可以在参考行驶轨迹下跟踪横摆速度，并保证了智能车的行驶稳定性。