Intelligent vehicle lateral tracking control based on multiple model prediction

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.