基于模型预测控制的智能汽车轨迹跟踪方法比较

针对智能汽车轨迹跟踪问题,基于模型预测控制原理分别采用运动学模型和动力学模型为预测模型,通过对非线性系统进行线性化和离散化处理,根据约束条件、目标函数设计了2种轨迹跟踪控制器。基于Carsim与Matlab/Simulink联合仿真平台,在不同附着系数、不同车速下进行典型轨迹跟踪仿真试验。结果表明:不同工况下,2种模型预测控制器都有良好动态跟踪特性,动力学模型的跟随效果和控制平滑度略优于运动学模型。

Comparison of Intelligent Vehicle Track Tracking Method Based on Model Predictive Control

Aiming at the intelligent vehicle trajectory tracking problem,based on the principle of model predictive control,the kinematics model and dynamics model were used as the predictive models.According to the constraints and objectives,two kinds of trajectory tracking controllers had been designed by linearizing and discretizing the nonlinear system.After that,based on the co-simulation platform of Carsim and Matlab/Simulink,the typical trajectory tracking simulation tests were carried out at different speeds and different adhesive coefficients.The results show that under different working conditions,the two model prediction controllers have good dynamic tracking characteristics,the following effect and control smoothness of the dynamic model are slightly better than the kinematics model.