基于模型预测控制的智能车轨迹跟踪控制研究

为了完成智能车的轨迹跟踪,提出一种基于模型预测控制的轨迹跟踪方法,利用将运动学模型这个非线性系统线性化的方案,来获得必须的线性时变系统,采取模型预测控制的三要素来设计控制器。并且基于MPC在控制过程中能增加多种约束的优点,建立基于车辆运动学模型的约束做轨迹跟踪仿真实验,最后,基于山东理工大学智能车平台上GPS提供的定位信息,在校园中采集路线并对前提规划好的的轨迹进行实车验证。实验结果表明:基于MPC算法所设计的控制器能快速且稳定地跟踪期望轨迹。

Path Tracking Control of Selfdriving Vehicles Based on Matlab Model Predictive Control

In order to complete the trajectory tracking of smart cars, a trajectory tracking method based on model predictive control is proposed. The linearization of the nonlinear system of the kinematic model is used to obtain the necessary linear time-varying system. The three elements of model predictive control are adopted. To design the controller. And based on the advantages that MPC can add a variety of constraints in the control process, a trajectory tracking simulation experiment is established based on the constraints of the vehicle kinematics model. Finally, based on the positioning information provided by the GPS on the intelligent vehicle platform of Shandong University of Technology, routes are collected on campus The real-world vehicle verification is performed on the trajectory planned in the premise. The experimental results show that the controller based on the MPC algorithm can track the desired trajectory quickly and stably.