In order to track the desired path as fast as possible, a novel autonomous vehicle path tracking based on model predictive control (MPC) and PID speed control was proposed for high-speed automated vehicles considering the constraints of vehicle physical limits, in which a forward-backward integration scheme was introduced to generate a time-optimal speed profile subject to the tire-road friction limit. Moreover, this scheme was further extended along one moving prediction window. In the MPC controller, the prediction model was an 8-degree-of-freedom (DOF) vehicle model, while the plant was a 14-DOF vehicle model. For lateral control, a sequence of optimal wheel steering angles was generated from the MPC controller; for longitudinal control, the total wheel torque was generated from the PID speed controller embedded in the MPC framework. The proposed controller was implemented in MATLAB considering arbitrary curves of continuously varying curvature as the reference trajectory. The simulation test results show that the tracking errors are small for vehicle lateral and longitudinal positions and the tracking performances for trajectory and speed are good using the proposed controller. Additionally, the case of extended implementation in one moving prediction window requires shorter travel time than the case implemented along the entire path.

为了尽可能快地跟踪期望的路径，提出了一种基于模型预测控制（MPC）和PID速度控制的新型自主车辆路径跟踪方法，该方法考虑了车辆物理限制的约束，用于高速自动车辆。引入后向积分方案以产生受轮胎-道路摩擦极限的时间最优速度曲线。此外，该方案沿一个移动预测窗口进一步扩展。在MPC控制器中，预测模型是8自由度（DOF）车辆模型，而工厂是14 DOF车辆模型。对于横向控制，从MPC控制器生成了一系列最佳车轮转向角；对于纵向控制，车轮总扭矩由MPC框架中嵌入的PID速度控制器生成。所提出的控制器是在MATLAB中实现的，其中将连续变化的曲率的任意曲线作为参考轨迹。仿真测试结果表明，所提控制器对车辆横向和纵向位置的跟踪误差较小，轨迹和速度的跟踪性能均良好。另外，在一个移动预测窗口中扩展实施的情况比在整个路径上实施的情况需要更短的行驶时间。