基于自适应路径预览的非线性模型预测控制路径跟踪

Path-Following Based on Nonlinear Model Predictive Control with Adaptive Path Preview

摘要:

提出了一种用于自动驾驶汽车路径跟踪的非线性模型预测控制器（NMPC）和自适应调整预览距离的算法。预测模型包括车辆动力学、路径跟随动力学和系统输入动力学。单轨车辆模型考虑了车辆的横向和纵向耦合动力学以及非线性轮胎力。基于曲线坐标推导了跟踪误差动力学。成本函数被设计为最小化路径跟踪误差和控制努力，同时考虑诸如致动器边界和轮胎抓地限制之类的约束。一种利用最佳预览距离矢量来查询相应参考曲率和参考速度的算法。基于车辆速度、航向误差和路径曲率自适应地调整预览路径的长度。在具有自主赛车场景的仿真环境中验证了控制器的性能，结果表明，车辆准确地遵循高度动态的路径，跟踪误差较小。所提出的模型与算法可以预先估计最大预览距离，并指导NMPC的预测范围的选择。

Abstract:

This paper presents a Nonlinear Model Predictive Controller （NMPC） for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance. The prediction model includes vehicle dynamics， path following dynamics， and system input dynamics. The single-track vehicle model considers the vehicle’s coupled lateral and longitudinal dynamics， as well as nonlinear tire forces. The tracking error dynamics are derived based on the curvilinear coordinates. The cost function is designed to minimize path tracking errors and control effort while considering constraints such as actuator bounds and tire grip limits. An algorithm that utilizes the optimal preview distance vector to query the corresponding reference curvature and reference speed. The length of the preview path is adaptively adjusted based on the vehicle speed， heading error， and path curvature. We validate the controller performance in a simulation environment with the autonomous racing scenario. The simulation results show that the vehicle accurately follows the highly dynamic path with small tracking errors. The maximum preview distance can be prior estimated and guidance the selection of the prediction horizon for NMPC.