基于前馈和多输入模糊LQR的路径跟踪控制研究
Research on Path Tracking Control Based on Feedforward and Multi-input Fuzzy LQR
邹天越 1喻厚宇 1何博 1胡永康 1尹思源1
作者信息
- 1. 武汉理工大学现代汽车零部件技术湖北省重点实验室,武汉 430070;武汉理工大学汽车零部件技术湖北省协同创新中心,武汉 430070;武汉理工大学湖北省新能源与智能网联车工程技术研究中心,武汉 430070
- 折叠
摘要
针对智能车路径跟踪控制问题,提出了一种基于前馈和多输入模糊LQR路径跟踪控制器.首先,建立了车辆二自由度动力学模型,构建了二自由度路径跟踪误差模型,设计了 LQR控制器和前馈控制器.然后,针对传统LQR控制器对多变行驶工况适应性较差的问题,设计了一种以智能车行驶速度、道路曲率和路径跟踪的横向误差作为输入的模糊控制规则,对LQR控制器的权重进行调节.最后,使用AGV线控底盘进行实验,对该控制器进行了验证.结果表明,对于连续变道工况和双移线工况,设计的控制器能够较好地对目标路径进行跟踪.
Abstract
Aiming at the problem of intelligent vehicle path tracking control,a path tracking controller based on feed-forward and muti-input fuzzy LQR is proposed.Firstly,a 2-DOF vehicle dynamics model is established,a 2-DOF path tracking error model is constructed,and an LQR controller and a feedforward controller are designed.Then,aiming at the poor adaptability of the traditional LQR controller to the changeable driving scenarios,a fuzzy control rule is designed which takes intelligent vehicle speed,the road curvature and the lateral error of path tracking as input.The weights of the LQR controller are adjusted by the proposed fuzzy rules.Finally the controller is validated through experiments using an AGV wire controlled chassis.The results show that the designed controller can track the target path well for continuous lane changing scenario and double lane changing scenario.
关键词
智能车/路径跟踪/前馈控制/多输入模糊LQRKey words
intelligent vehicles/path tracking/feedforward control/multi-input fuzzy LQR引用本文复制引用
出版年
2024
NETL
NSTL
万方数据