• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2018, Vol. 54 ›› Issue (24): 166-173.doi: 10.3901/JME.2018.24.166

• 运载工程 • 上一篇    下一篇

无人驾驶车辆路径跟踪控制预瞄距离自适应优化

赵治国1,2, 周良杰1,2, 朱强1,2   

  1. 1. 同济大学新能源汽车工程中心 上海 201804;
    2. 同济大学汽车学院 上海 201804
  • 收稿日期:2017-11-20 修回日期:2018-07-14 出版日期:2018-12-20 发布日期:2018-12-20
  • 通讯作者: 赵治国(通信作者),男,1971年出生,教授,博士研究生导师。主要研究方向为车辆动力学控制、混合动力汽车系统集成控制及新型传动系统控制,发表论文160余篇。E-mail:zhiguozhao@tongji.edu.cn
  • 作者简介:周良杰,男,1981年出生,博士研究生。主要研究方向为车辆动力学控制与驾驶员辅助系统控制。E-mail:1410809@tongji.edu.cn
  • 基金资助:
    国家自然科学基金联合基金资助项目(U1564208)。

Preview Distance Adaptive Optimization for the Path Tracking Control of Unmanned Vehicle

ZHAO Zhiguo1,2, ZHOU Liangjie1,2, ZHU Qiang1,2   

  1. 1. Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804;
    2. School of Automotive Studies, Tongji University, Shanghai 201804
  • Received:2017-11-20 Revised:2018-07-14 Online:2018-12-20 Published:2018-12-20

摘要: 基于道路信息,使用驾驶员预瞄模型产生执行器输入是无人驾驶车辆在路径跟踪中使用的主要方法之一,但对于车速较高与转弯半径小等工况,模型误差会导致较差的驾驶舒适性,车辆甚至失去稳定性。为提高无人驾驶车辆路径的跟踪精度,同时兼顾转向频度和车辆稳定性,提出基于粒子群多目标优化(Particle swarm optimization,PSO)算法的预瞄距离自适应驾驶员模型,并将之应用于路径跟踪控制。首先,基于单点预瞄偏差模型,采用滑模变结构设计转向控制器;其次,以路径跟踪精度、转向频度和车辆稳定性为综合性能指标,设计了PSO优化算法,实现了驾驶员模型预瞄距离的自适应寻优。最后,在搭建的CarSim-Simulink联合仿真平台与台架试验上,对所提出的预瞄距离自适应驾驶员预瞄模型进行了仿真和硬件在环试验验证。结果表明,经优化后的预瞄距离能够适应不同车速和道路曲率,驾驶员预瞄模型能兼顾路径跟踪精度、转向频度和车辆稳定性等需求。预瞄距离自适应驾驶员模型结合道路与车速信息,增大对路况与车况适应性,为无人驾驶车辆路径跟踪控制提供可靠的输入。

关键词: 驾驶员模型, 粒子群优化, 路径跟踪, 无人驾驶车辆, 预瞄距离

Abstract: Based on the road information, using the driver preview model to generate the actuator input is one of the main method in unmanned vehicle path tracking, but for higher speed and small turn radius, the error of the driver preview model may induce less driving comfort and the vehicle even lose stability. In order to improve the accuracy of path tracking and ensure its steering frequency and stability, an adaptive optimization control strategy for preview distance is proposed, which is based on particle swarm optimization(PSO) algorithm. Firstly, based on the tracking error, designed a driver preview model by using slide mode control. Secondly, an adaptive algorithm based on PSO is proposed, which takes into account of tracking accuracy, steering frequency and stability. Finally, the proposed algorithm is tested on co-simulation platform of CarSim and Simulink software and driving simulator test bench. The results show that the algorithm is effective and it can ensure path tracking accuracy, steering controllability and stability. The optimized preview distance driver model combined the information of road and speed, and provides reliable input for unmanned vehicle path tracking control.

Key words: driver model, path tracking, preview distance, PSO, unmanned vehicle

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