重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (1): 37-46.

• “复杂环境智能汽车感知与控制”专栏 • 上一篇    下一篇

智能汽车仿人换道 TSK模糊可拓控制研究

耿国庆,丁鹏程,江浩斌   

  1. 1.江苏大学 汽车与交通工程学院,江苏 镇江 212013; 2.江苏大学 汽车工程研究院,江苏 镇江 21201
  • 出版日期:2023-02-16 发布日期:2023-02-16
  • 作者简介:耿国庆,男,博士,教授,主要从事驾驶辅助(ADAS)智能车辆及仿人转向系统方面研究,Email:ggq@ujs. edu.cn。

Research on TSK fuzzy extension control of human simulated lane-changing of intelligent vehicles

  • Online:2023-02-16 Published:2023-02-16

摘要: 为提高智能汽车自主换道的轨迹跟踪精度和乘员舒适性,提出了一种将可拓控制 与 TSK(TakagiSugenoKang)模糊控制相结合的智能汽车仿人换道控制方法。通过驾驶模拟器 对熟练驾驶员的实际驾驶轨迹数据进行采集,基于广义回归神经网络进行仿人理想轨迹拟合。 为提高智能汽车对拟合轨迹的跟踪能力,引入可拓控制策略根据系统状态划分不同控制域,并 在经典域和可拓域分别采用 PID反馈控制和 PID前馈 -反馈控制,解决单一控制算法的局限 性。为进一步改善可拓控制在不同控制域边界的抖动问题,采用了 TSK模糊控制对其稳定性 进行优化。仿真结果表明,该控制算法保证在不同换道工况下都具有较高的跟踪精度和舒 适性。

关键词: 智能汽车, 换道, 熟练驾驶员, 可拓控制, TSK模糊控制

Abstract: Intelligent vehicles have become a research hotspot in academia and industry, mainly because the driving tasks of intelligent vehicles can be performed by the auto drive system, which greatly reduces the driving burden of human drivers and improves driving efficiency and safety. A large number of traffic jams and casualties are caused by the factor of “people”, and automatic driving can eliminate the hidden danger of “people” from the “people-vehicle-road” system, thereby greatly enhancing the safety of road traffic. Relevant research shows that the difference between an autonomous vehicle and a skilled driver is that the former gives people less comfort in steering and other operations. As one of the key operations of vehicles in the driving process, lane changing puts forward higher requirements for the transverse and longitudinal control of vehicles. In order to improve the tracking accuracy and occupant comfort of an intelligent vehicle in autonomous lane changing, this paper proposes a control method of intelligent vehicle lane changing by imitating human beings, which combines the extension control with TSK (Takagi Sugeno Kang) fuzzy control. First of all, five driving school coaches with rich driving experience are recruited as representatives of skilled drivers, the lane change track data of skilled drivers are collected by a combination of real vehicles and driving simulators, and the test track is analyzed to study the impact of different factors on lane change path. The behavior of intelligent vehicle lane changing has strong nonlinear characteristics. Based on generalized regression neural network (GRNN), a path planning model for human like lane changing is designed. Secondly, the relationship between preview position deviation and the yaw angle is derived so as to establish the “vehicle road” system model by analyzing the preview characteristics of skilled drivers and combining the preview deviation, road environment and vehicle state. In order to improve the tracking accuracy and occupant comfort of intelligent vehicles under different lane changing conditions, a humanoid trajectory tracking controller based on extension theory is designed. The controller can be divided into upper and lower layers. The upper layer controller mainly extracts the tracking characteristics of intelligent vehicles and divides the classical domain, extension domain and non domain. In order to meet the control requirements of intelligent vehicles in different control domains, the lower controller adopts PID feedback control based on road curvature and emergency braking respectively, which solves the limitations of a single control method and realizes accurate control in the global range. Because the extension control will produce control quantity jitter at the switching point, and the local stability will be affected, an optimization method based on TSK fuzzy theory is proposed to further improve the global stability and tracking accuracy of the extension control. By analyzing the results of joint simulation, it can be concluded that the extension control method based on TSK fuzzy theory proposed in this paper has higher control accuracy and stability.

中图分类号: 

  • TP212