智能汽车仿人换道TSK模糊可拓控制研究  

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

作　　者：耿国庆[1] 丁鹏程 江浩斌[1,2] 唐斌[2] GENG Guoqing;DING Pengcheng;JIANG Haobin;TANG Bin(School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013,China;Automotive Engineering Research Institute,Jiangsu University,Zhenjiang 212013,China)

机构地区：[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212013

出　　处：《重庆理工大学学报（自然科学）》2023年第1期37-46,共10页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(51675235)。

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。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 occu

关 键 词：智能汽车 换道 熟练驾驶员 可拓控制 TSK模糊控制 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]