面向复杂曲率变化的智能车路径跟踪控制  

Intelligent Vehicle Path Tracking Control Based on Complex Curvature Variation

作　　者：梁军[1] 朱方博 蔡英凤[1] 陈小波[1] 陈龙[1] Jun Liang;Fangbo Zhu;Yingfeng Cai;Xiaobo Chen;Long Chen(Automotive Engineering Research Institute,Jiangsu University,Zhenjiang212013)

机构地区：[1]江苏大学汽车工程研究院,镇江212013

出　　处：《汽车工程》2021年第12期1771-1779,共9页Automotive Engineering

基　　金：国家重点研发计划(2017YFB0102603);国家自然科学基金(U1564201);江苏省交通运输与安全保障重点建设实验室开放课题(TTS2018-05)。

摘　　要：针对智能车路径跟踪过程中对于复杂曲率变化工况适应能力弱的问题,提出了一种基于RBF神经网络补偿模型预测的控制方法。首先以3自由度智能车动力学模型作为预测模型,通过对线性时变方程分析后得到状态转移误差模型,利用RBF神经网络自适应补偿误差,保证控制的精确性,提高了路径跟踪准确性。最后,以中国智能汽车大赛比赛赛道为原型构建了包括直线路段、蛇行路段与双移线路段的复杂路径曲率变化工况,在半实车仿真平台上验证了高速环境下控制方法的路径跟踪效果。结果显示,最大轨迹跟踪误差在0.285 m范围内,并且侧向加速度最大为0.3299 m/s2,保证了路径跟踪的准确性与稳定性。For the problem of weak adaptability to complex curvature changing conditions in the path tracking of intelligent vehicle,a control method based on RBF neural network compensation of model prediction is proposed.Firstly,the three-degree-of-freedom intelligent vehicle dynamics model is used as the prediction model.Then the state transition error model is obtained by analyzing the linear time-varying equations.The adaptive compensation for error by the RBF neural network is realized to ensure the accuracy of the control and improve the path tracking accuracy.Finally,the complex path curvature changing condition including straight line segment,serpentine segment and double-shift line segment is constructed based on the China Smart Car Competition track.The path tracking performance of the control method in high-speed environment is verified on the semi-real vehicle simulation platform.The results show that the maximum trajectory tracking error is within the range of 0.285 m,and the maximum lateral acceleration is 0.3299 m/s2,which ensures the accuracy and stability of the path tracking.

关 键 词：智能车 路径跟踪 复杂曲率变化 误差补偿 

分 类 号：U463.6[机械工程—车辆工程]