摘要
针对智能车辆的轨迹跟踪控制问题,提出了一种可以调节参数的智能车辆轨迹跟踪控制方法.首先,设计了模糊控制器对智能车辆进行路径跟踪控制;其次,为了提高车辆在高速下的路径跟踪效果,设计模型预测控制器,并结合轮胎的动力学特性及车辆动态特性对轮胎侧偏角、质心侧偏角等进行约束;然后,为了提高车辆在不同工况下的路径跟踪效果,进一步设计了基于PSO算法的模型预测控制器.比较三种控制器的控制效果,选择典型工况在联合仿真平台上进行仿真.结果表明,提出的智能车辆的轨迹跟踪控制方法可以有效地对车辆轨迹进行跟踪.
Aiming at the trajectory tracking control problem of intelligent vehicles,the trajectory tracking control method with adjustable parameters of intelligent vehicles was presented.Firstly,the fuzzy controller was designed to track intelligent vehicles.Secondly,in order to improve the path tracking effect of vehicles at high speeds,the model prediction controller was further designed,and the tire side deviation angle and centroid side deviation angle were restrained by combining the dynamic characteristics of tires and vehicles.Then,in order to improve the path tracking effect of vehicles under different working conditions,a model prediction controller based on PSO algorithm was further designed.The control effects with different methods were compared.Finally,the typical working conditions were selected for simulation on the joint simulation platform.The results showed that the proposed trajectory tracking control method of intelligent vehicles can effectively track the vehicle trajectory.
作者
唐传茵
赵懿峰
赵亚峰
周淑文
TANG Chuan-yin;ZHAO Yi-feng;ZHAO Ya-feng;ZHOU Shu-wen(School of Mechanical Engineering&Automation,Northeastern University,Shenyang 110819,China;Shanghai Superior Die Technology Co.,Ltd.,Shanghai 201209,China)
出处
《东北大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2020年第9期1297-1303,共7页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(51505071)
企业合作项目(新能源车动力性经济性分析)(2018020900024)。
关键词
智能车辆
轨迹跟踪
模型预测控制
模糊控制
粒子群优化(PSO)
intelligent vehicle
trajectory tracking
model predictive control
fuzzy control
particle swarm optimization(PSO)