A Model Predictive Control Based Path Tracker in Mixed-Domain

被引:7
|
作者
Hu, Jia [1 ]
Feng, Yongwei [1 ]
Li, Xin [2 ]
Wang, Haoran [1 ]
机构
[1] Tongji Univ, Key Lab Rd & Traff Engn, Minist Educ, Shanghai 201804, Peoples R China
[2] Dalian Maritime Univ, Coll Transportat Engn, Dalian, Peoples R China
来源
2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) | 2021年
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
MOBILE ROBOT; TRAJECTORY TRACKING; AUTONOMOUS VEHICLES; NEURAL-NETWORK; DESIGN; SYSTEM;
D O I
10.1109/IV48863.2021.9575934
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This research proposes a Model Predictive Control (MPC) based path tracker controller. It is designed for maneuvering an autonomous driving vehicle to follow its desired trajectory smoothly and accurately. The proposed path tracker has the following features: i) formulated in the time and space mixed-domain to improved control accuracy ii) with consideration of vehicle dynamics; iii) with consideration of vehicle control delay. Simulation and field test results demonstrate that the maximum longitudinal speed error is 2.3km/h and the maximum lateral position error is 11cm. It is 27% smaller than that of the conventional path-trackers. Moreover, the average computation time of the proposed path-tracker is 12 milliseconds on a laptop equipped with an Intel i7-4710MQ CPU. It indicates that the proposed path tracker is ready for real-time implementation.
引用
收藏
页码:1255 / 1260
页数:6
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