Open AccessTrajectory Tracking Control for Double-steering Automated Guided Vehicle Based on Model Predictive Control
Author(s) -
Jiahui Qi,
Yaohua Wu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1449/1/012107
Subject(s) - trajectory , control theory (sociology) , model predictive control , robustness (evolution) , kinematics , tracking (education) , computer science , control engineering , controller (irrigation) , vehicle dynamics , engineering , control (management) , artificial intelligence , psychology , pedagogy , biochemistry , chemistry , physics , classical mechanics , automotive engineering , gene , agronomy , astronomy , biology
In order to solve the trajectory tracking control problem of double-steering automated guided vehicle (AGV), considering various constraints in practical work, a trajectory tracking controller based on model predictive control is designed. Firstly, the kinematic model of double-steering AGV is established. Then, a trajectory tracking model based on model predictive control is designed to achieve fast and accurate tracking performance. Finally, the influence of the predicted time-domain length on the system performance is analyzed by simulation, and the simulation experiment is compared with that of the AGV based on the proportional integral differential trajectory tracking controller. The experiment result shows that the model predictive controller can meet all kinds of constraints in actual work, and complete the tracking of the continuous high curvature target path efficiently and accurately in real-time with high robustness.