Open AccessOptimal control of high speed unmanned vehicle path tracking
Author(s) -
Shengjun Fan,
Tao Xie,
Zhigang Chen,
Xuan Hu,
Lei Gao
Publication year - 2022
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 - 10.1088/1742-6596/2195/1/012006
Subject(s) - carsim , control theory (sociology) , robustness (evolution) , model predictive control , matlab , vehicle dynamics , computer science , tracking (education) , flight envelope , stability (learning theory) , controller (irrigation) , control engineering , engineering , automotive engineering , control (management) , aerospace engineering , artificial intelligence , aerodynamics , psychology , pedagogy , biochemistry , chemistry , agronomy , machine learning , biology , gene , operating system
In order to ensure the accuracy and stability of tracking control of driverless vehicle at high speed, an improved model predictive control method was designed. First of all, for the current high-speed unmanned vehicle tracking control field, there is little research and the tracking accuracy is poor. Starting from the three-degree-of-freedom vehicle dynamics model, by analyzing the vehicle yaw stability, the model is added with envelope constraints, and the linear time is derived. The variable path tracking controller model is designed with or without additional constraints. Through Matlab and CarSim co-simulation, it is shown that the MPC controller with envelope constraint has higher tracking accuracy than the ordinary MPC controller, and the vehicle has better robustness to the road adhesion coefficient, and the vehicle driving stability is significantly improved.