Open AccessNeural Network Based Modelling and Steering Control of an Intelligent Vehicle Under Dynamic Sensitive Conditions
Author(s) -
K. Zhang,
S. J. Wang,
Li Ji,
C. Wang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/825/1/012043
Subject(s) - artificial neural network , control theory (sociology) , vehicle dynamics , term (time) , computer science , controller (irrigation) , lyapunov function , control (management) , stability (learning theory) , lyapunov stability , intelligent control , control engineering , engineering , automotive engineering , artificial intelligence , nonlinear system , agronomy , physics , quantum mechanics , machine learning , biology
A neural network based modelling and steering control method of an intelligent vehicle under dynamic sensitive conditions is proposed in this paper. Four radius function neural networks are used to compensate the modelling error of a traditional dynamic vehicle steering model. The designed control law is consisted of four terms: a neural network term, a proportion term, an integral term and a robust term. Stability is analysed through Lyapunov function and the training law is generated at the same time. Test results show that the designed steering controller works quite well and is of great value in path tracking of an intelligent vehicle when the vehicle dynamics getting significant.