Open AccessAdaptive neural control of state delayed non‐linear systems with unmodelled dynamics and distributed time‐varying delays
Author(s) -
Zhang Tianping,
Xia Xiaonan,
Zhu Jiaming
Publication year - 2014
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2013.0803
Subject(s) - control theory (sociology) , recursion (computer science) , state (computer science) , computer science , artificial neural network , adaptive control , function (biology) , mathematics , control (management) , algorithm , artificial intelligence , evolutionary biology , biology
In this study, a robust adaptive control is proposed for a class of strict‐feedback state delayed non‐linear systems with unmodelled dynamics and distributed time‐varying delays using radial basis function neural networks. Dynamic uncertainties are dealt with using separation technique and introducing a dynamic signal. The terms including state time‐varying delay and distributed time‐varying delay uncertainties are compensated for by constructing appropriate Lyapunov–Krasovskii functionals. Using Young's inequality, only one learning parameter need to be tuned online at each step of recursion. It is proved that the proposed design method is able to guarantee semi‐global uniform ultimate boundedness of all signals in the closed‐loop system. Simulation results demonstrate the effectiveness of the proposed approach.