Open AccessAdaptive neural data‐based compensation control of non‐linear systems with dynamic uncertainties and input saturation
Author(s) -
Wang Huanqing,
Liu Xiaoping,
Liu Kefu
Publication year - 2015
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.2014.0709
Subject(s) - control theory (sociology) , compensation (psychology) , saturation (graph theory) , adaptive control , computer science , control engineering , artificial neural network , control (management) , engineering , mathematics , artificial intelligence , psychology , psychoanalysis , combinatorics
In this study, an adaptive neural backstepping control scheme is proposed for a class of strict‐feedback non‐linear systems with unmodelled dynamics, dynamic disturbances and input saturation. To solve the difficulties from the unmodelled dynamics and input saturation, a dynamic signal and smooth function in non‐affine structure subject to the control input signal are introduced, respectively. Radial basis function (RBF) neural networks are used to approximate the packaged unknown non‐linearities, and an adaptive neural control approach is developed via backstepping, which guarantees that all the signals in the closed‐loop system are semi‐globally uniformly ultimately bounded in mean square. The main contributions of this note lie in that a control strategy is provided for a class of strict‐feedback non‐linear systems with unmodelled dynamics uncertainties and input saturation, and the proposed control scheme does not require any information of the bound of input saturation non‐linearity. Simulation results are used to show the effectiveness of the proposed control scheme.