Open AccessAdaptive backstepping control for strict‐feedback non‐linear systems with input delay and disturbances
Author(s) -
Ma Jiali,
Xu Shengyuan,
Cui Guozeng,
Chen Weimin,
Zhang Zhengqiang
Publication year - 2019
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.2018.5326
Subject(s) - backstepping , control theory (sociology) , adaptive control , mathematics , tracking error , artificial neural network , bounded function , lyapunov function , stability (learning theory) , neighbourhood (mathematics) , padé approximant , lyapunov stability , computer science , nonlinear system , control (management) , artificial intelligence , mathematical analysis , physics , quantum mechanics , machine learning
The problem of adaptive neural networks (NNs) control for a class of uncertain non‐linear systems with input delay and disturbances is studied. By using Pade approximation method, an auxiliary system is constructed to compensate the input delay based on the introduced variable. NNs are used to approximate the unknown non‐linear functions. With the aid of backstepping technique, adaptive NNs controllers are designed which can guarantee all the signals in the closed‐loop systems are semi‐globally uniformly ultimately bounded and the tracking error can be adjusted around the origin with a small neighbourhood. The stability of the closed‐loop systems is proved by using the Lyapunov stability theorem and two simulation examples are given to illustrate the effectiveness of the proposed methods.