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Adaptive neural network tracking control for uncertain nonlinear systems with input delay and saturation
Author(s) -
Ma Jiali,
Xu Shengyuan,
Zhuang Guangming,
Wei Yunliang,
Zhang Zhengqiang
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4887
Subject(s) - backstepping , control theory (sociology) , nonlinear system , artificial neural network , bounded function , tracking error , computer science , saturation (graph theory) , adaptive control , compensation (psychology) , adaptive system , mathematics , control (management) , artificial intelligence , mathematical analysis , physics , quantum mechanics , combinatorics , psychoanalysis , psychology
Summary In this article, the adaptive tracking control problem is considered for a class of uncertain nonlinear systems with input delay and saturation. To compensate for the effect of the input delay and saturation, a compensation system is designed. Radial basis function neural networks are directly utilized to approximate the unknown nonlinear functions. With the aid of the backstepping method, novel adaptive neural network tracking controllers are developed, which can guarantee all the signals in the closed‐loop system are semiglobally uniformly ultimately bounded, and the system output can track the desired signal with a small tracking error. In the end, a simulation example is given to illustrate the effectiveness of the proposed methods.