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Adaptive neural tracking control for nonstrict‐feedback nonlinear systems with unknown backlash‐like hysteresis and unknown control directions
Author(s) -
Wang Xinjun,
Yin Xinghui,
Wu Qinghui,
Meng Fanqi
Publication year - 2018
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.4303
Subject(s) - backstepping , control theory (sociology) , backlash , nonlinear system , controller (irrigation) , artificial neural network , adaptive control , computer science , bounded function , signal (programming language) , control engineering , control (management) , engineering , mathematics , artificial intelligence , mathematical analysis , physics , quantum mechanics , agronomy , biology , programming language
Summary In this paper, a neural network–based adaptive tracking control problem is investigated for a class of nonstrict‐feedback nonlinear systems in the presence of unknown backlash‐like hysteresis nonlinearity, unmodeled dynamics, and unknown control directions. A state feedback controller is developed for the considered system by applying the adaptive backstepping technique and neural networks. The design difficulties exhibited in this paper due to unmodeled dynamics, unknown control directions, and nonstrict‐feedback form are handled by resorting to a dynamic signal, a Nussbaum function, and the variable separation approach, respectively. It is shown that the designed adaptive controller can guarantee that all the signals remain bounded and that the desired signal can be tracked with a small domain of the origin. A numerical example and an example of a real plant for a one‐link manipulator are provided to show the feasibility of the newly designed controller scheme.