Premium
Finite‐time adaptive neural control for nonlinear systems under state‐dependent sensor attacks
Author(s) -
Lv Wenshun
Publication year - 2021
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.5498
Subject(s) - backstepping , control theory (sociology) , nonlinear system , controller (irrigation) , computer science , stability (learning theory) , artificial neural network , adaptive control , state (computer science) , scheme (mathematics) , function (biology) , control (management) , control engineering , mathematics , engineering , artificial intelligence , algorithm , machine learning , mathematical analysis , physics , quantum mechanics , evolutionary biology , agronomy , biology
Abstract This article proposes a finite‐time control scheme for a class of uncertain nonlinear systems in the presence of sensor attacks. Specifically, based on the backstepping technology and the nonlinear function approximation capability of radial basis function neural networks, we develop an adaptive controller, guaranteeing the finite‐time stability of the closed‐loop system despite sensor attacks. In particular, the unknown time‐varying state‐feedback gains caused by sensor attacks are handled by Nussbaum functions. To decrease the design difficulty of the finite‐time controller, a novel practical finite‐time stability criterion is given. Finally, two simulation examples are provided, demonstrating the effectiveness of the proposed adaptive control scheme.