Premium
Quantized control for a class of neural networks with adaptive event‐triggered scheme and complex cyber‐attacks
Author(s) -
Liu Jinliang,
Suo Wei,
Xie Xiangpeng,
Yue Dong,
Cao Jinde
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.5500
Subject(s) - denial of service attack , artificial neural network , computer science , cyber physical system , lyapunov stability , lyapunov function , control theory (sociology) , controller (irrigation) , quantization (signal processing) , scheme (mathematics) , set (abstract data type) , control (management) , computer security , artificial intelligence , mathematics , algorithm , the internet , nonlinear system , quantum mechanics , world wide web , agronomy , biology , programming language , operating system , mathematical analysis , physics
This article is concerned with the quantized control problem for neural networks with adaptive event‐triggered scheme (AETS) and complex cyber‐attacks. By fully considering the characteristics of cyber‐attacks, a mathematical model of complex cyber‐attacks, which consists of replay attacks, deception attacks, and denial‐of‐service (DoS) attacks, is firstly built for neural networks. For the sake of relieving the pressure under limited communication resources, an AETS and a quantization mechanism are employed in this article. By utilizing Lyapunov stability theory, adequate conditions ensuring the stability of neural networks are obtained. Moreover, the controller gain is derived by solving a set of linear matrix inequalities. At last, the usefulness of the proposed method is verified by a numerical example.