Open AccessQuantised control for local Mittag–Leffler stabilisation of fractional‐order neural networks with input saturation: A refined sector condition
Author(s) -
Fan Yingjie,
Huang Xia,
Wang Zhen,
Li Yuxia
Publication year - 2022
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/cth2.12220
Subject(s) - saturation (graph theory) , artificial neural network , control theory (sociology) , mathematics , order (exchange) , fractional calculus , control (management) , biological system , computer science , economics , artificial intelligence , biology , combinatorics , finance
This brief focuses on the local Mittag–Leffler stabilisation of fractional‐order neural networks via quantised control with input saturation. First, a refined sector condition is put forward, which can deal with the problem of the simultaneous existence of quantiser effect and actuator constraint. With the aid of refined sector condition, theoretical analysis on the local Mittag–Leffler stabilisation of the resulting closed‐loop systems is carried out by using some inequality techniques on Mittag–Leffler function and fractional‐order Lyapunov theory. In addition, two convex optimisation schemes are respectively developed to minimise the cost of actuators and to enlarge the admissible initial region. Finally, a three‐neurons fractional‐order neural networks is applied to illustrate the effectiveness of the derived results.