Open AccessCooperative adaptive finite‐time control for stochastic multi‐agent systems with input quantisation
Author(s) -
Zhang Yanhui,
Sun Jian,
He Wei,
Li Hongyi
Publication year - 2019
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/iet-cta.2018.5330
Subject(s) - control theory (sociology) , computer science , control (management) , stability (learning theory) , scheme (mathematics) , artificial neural network , tracking (education) , adaptive control , multi agent system , mathematical optimization , mathematics , artificial intelligence , psychology , mathematical analysis , pedagogy , machine learning
In this study, the finite‐time cooperative tracking issue for non‐linear multi‐agent systems with stochastic disturbances is addressed. Different from the existing asymptotical stable control, the finite‐time cooperative control can ensure the outputs of agents reach agree in finite time by raising a novel finite‐time stability criterion. The radial basis function neural networks are employed to cope with the unknown non‐linearities caused by the non‐linear non‐strict feedback form. Moreover, this study designs a new adaptive quantised control strategy to reduce communication burden. With such a control scheme, the tracking errors converge to a small area of the origin in finite time. Finally, some numerical simulation results are presented to testify the effectiveness of the approach proposed in this study.