Open AccessAdaptive model‐based event‐triggered control of networked control system with external disturbance
Author(s) -
Zhang Huaipin,
Yue Dong,
Yin Xiuxia,
Chen Ji
Publication year - 2016
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.2015.1289
Subject(s) - control theory (sociology) , adaptive control , context (archaeology) , computer science , controller (irrigation) , stability (learning theory) , lyapunov stability , event (particle physics) , networked control system , transmission (telecommunications) , control system , lyapunov function , control (management) , property (philosophy) , model predictive control , disturbance (geology) , reference model , control engineering , engineering , artificial intelligence , nonlinear system , paleontology , electrical engineering , biology , telecommunications , philosophy , physics , software engineering , epistemology , quantum mechanics , machine learning , agronomy
This study is concerned with adaptive model‐based event‐triggered control of an uncertain continuous system with external disturbance. The proposed framework incorporates two important control techniques for reducing communication burden and regulating the states of the system online in control network, that is, adaptive model‐based networked control system and event‐triggered control (ETC). An adaptive model of the plant is capable of generalising the zero‐order hold implementation in traditional ETC schemes, while also providing stability thresholds that are robust to model uncertainties. In the adaptive model‐based controller, the authors present an update law to estimate the parameters of the adaptive model at triggered instant. After revisiting the adaptive model property in the context of event‐triggered communication, an event‐triggered condition is proposed using the Lyapunov technique. The stability condition of the proposed approach does not need explicit knowledge of the plant parameters, but are given only in terms of the parameters of the adaptive model and some bounds in the model uncertainties. In addition, lower bound on transmission periods are provided. Meanwhile,L 2stability with respect to external disturbance is examined. A real‐time simulation example is presented to demonstrate the effectiveness of the theoretical results.