TY - JOUR
T1 - Event-based H∞ fault estimation for networked time-varying systems with randomly occurring nonlinearities and (x, v)-dependent noises
AU - Chao, Daikun
AU - Sheng, Li
AU - Liu, Yang
AU - Liu, Yurong
AU - Alsaadi, Fuad E.
N1 - Funding Information:
This work was supported by National Natural Science Foundation of China under Grants 61573377 , 61773400 , 61703244 , Project for the Applied Basic Research of Qingdao under Grants 16-5-1-3-jch, 16-8-3-1-zhc and Fundamental Research Fund for the Central Universities of China under Grants 15CX08014A, 17CX02059.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/4/12
Y1 - 2018/4/12
N2 - In this paper, the problem of finite-horizon H∞ fault estimation is investigated for a class of networked time-varying stochastic systems with randomly occurring nonlinearities and state- and disturbance-dependent noises (also called (x, v)-dependent noises). An event-triggered scheme is proposed to reduce data transmission burden where the current measurement is transmitted only when the certain condition is satisfied. The aim of the addressed problem is to design a fault estimator, in the presence of randomly occurring nonlinearities and (x, v)-dependent noises, such that faults can be estimated through measurement outputs. By employing the stochastic analysis method, the sufficient conditions are derived to guarantee that the error dynamics of estimations satisfies a prescribed H∞ performance constraint. Moreover, the parameters of fault estimator can be calculated via the recursive linear matrix inequality (RLMI) approach. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed method.
AB - In this paper, the problem of finite-horizon H∞ fault estimation is investigated for a class of networked time-varying stochastic systems with randomly occurring nonlinearities and state- and disturbance-dependent noises (also called (x, v)-dependent noises). An event-triggered scheme is proposed to reduce data transmission burden where the current measurement is transmitted only when the certain condition is satisfied. The aim of the addressed problem is to design a fault estimator, in the presence of randomly occurring nonlinearities and (x, v)-dependent noises, such that faults can be estimated through measurement outputs. By employing the stochastic analysis method, the sufficient conditions are derived to guarantee that the error dynamics of estimations satisfies a prescribed H∞ performance constraint. Moreover, the parameters of fault estimator can be calculated via the recursive linear matrix inequality (RLMI) approach. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed method.
KW - (x, v)-dependent noises
KW - Event-triggered mechanism
KW - H fault estimation
KW - Networked time-varying systems
KW - Recursive linear matrix inequalities
UR - http://www.scopus.com/inward/record.url?scp=85044859095&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2018.01.042
DO - 10.1016/j.neucom.2018.01.042
M3 - Article
AN - SCOPUS:85044859095
VL - 285
SP - 220
EP - 229
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -