Open AccessState estimation via Markov switching‐channel network and application to suspension systems
Author(s) -
Yin Xunyuan,
Zhang Lixian,
Ning Zepeng,
Tian Dapeng,
Alsaedi Ahmed,
Ahmad Bashir
Publication year - 2017
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.2016.1108
Subject(s) - control theory (sociology) , estimator , markov process , channel (broadcasting) , markov chain , computer science , network packet , transmission (telecommunications) , state (computer science) , telecommunications network , markov model , networked control system , attenuation , mathematics , algorithm , control (management) , telecommunications , computer network , artificial intelligence , statistics , machine learning , physics , optics
The problem of H ∞ estimation for a class of networked non‐linear systems is investigated. A practical scenario with multiple switching communication channels coexisting in the network is considered. System signals are exchanged over the multiple communication channels and each channel is subject to the two main transmission imperfections, network‐induced time‐varying delays and packet dropouts. The channel switching is assumed to be governed by a continuous‐time Markov process, and a Markov jump non‐linear system model is exploited to represent the overall networked system. Linear estimators are designed such that the underlying estimation error system is stochastically stable and the disturbance rejection attenuation satisfies an H ∞performance bound. As a case study, a state estimation problem for an intelligent active suspension system is addressed to verify the theoretical findings.