Open AccessH ∞ filtering for non‐linear systems with stochastic sensor saturations and Markov time delays: the asymptotic stability in probability
Author(s) -
Liu Yang,
Wang Zidong,
He Xiao,
Zhou Donghua
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.2015.1062
Subject(s) - control theory (sociology) , exponential stability , stability (learning theory) , markov chain , mathematics , markov process , computer science , linear system , nonlinear system , artificial intelligence , statistics , mathematical analysis , control (management) , physics , machine learning , quantum mechanics
This study is concerned with the filtering problem for a class of non‐linear systems with stochastic sensor saturations and Markovian measurement transmission delays, where the asymptotic stability in probability is considered. The sensors are subject to random saturations characterised by a Bernoulli distributed sequence. The transmission time delays are governed by a discrete‐time Markov chain with finite states. In the presence of the non‐linearities, stochastic sensor saturations and Markovian time delays, sufficient conditions are established to guarantee that the filtering process is asymptotically stable in probability without disturbances and also satisfies the H ∞criterion with respect to non‐zero exogenous disturbances under the zero‐initial condition. Moreover, it is illustrated that the results can be specialised to linear filters. Two simulation examples are presented to show the effectiveness of the proposed algorithms.