Open AccessRobust estimation for discrete time‐delay Markov jump systems with sensor non‐linearity and missing measurements
Author(s) -
You Jia,
Yin Shen,
Yu Zhandong
Publication year - 2014
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.2013.0577
Subject(s) - control theory (sociology) , bernoulli's principle , markov process , bernoulli distribution , discrete time and continuous time , mathematics , filter (signal processing) , computer science , random variable , engineering , statistics , control (management) , artificial intelligence , computer vision , aerospace engineering
This study addresses the ℋ ∞ filtering design issue for a class of time‐delay Markov jump system with non‐linear characteristics. A stochastic system with sensor saturation and intermittent measurements is considered in the authors study. Random noise depending on state and external‐disturbance are also taken into account. A decomposition approach and a bernoulli process are utilised to model the characteristic of sensor saturation and missing measurements, respectively. By transforming the filtering error system into an input–output form, sufficient conditions for the stochastic stability of the system with a prescribed ℋ ∞ level are presented with the help of Scaled Small Gain theorem developed for stochastic systems. Based on the proposed conditions, the rubost filter design approach is proposed. A numerical example is finally provided to demonstrate effectiveness of the proposed approahc.