Premium
H ∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts
Author(s) -
Shen Bo,
Wang Zidong,
Shu Huisheng,
Wei Guoliang
Publication year - 2010
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.1662
Subject(s) - bernoulli's principle , filtering problem , filter (signal processing) , control theory (sociology) , network packet , bernoulli distribution , set (abstract data type) , computer science , discrete time and continuous time , class (philosophy) , state (computer science) , mathematics , mathematical optimization , filter design , algorithm , random variable , statistics , engineering , artificial intelligence , programming language , aerospace engineering , computer network , control (management) , computer vision
This paper is concerned with the robust H ∞ finite‐horizon filtering problem for discrete time‐varying stochastic systems with multiple randomly occurred sector‐nonlinearities (MROSNs) and successive packet dropouts. MROSNs are proposed to model a class of sector‐like nonlinearities that occur according to the multiple Bernoulli distributed white sequences with a known conditional probability. Different from traditional approaches, in this paper, a time‐varying filter is designed directly for the addressed system without resorting to the augmentation of system states and measurement, which helps reduce the filter order. A new H ∞ filtering technique is developed by means of a set of recursive linear matrix inequalities that depend on not only the current available state estimate but also the previous measurement, therefore ensuring a better accuracy. Finally, two illustrative examples are used to demonstrate the effectiveness and applicability of the proposed filter design scheme. Copyright © 2010 John Wiley & Sons, Ltd.