By Topic

Delay-dependent robust H filtering design for uncertain discrete-time T-S fuzzy systems with interval time-varying delay

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jianbin Qiu ; Department of Manufacturing Engineering and Engineering Management, University of Science and Technology of China & City University of Hong Kong Joint Advanced Research Center, Suzhou, 215123, China ; Gang Feng ; Jie Yang

This paper investigates the problem of delay-dependent robust Hinfin filtering design for a class of uncertain discrete-time state-delayed T-S fuzzy systems. The state delay is assumed to be time-varying and of an interval-like type, which means that both the lower and upper bounds of the time-varying delay are available. The parameter uncertainties are assumed to have a structured linear fractional form. Based on a novel delay and fuzzy-basis-dependent Lyapunov-Krasovskii functional combined with Finslerpsilas Lemma, a new sufficient condition for robust Hinfin performance analysis is firstly derived and then the filter synthesis is developed. It is shown that by using a new linearization technique incorporating a bounding inequality, a unified framework can be developed such that both the full-order and reduced-order filters can be obtained by solving a set of linear matrix inequalities, which are numerically efficient with commercially available software. Finally, a numerical example is provided to illustrate the advantages and less conservatism of the proposed approach.

Published in:

Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on

Date of Conference:

1-6 June 2008