Open AccessNetworked H ∞ filtering for Markovian jump T–S fuzzy systems with imperfect premise matching
Author(s) -
Ma Shaodong,
Peng Chen,
Song Yang,
Du Dajun
Publication year - 2017
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2016.0371
Subject(s) - fuzzy logic , imperfect , filter (signal processing) , control theory (sociology) , computer science , fuzzy control system , matching (statistics) , premise , mathematics , filter design , stability (learning theory) , fuzzy number , markov process , jump , fuzzy set , artificial intelligence , machine learning , computer vision , statistics , philosophy , linguistics , physics , control (management) , quantum mechanics
This study focuses on networked H ∞ fuzzy filtering for Markovian jump Takagi–Sugeno (T–S) fuzzy systems. Since the traditional PDC method is ineffective under network environments, a flexible filter design method is provided with imperfect premise matching. First, a unified T–S fuzzy error model is provided by considering the mismatched grades of membership. Second, by use of the constructed model and the Markovian switched Lyapunov–Krasovskii functional, a stability and a stabilisation criteria of the fuzzy filtering error system are derived, in which it is no required that the fuzzy filter shares the same membership functions with the fuzzy systems. Therefore, the designed filter is available under network environments since the mismatched premises induced by networks are well considered. Finally, the effectiveness of the proposed new design method is illustrated by two examples.