Open AccessIntelligent digital redesign for T–S fuzzy systems: sampled‐data filter approach
Author(s) -
Kim Ho Jun,
Park Jin Bae,
Joo Young Hoon
Publication year - 2018
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.2017.0964
Subject(s) - discretization , control theory (sociology) , fuzzy logic , sampled data systems , filter (signal processing) , fuzzy control system , stability (learning theory) , process (computing) , computer science , mathematics , control system , artificial intelligence , engineering , control (management) , computer vision , electrical engineering , mathematical analysis , machine learning , operating system
This study proposes an intelligent digital redesign (IDR) technique for sampled‐data fuzzy filters of non‐linear systems. The technique constructs a closed‐loop system with predesigned continuous‐time and sampled‐data filters based on the Takagi–Sugeno (T–S) fuzzy model. The closed‐loop systems ensure asymptotic stability and state‐matching condition in the IDR problem. Unlike previous techniques, the proposed method solves the IDR problem without a discretization process which degrades the IDR performance. Sufficient conditions for solving the IDR problem are proposed and derived in terms of linear matrix inequalities. In addition, the performance recovery of the sampled‐data fuzzy filter is shown. Finally, the feasibility of the proposed technique is demonstrated in two simulation examples.