Fuzzy filter for nonlinear sampled-data systems: Intelligent digital redesign approach

Ho Jun Kim; Jin Bae Park; Young Hoon Joo

doi:10.1007/s12555-015-0437-9

Fuzzy filter for nonlinear sampled-data systems: Intelligent digital redesign approach

Ho Jun Kim, Jin Bae Park, Young Hoon Joo

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

This paper presents a fuzzy filter design method for nonlinear sampled-data systems using an intelligent digital redesign (IDR) technique. Based on a Takagi–Sugeno (T–S) fuzzy model, discretized closed-loop systems with pre-designed analog fuzzy and digital fuzzy filters are presented. An IDR problem is given to guarantee both state-matching condition and asymptotic stability. Sufficient conditions for solving the IDR problem are proposed and are derived in terms of linear matrix inequalities (LMIs). Finally, a simulation example is given to show the effectiveness of the proposed method.

Original language	English
Pages (from-to)	603-610
Number of pages	8
Journal	International Journal of Control, Automation and Systems
Volume	15
Issue number	2
DOIs	https://doi.org/10.1007/s12555-015-0437-9
Publication status	Published - 2017 Apr 1

Bibliographical note

Publisher Copyright:
© 2017, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Computer Science Applications

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10.1007/s12555-015-0437-9

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abstract = "This paper presents a fuzzy filter design method for nonlinear sampled-data systems using an intelligent digital redesign (IDR) technique. Based on a Takagi–Sugeno (T–S) fuzzy model, discretized closed-loop systems with pre-designed analog fuzzy and digital fuzzy filters are presented. An IDR problem is given to guarantee both state-matching condition and asymptotic stability. Sufficient conditions for solving the IDR problem are proposed and are derived in terms of linear matrix inequalities (LMIs). Finally, a simulation example is given to show the effectiveness of the proposed method.",

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AU - Park, Jin Bae

AU - Joo, Young Hoon

PY - 2017/4/1

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N2 - This paper presents a fuzzy filter design method for nonlinear sampled-data systems using an intelligent digital redesign (IDR) technique. Based on a Takagi–Sugeno (T–S) fuzzy model, discretized closed-loop systems with pre-designed analog fuzzy and digital fuzzy filters are presented. An IDR problem is given to guarantee both state-matching condition and asymptotic stability. Sufficient conditions for solving the IDR problem are proposed and are derived in terms of linear matrix inequalities (LMIs). Finally, a simulation example is given to show the effectiveness of the proposed method.

AB - This paper presents a fuzzy filter design method for nonlinear sampled-data systems using an intelligent digital redesign (IDR) technique. Based on a Takagi–Sugeno (T–S) fuzzy model, discretized closed-loop systems with pre-designed analog fuzzy and digital fuzzy filters are presented. An IDR problem is given to guarantee both state-matching condition and asymptotic stability. Sufficient conditions for solving the IDR problem are proposed and are derived in terms of linear matrix inequalities (LMIs). Finally, a simulation example is given to show the effectiveness of the proposed method.

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Fuzzy filter for nonlinear sampled-data systems: Intelligent digital redesign approach

Abstract

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