Intelligent Digital Redesign for Sampled-data Fuzzy Control Systems Based on State-matching Error Cost Function Approach

Geun Bum Koo, Jin Bae Park, Young Hoon Joo

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


This paper presents a novel intelligent digital redesign (IDR) technique for a sampled-data fuzzy controller of fuzzy systems which can be modeled in Takagi-Sugeno (T-S) fuzzy systems. The IDR technique, which is one of sampled-data fuzzy controller design techniques, is to effectively convert a well-designed analog fuzzy controller into a sampled-data fuzzy controller in the state-matching sense. In this paper, unlike previous IDR techniques, the state-matching error is minimized by defining the state-matching error cost function. Also, to improve the state-matching condition, the exact discretized model is used for the proposed IDR technique. The sufficient condition of the proposed IDR technique is developed and derived in terms of linear matrix inequalities (LMIs). Finally, some numerical examples are provided to verify the effectiveness of the proposed techniques.

Original languageEnglish
Pages (from-to)350-359
Number of pages10
JournalInternational Journal of Control, Automation and Systems
Issue number1
Publication statusPublished - 2018 Feb 1

Bibliographical note

Funding Information:
Manuscript received March 25, 2017; revised May 22, 2017; accepted June 1, 2017. Recommended by Associate Editor Sun Jin Yoo under the direction of Editor Duk-Sun Shim. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP; Ministry of Science, ICT & Future Planning) (No. NRF-2017R1C1B1005422).

Publisher Copyright:
© 2018, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications


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