Sampled-data control of fuzzy systems based on the intelligent digital redesign method via an improved fuzzy Lyapunov functional approach

Han Sol Kim, Jin Bae Park, Young Hoon Joo

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


This study presents a linear matrix inequality (LMI) approach to the sampled-data control of Takagi-Sugeno fuzzy systems, based on the intelligent digital redesign (IDR) technique. The objective of the IDR is to design a digital control system whose trajectory closely matches that of a given well-constructed analogue control system by minimising the state-matching error. In this study, state-matching performance is enhanced by using a continuous-Time state-matching criterion, which guarantees that the state-matching error is minimised through the entire time interval. Unlike previous studies, mismatched information of membership functions for both analogue and digital control systems is directly manipulated. Moreover, the authors introduce an improved fuzzy Lyapunov functional that consists of both membership functions for analogue and digital control systems, which relaxes the conservativeness of LMI conditions. Finally, two examples demonstrating the effectiveness of the authors' method are provided.

Original languageEnglish
Pages (from-to)163-173
Number of pages11
JournalIET Control Theory and Applications
Issue number1
Publication statusPublished - 2018 Jan 2

Bibliographical note

Funding Information:
This work was supported by Barun ICT Research Center at Yonsei University, and by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1A2A2A05001610), and by the Korea Institute of Energy Technology Evaluation and Planning(KETEP) and the Ministry of Trade, Industry & Energy(MOTIE) of the Republic of Korea (no. 20174030201670).

Publisher Copyright:
© The Institution of Engineering and Technology 2017.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Control and Optimization
  • Electrical and Electronic Engineering


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