On-line adaptive T-S fuzzy-neural control for a class of general multi-link robot manipulators

Wei Yen Wang, Yi Hsing Chien, Yih Guang Leu, Tsu Tian Lee

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

This paper proposes a novel method of on-line modeling and control through the Takagi-Sugeno (T-S) fuzzy-neural model for a class of general n-link robot manipulators. Compared with previous methods, this paper has two unique aspects: first, a more general n-link robot system using on-line adaptive T-S fuzzy-neural controller is investigated, and second, the complete proof of the controller is given. The general robot systems are linearized via the mean value theorem, and then the T-S fuzzy-neural model can approximate the linearized system. Also, we propose an on-line identification algorithm and put significant emphasis on robust tracking controller design using an adaptive scheme for the robot systems. Finally, an example including two cases is provided to demonstrate the feasibility and robustness of the proposed method.

Original languageEnglish
Pages (from-to)240-249
Number of pages10
JournalInternational Journal of Fuzzy Systems
Volume10
Issue number4
Publication statusPublished - 2008 Dec
Externally publishedYes

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Keywords

  • Fuzzy-neural model
  • Multi-link robot manipulators
  • Robust adaptive control

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Computational Theory and Mathematics
  • Artificial Intelligence

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