An on-line robust and adaptive T-S fuzzy-neural controller for more general unknown systems

Wei Yen Wang*, Yi Hsing Chien, I. Hsum Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


This paper proposes a novel method of on-line modeling via the Takagi-Sugeno (T-S) fuzzy-neural model and robust adaptive control for a class of general unknown nonaffine nonlinear systems with external disturbances. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known on the more complicated and general nonlinear systems. Compared with the previous approaches, the contribution of this paper is an investigation of the more general unknown nonaffine nonlinear systems using on-line adaptive T-S fuzzy-neural controllers. Instead of modeling these unknown systems directly, the T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS), with modeling errors and external disturbances. We prove that the closed-loop system controlled by the proposed controller is robust stable and the effect of all the unmodeled dynamics, modeling errors and external disturbances on the tracking error is attenuated under mild assumptions. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

Original languageEnglish
Pages (from-to)33-43
Number of pages11
JournalInternational Journal of Fuzzy Systems
Issue number1
Publication statusPublished - 2008 Mar


  • Fuzzy-neural model
  • General unknown systems
  • On-line modeling

ASJC Scopus subject areas

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


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