Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems

Yih Guang Leu*, Wei Yen Wang, Tsu Tian Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

232 Citations (Scopus)


In this paper, an observer-based direct adaptive fuzzy-neural control scheme is presented for nonaffine nonlinear systems in the presence of unknown structure of nonlinearities. A direct adaptive fuzzy-neural controller and a class of generalized nonlinear systems, which are called nonaffine nonlinear systems, are instead of the indirect one and affine nonlinear systems given by Leu et al. By using implicit function theorem and Taylor series expansion, the observer-based control law and the weight update law of the fuzzy-neural controller are derived for the nonaffine nonlinear systems. Based on strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. Moreover, the overall adaptive scheme guarantees that all signals involved are bounded and the output of the closed-loop system will asymptotically track the desired output trajectory. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.

Original languageEnglish
Pages (from-to)853-861
Number of pages9
JournalIEEE Transactions on Neural Networks
Issue number4
Publication statusPublished - 2005 Jul
Externally publishedYes


  • Direct adaptive control
  • Fuzzy-neural control
  • Nonaffine nonlinear systems
  • Output feedback control

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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