New time-efficient structure for observer-based adaptive fuzzy-neural controllers for nonaffine nonlinear systems

Wei-Yen Wang, I. Hsum Li, Ming Chang Chen, Shun Feng Su, Yih-Guang Leu

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This paper proposes an observer-based adaptive controller with a merged fuzzy-neural network for nonaffine nonlinear systems under the constraint that only the system output is available for measurement. Using a conventional fuzzy-neural network leads to rule explosion which leads to huge computation time. Our proposed merged-FNN does not have this problem, and can take the place of the conventional fuzzy-neural networks under some assumptions while maintaining the property of stability. Moreover, the adaptive scheme using the merged-FNN guarantees that all signals involved are bounded and the output of the closed-loop system asymptotically tracks the desired output trajectory. Finally, this paper gives examples of the proposed controller for nonaffine nonlinear systems, and is shown to provide good effectiveness.

Original languageEnglish
Pages (from-to)963-978
Number of pages16
JournalInternational Journal of Innovative Computing, Information and Control
Volume6
Issue number3
Publication statusPublished - 2010 Mar 1

Keywords

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

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computational Theory and Mathematics

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