Adaptive fuzzy-neural observer for a class of nonlinear systems

Yih Guang Leu, Tsu Tian Lee

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Based on the H control technique and the strictly positive real Lyapunov (SPR-Lyapunov) design approach, an adaptive fuzzy-neural observer tuned on-line for a class of uncertain (unknown) nonlinear systems is developed. Unlike previous results, the assumption that the uncertain system nonlinearities only are restricted to the system output is not required. Moreover, the adaptive fuzzy-neural observer provides the modeling error (and the external bounded disturbance) attenuation with H performance, obtained by a Riccati-like equation. Finally, simulation results demonstrate that the proposed observer yields satisfactory performance.

Original languageEnglish
Pages (from-to)2130-2135
Number of pages6
JournalProceedings-IEEE International Conference on Robotics and Automation
Volume3
DOIs
Publication statusPublished - 2000 Jan 1

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Control nonlinearities
Uncertain systems
Nonlinear systems

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

Adaptive fuzzy-neural observer for a class of nonlinear systems. / Leu, Yih Guang; Lee, Tsu Tian.

In: Proceedings-IEEE International Conference on Robotics and Automation, Vol. 3, 01.01.2000, p. 2130-2135.

Research output: Contribution to journalArticle

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