Observer-based adaptive fuzzy-neural control for a class of MIMO nonlinear systems

Yih-Guang Leu, Tsu Tian Lee

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

In this paper, an observer-based adaptive fuzzy-neural controller for a class of multi-input multi-output (MIMO) nonlinear systems is developed based on observers used to estimate the time derivatives of the system outputs. The proposed method has the merit that no differentiation of the system output is required in order to avoid the noise amplification associated with numerical differentiation. The stability of the observer-based adaptive fuzzy-neural controller is proven by using strictly-positive-real (SPR) Lyapunov theory. The overall adaptive scheme guarantees that all signals involved are bounded and the outputs of the closed-loop system asymptotically track the desired output trajectories. Finally, simulation results are provided to demonstrate the robustness and applicability of the proposed method.

Original languageEnglish
Pages178-183
Number of pages6
Publication statusPublished - 2000 Dec 1
Event26th Annual Conference of the IEEE Electronics Society IECON 2000 - Nagoya, Japan
Duration: 2000 Oct 222000 Oct 28

Other

Other26th Annual Conference of the IEEE Electronics Society IECON 2000
CountryJapan
CityNagoya
Period00/10/2200/10/28

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Leu, Y-G., & Lee, T. T. (2000). Observer-based adaptive fuzzy-neural control for a class of MIMO nonlinear systems. 178-183. Paper presented at 26th Annual Conference of the IEEE Electronics Society IECON 2000, Nagoya, Japan.