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

Yih-Guang Leu; Tsu Tian Lee

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

Yih-Guang Leu, Tsu Tian Lee

Department of Electrical Engineering

Research output: Contribution to conference › Paper

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 language	English
Pages	178-183
Number of pages	6
Publication status	Published - 2000 Dec 1
Event	26th Annual Conference of the IEEE Electronics Society IECON 2000 - Nagoya, Japan Duration: 2000 Oct 22 → 2000 Oct 28

Other

Other	26th Annual Conference of the IEEE Electronics Society IECON 2000
Country	Japan
City	Nagoya
Period	00/10/22 → 00/10/28

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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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.

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