H-observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems

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3 Citations (Scopus)

Abstract

This paper presents a method for designing an H-observer-based adaptive fuzzy-neural output feedback control law with on-line tuning of fuzzy-neural weighting factors for a class of uncertain nonlinear systems based on the H control technique and the strictly positive real Lyapunov (SPR-Lyapunov) design approach. The H-observer-based output feedback control law guarantees all signals involved are bounded, and provides the modeling error (and the external bounded disturbance) attenuation with H performance, obtained by a Riccati-like equation. Besides, the H-observer-based output feedback control law doesn't require the assumptions of the total system states available for measurement and the uncertain system nonlinearities only restricted to the system output. Finally, an example is simulated in order to confirm the effectiveness and applicability of the proposed methods.

Original languageEnglish
Pages (from-to)I-449 - I-454
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume1
Publication statusPublished - 1999 Dec 1
Event1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics' - Tokyo, Jpn
Duration: 1999 Oct 121999 Oct 15

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Feedback control
Nonlinear systems
Control nonlinearities
Uncertain systems
Tuning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

Cite this

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title = "H∞-observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems",
abstract = "This paper presents a method for designing an H∞-observer-based adaptive fuzzy-neural output feedback control law with on-line tuning of fuzzy-neural weighting factors for a class of uncertain nonlinear systems based on the H∞ control technique and the strictly positive real Lyapunov (SPR-Lyapunov) design approach. The H∞-observer-based output feedback control law guarantees all signals involved are bounded, and provides the modeling error (and the external bounded disturbance) attenuation with H∞ performance, obtained by a Riccati-like equation. Besides, the H∞-observer-based output feedback control law doesn't require the assumptions of the total system states available for measurement and the uncertain system nonlinearities only restricted to the system output. Finally, an example is simulated in order to confirm the effectiveness and applicability of the proposed methods.",
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AU - Wang, Wei Yen

AU - Lee, Tsu Tian

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N2 - This paper presents a method for designing an H∞-observer-based adaptive fuzzy-neural output feedback control law with on-line tuning of fuzzy-neural weighting factors for a class of uncertain nonlinear systems based on the H∞ control technique and the strictly positive real Lyapunov (SPR-Lyapunov) design approach. The H∞-observer-based output feedback control law guarantees all signals involved are bounded, and provides the modeling error (and the external bounded disturbance) attenuation with H∞ performance, obtained by a Riccati-like equation. Besides, the H∞-observer-based output feedback control law doesn't require the assumptions of the total system states available for measurement and the uncertain system nonlinearities only restricted to the system output. Finally, an example is simulated in order to confirm the effectiveness and applicability of the proposed methods.

AB - This paper presents a method for designing an H∞-observer-based adaptive fuzzy-neural output feedback control law with on-line tuning of fuzzy-neural weighting factors for a class of uncertain nonlinear systems based on the H∞ control technique and the strictly positive real Lyapunov (SPR-Lyapunov) design approach. The H∞-observer-based output feedback control law guarantees all signals involved are bounded, and provides the modeling error (and the external bounded disturbance) attenuation with H∞ performance, obtained by a Riccati-like equation. Besides, the H∞-observer-based output feedback control law doesn't require the assumptions of the total system states available for measurement and the uncertain system nonlinearities only restricted to the system output. Finally, an example is simulated in order to confirm the effectiveness and applicability of the proposed methods.

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