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Title: H-inf.-observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems
Authors: 國立臺灣師範大學電機工程學系
Y.-G. Leu
W.-Y. Wang
T.-T. Lee
Issue Date: 15-Oct-1999
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 that 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
Other Identifiers: ntnulib_tp_E0604_02_088
Appears in Collections:教師著作

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