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

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.