Robust adaptive control using fuzzy-neural controller

This paper proposes an adaptive fuzzy-neural control scheme that yields robust trajectory tracking in the presence of parametric and unstructured uncertainty. The uncertainties include bounded disturbances, dynamic-parametric changes as well as unmodeled dynamics which is dependent on state variables. The proposed method employs fuzzy-neural controlled to compensate for uncertain nonlinearity of dynamic system in the traditional direct MRAC system. To improve the robustness of adaptive fuzzy controller and diminish the tracking error boundary, a robust adaptive law is derived from the Lyapunov stability technique and switching /spl sigma/-scheme, usually applied to adaptive control. Combining fuzzy-neural theory and adaptive control technique, the proposed control provides better robust tracking control performance than a traditional MRAC.