Robust adaptive fuzzy-neural controllers for uncertain nonlinear systems

A robust adaptive fuzzy-neural controller for a class of unknown nonlinear dynamic systems with external disturbances is proposed in this paper. The f uzzy-neural, approximator is established to approximate an unknown nonlinea r dynamic system in a linearized way, The fuzzy B-spline membership functio n (BMF) which possesses fixed number of control points is developed for on- line tuning. The concept of tuning the adjustable vectors, which include me mbership functions and weighting factors, is described to derive the update laws of the robust adaptive fuzzy-neural controller. Furthermore, the effe ct of all the unmodeled dynamics, BMF modeling errors and external disturba nces on the tracking error is attenuated by the error compensator which is also constructed by the fuzzy-neural inference. In this paper, we can prove that the closed-loop system which is controlled by the robust adaptive fuz zy-neural controller is stable and the tracking error will converge to zero under mild assumptions. Several examples are simulated in order to confirm the effectiveness and applicability of the proposed methods in this paper.