A composite controller for unknown nonlinear dynamical systems using robust adaptive fuzzy-neural control schemes

Abstract

A robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbance and modeling errors. A composite update law, which has a generalized form combining the projection algorithm modification and the switching-/spl sigma/ adaptive law, is used to tune the adjustable parameters for preventing parameter drift and confining states of the system into the specified regions. Moreover, a fuzzy variable structure control method is incorporated into the control law so that the derived controller is robust with respect to unmodeled dynamics, disturbances and modeling errors. Compared with previous control schemes for nonlinear systems, the magnitude of the control input by using the proposed approach is much smaller, which is a significant advantage in designing controllers for practical applications. To demonstrate the effectiveness and applicability of the proposed method, several examples are illustrated in the paper.