ROBUST ADAPTIVE FUZZY CONTROL FOR NONLINEAR SYSTEMS WITH NON-MINIMUM PHASE

Abstract

ABSTRACT This paper presents a robust adaptive fuzzy control scheme for the stabilization of nonlinear systems with non-minimum phase. Generally, while straightforward application of the feedback linearization technique to a non-minimum phase system results in a linear input- output response, the associated internal dynamics are unstable. The proposed scheme employs a fuzzy system to adapatively compensate for the plant nonlinearities and forces the plant output to asymptotically track the output of a prespecified reference model, which can stabilize the internal dynamics. When matching with the model occurs, the plant output will converge to the origin and the internal dynamics can be simultaneously stabilized. The stability and the tracking error asymptotically convergent to a predetermined boundary are established via Lyapunov's stability theorem. Experiments on an inverted wedge system are given to show the effectiveness of the proposed scheme.