Robust neurofuzzy controller design of a class of uncertain multivariable nonlinear systems

Abstract

The goal of this paper is to develop a stable adaptive MIMO fuzzy logic controller to overcome the interaction among the subsystems by a decoupling neural network and to facilitate robust properties by fine-tuning the consequent membership functions. The proposed adaptive fizzy controller does not require any knowledge of a nonlinear system. By using H/sup /spl infin// tracking performance index, the overall system with the proposed controller has been proved to be uniform ultimate bounded. Simulation results of a two-dimensional inverted pendulum confirm that the effect of both the fuzzy approximation error and external disturbance on the tracking error can be attenuated efficiently by the proposed method.