Neuro-fuzzy-based direct adaptive controller design for a class of uncertain multivariable nonlinear systems

Abstract

Develops an approach for designing a direct adaptive MIMO fuzzy logic controller to overcome the interaction among the subsystems and facilitate robust properties. The proposed adaptive fuzzy controller requires no knowledge of the controlled nonlinear system. By employing fuzzy descriptions to the input applied to one subsystem affecting the other subsystem and using the Lyapunov stability theory, the overall adaptation scheme has been proved to be able to guarantee the tracking error residual set being uniform ultimate bounded. The bounds of the fuzzy modeling error are estimated adaptively using an estimation algorithm and the global asymptotic stability of the algorithm is established via an H/sup /spl infin// tracking performance index. 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.