Fuzzy indirect adaptive control for a class of decentralized nonlinear systems

Abstract

A stable fuzzy indirect control scheme is presented for a class of interconnected nonlinear systems for which an explicit linear parametrization of the uncertainty is either unknown or impossible. In the control algorithm, fuzzy logic systems, are employed to approximate the unknown dynamics in each subsystems, the feedback and adaptation mechanisms for each subsystems depend only upon local measurements to provide asymptotic tracking of a reference trajectory. In addition, a fuzzy sliding mode controller is developed to compensate for the fuzzy approximating errors or neural network approximating, and to attenuate the interactions between subsystems. Global asymptotic stability is established in the Lyapunov sense, with the tracking errors converging to a neighbourhood of zero