STABILIZED NONLINEAR DISCRETE-TIME ADAPTIVE CONTROLLER BASED ON FUZZY RULES EMULATED NETWORKS AND TIME VARYING LEARNING RATE

Abstract

A direct adaptive controller based on fuzzy rules emulated network (FREN) for a class of unknown nonlinear discrete-time systems is addressed in this article. According to the fuzzy inference system inside FREN, the human knowledge about the unknown systems is transferred to be if–then rules for setting the network structure. All adjustable parameters are tuned by the on-line learning mechanism with time varying learning rate. This variation of learning rate is introduced by main theorem to improve the system performance and stabilization. Furthermore, the convergence of adjustable parameters is guaranteed through the on-line learning and membership functions properties. The theoretical validation is delineated by the experimental setup with the commercial omni-directional mobile robot system.