A new adaptive fuzzy controller with saturation employing influential rule search scheme (IRSS)

Abstract

The present paper describes the development of a new adaptive fuzzy logic controller (AFLC), which can directly employ an adaptive fuzzy/neuro/neuro-fuzzy function approximator to develop a desired controller, based on an ideal controller input-output data set. The system also proposes the development of the AFLC based on a new adaptive fuzzy system, called influential rule search scheme (IRSS), as a pure function approximation tool. The proposed AFLC employs two additional sub-modules: i) a new static fuzzy resetting action controller (FRAC), which fuzzily changes the resetting action of the controller at each sampling instant, to enhance system performance, and ii) a new fuzzy inverse process estimator, based on IRSS as a function approximator itself. Simulation studies are shown to demonstrate the effectiveness of the proposed controller scheme over conventional PID controllers and other existing fuzzy and neural network based controllers.