A design methodology for an intelligent controller using fuzzy logic and artificial neural networks

Abstract

The optimal control of nonlinear time-varying systems, particularly when the mathematical model of the system is unavailable or inexact, is an interesting and difficult control problem. This paper outlines a methodology for the design of an intelligent controller to perform optimal control of a nonlinear system adaptively, using emerging technologies of fuzzy logic (FL) and artificial neural networks (ANN). FL is utilized to incorporate the available knowledge into the control system, and ANN technology is applied to adaptively provide an optimal control strategy based on some performance criteria. The technique is tested on a system that consists of a DC motor (a linear time-invariant (LTI) system) and a thermal system (a time-varying nonlinear system). Performance criteria such as tracking accuracy, cost, robustness, are considered, and the results are presented in this paper. >