Generalized minimum variance control of steam-boiler temperature using neuro-fuzzy approach

Abstract

A neuro-fuzzy network predictive approach is introduced to design a control system for nonlinear industrial process. While the nonlinear process is modeled by neuro-fuzzy technique containing local CARMA model, traditional generalized minimum variance predictive control method can be extended to a nonlinear case in a neuro-fuzzy fashion. Boiler steam temperature process is chosen as the realistic system for the demonstration of the techniques discussed and the neuro-fuzzy controller was found to provide a satisfactory performance over the complex system.