Adaptive neurofuzzy controller to regulate UTSG water level in nuclear power plants

Abstract

A data-driven adaptive neurofuzzy controller is presented for the water-level control of U-tube steam generators in nuclear power plants. This neurofuzzy controller is capable of learning the control action principles from the data obtained using other methods of automatic or manual control. There are four inputs in the neurofuzzy system, yet only eighty fuzzy rules involved. Therefore, the fuzzy system is versatile and moderately compact. The versatility is due to the higher input space dimension that helps to learn more control principles. The compactness is due to the number of rules being not too many. A 10-h evaluation trial of the trained fuzzy controller demonstrated its capability in regulating the water level under random disturbances and reference level changes.