DESIGN OF STABILITY-GUARANTEED NEUROFUZZY LOGIC-CONTROLLER FOR NUCLEAR STEAM-GENERATORS

A neurofuzzy logic controller (NFLC), which is implemented by using a multilayer neural network with special types of fuzzifier, inference e ngine and defuzzifier, is applied to the water level control of a nucl ear steam generator (SG). This type of NFLC has the structural advanta ge that arbitrary two-input, single-output linear controllers can be a dequately mapped into a set of specific control rules of the NFLC. In order to design a stability-guaranteed NFLC, the stable sector of the given linear gain is obtained from Lyapunov's stability criteria. Then this sector is mapped into two linear rule tables that are used as th e limits of NFLC control rules. The automatic generation of NFLC rule tables is accomplished by using the back-error-propagation (BEP) algor ithm. There are two separate paths for the error back propagation in t he SG. One considers the level dynamics depending on the rank capacity and the other takes into account the reverse dynamics of the SG. The amounts of error back propagated through these paths show opposite eff ects in the BEP algorithm from each other for the swell-shrink phenome non. Through computer simulation it is found that the BEP algorithm ad equately generates NFLC rule tables according to given learning parame ters.