Design of a genetic fuzzy controller for the nuclear steam generator water level control

The nuclear steam generator is a nonminimum-phase system, which is caused by the swell and shrink effects. Since its inverse system has unstable dynamics, it is difficult to train the fuzzy controller via the conventional backpropagation of the system output errors. In this paper, a genetic algorithm is applied for the simultaneous design of membership functions and rule sets for a fuzzy control method for a steam generator water level. The genetic fuzzy controller for the steam generator is a fuzzy logic controller which is tuned offline by the genetic algorithm using the water level, feedwater flowrate, and steam flowrate signals of the steam generator. The symmetric Gaussian membership functions based on the flowrate and water level errors are applied. The proposed genetic fuzzy controller has a generalized and simplified rule base. The same genetic algorithm that is used to optimize the genetic fuzzy controller tunes a conventional proportional-integral (P-I) controller, and the performance of two controllers is compared. The genetic fuzzy controller shows good response that its swell and shrink phenomena are smaller and its response is faster than those of a well-tuned P-I controller are