A neural network controller for load following operation of nuclear reactors

Nuclear reactors are in nature nonlinear and their parameters vary with time as a function of power level, fuel burn-up, and control rod worth. Therefore, these characteristics must be considered if large power variations occur in power plant working regimes (for example in load following conditions). In this paper a neural network controller (NNC) is presented. A robust optimal self-tuning regulator (ROSTR) response is used as a reference trajectory to determine the feedback, feedforward and observer gains of the NNC. The NNC displays good stability and performance for a wide range of operation as well as considerable reduction in computation time with regard to the ROSTR and fuzzy logic controller (FAROC).