Neural control for power ascent of a TRIGA reactor

A basic control problem in a nuclear research reactor consists of increasing or decreasing the neutron power from a certain level R₀ to a new desired level R₁ and maintain the reactor stable at the new power level. For security reasons, this task must be performed in such a way that, during the power ascent, the instantaneous period of the reactor must always be greater than or equal to a lower limit value. To solve this problem, avoiding the difficulties associated with the physical modeling of the nuclear process, in this paper, we propose to use an indirect adaptive control scheme in which a single layer second order differential neural network achieves the on-line identification based only on three variables: the external reactivity, the fuel temperature, and the neutron power. The mathematical model provided by this identification process is employed to accomplish the control action in two stages. During the transient stage, the controller objective is to maintain the plant on a constant period. Once the desired power is reached, the control action is switched to a regulation stage. This identifier-controller is tested by simulation. Instead of the real plant, an eighth order physical model of a TRIGA reactor considered as a black box is used. The results show a good performance of the suggested approach.