Power level control of the TRIGA Mark-II research reactor using the multifeedback layer neural network and the particle swarm optimization

Coban R.

ANNALS OF NUCLEAR ENERGY, cilt.69, ss.260-266, 2014 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 69
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1016/j.anucene.2014.02.019
  • Sayfa Sayıları: ss.260-266


In this paper, an artificial neural network controller is presented using the Multifeedback-Layer Neural Network (MFLNN), which is a recently proposed recurrent neural network, for neutronic power level control of a nuclear research reactor. Off-line learning of the MFLNN is accomplished by the Particle Swarm Optimization (PSO) algorithm. The MFLNN-PSO controller design is based on a nonlinear model of the TRIGA Mark-II research reactor. The learning and the test processes are implemented by means of a computer program at different power levels. The simulation results obtained reveal that the MFLNN-PSO controller has a remarkable performance on the neutronic power level control of the reactor for tracking the step reference power trajectories. (C) 2014 Elsevier Ltd. All rights reserved.