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, vol.69, pp.260-266, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 69
  • Publication Date: 2014
  • Doi Number: 10.1016/j.anucene.2014.02.019
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.260-266
  • Çukurova University Affiliated: Yes


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.