Power level control of the TRIGA Mark-II research reactor using the multifeedback layer neural network and the particle swarm optimization
ANNALS OF NUCLEAR ENERGY, cilt.69, ss.260-266, 2014 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 69
- Basım Tarihi: 2014
- Doi Numarası: 10.1016/j.anucene.2014.02.019
- Dergi Adı: ANNALS OF NUCLEAR ENERGY
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.260-266
- Çukurova Üniversitesi Adresli: Evet
Özet
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.