Computational intelligence-based trajectory scheduling for control of nuclear research reactors

Progress in Nuclear Energy

Volumn 52, Issue 4, 2010, Pages 415-424

  Coban, Ramazan ^a  

^a CUKUROVA UNIVERSITY   (Turkey)

Author keywords

Computational intelligence; Genetic algorithms; Neural networks; Reactor control; Trajectory scheduling

Indexed keywords

ARTIFICIAL NEURAL NETWORK; COMPUTATIONAL INTELLIGENCE; ERROR TOLERANCE; NUCLEAR RESEARCH REACTOR; POWER LEVELS; REACTOR CONTROL;

CONTROLLERS; GENETIC ALGORITHMS; GENETIC ENGINEERING; NUCLEAR ENGINEERING; RESEARCH REACTORS; TRAJECTORIES;

NEURAL NETWORKS;

EID: 76449108826     PISSN: 01491970     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pnucene.2009.09.004     Document Type: Article

References (22)

1. Baba A.F. Fuzzy logic controller. Nucl. Eng. Int. 49 (2004) 36-38
2. Bernard, J.A., 1991. Period-generated control for trajectory tracking. In: Proceedings of the American Control Conference, Boston, MA, vol. 3, June 1991, pp. 3036-3037.
3. Can, B., 1992. The optimal control of ITU TRIGA Mark-II Reactor. In: The Twelfth European TRIGA User's Conference, NRI Bucuresti-Pitesti, Romania, September 22-25.
4. Can B., Baba F., and Erdal H. Optimal controller design for Triga Mark-II Reactor by using piece-vise trajectory. Marmara Univ. J. Sci. Technol. 15 (1999) 187-196
5. Can, B., Yavuz, H., Akbay, E., 1990. The investigation of nonlinear dynamics behaviour of ITU Triga Mark-II reactor. In: Eleventh European TRIGA Users Conference Papers and Abstracts, Heidelberg, Germany, September 11-13, pp. 39-49.
6. Coban, R., 2006. Fuzzy control of ITU Triga Mark-II reactor using artificial neural networks and genetic algorithms. Ph.D. Dissertation, Department of Electronics Engineering, Gebze Institute of Technology, Turkey (in Turkish).
7. Coban R., and Can B. Identification and control of ITU Triga Mark-II nuclear research reactor using neural networks and fuzzy logic. Lect. Notes Artif. Int. 3809 (2005) 1057-1062
8. Coban R., and Can B. An expert trajectory design for control of nuclear research reactors. Expert Syst. Appl. 36 9 (2009) 11502-11508
9. Fang G., and Dissanayake M.W.M.G. A neural network-based method for time-optimal trajectory planning. Robotica 16 2 (1998) 143-158
10. Flores-Cerrillo J., and MacGregor J.F. Latent variable MPC for trajectory tracking in batch processes. J. Process Control 15 (2005) 651-663
11. Goldberg D.E. Genetic Algorithms in Search, Optimization, and Machine Learning (1989), Addison-Wesley, Reading, MA, Massachusetts
12. Hertz J., Krogh A., and Palmer R.G. Introduction to the Theory of Neural Computation (1991), Addison-Wesley, New York
13. Holland J.H. Adaptation in Natural and Artificial Systems (1975), University of Michigan Press, Ann Arbor, MI
14. Instrumentation System, Operation and Maintenance Manual (1976), General Atomic Co., USA
15. Kravaris C., Wright R.A., and Carrier J.F. Nonlinear controllers for trajectory tracking in batch processes. Comput. Chem. Eng. 13 (1989) 73-82
16. Ladd S.C. Genetic Algorithms in C++ (1996), M&T Books, New York
17. Michalewicz Z. Genetic Algorithms + Data Structures = Evolution Programs (1996), Springer-Verlag, Berlin Heidelberg, New York
18. Omatu S., Khalid M., and Yusof R. Neuro-control and its Applications: Advances in Industrial Control (1996), Springer-Verlag, London
19. Park M.G., and Cho N.Z. Design of a nonlinear model-based controller with adaptive PI gains for robust control of a nuclear reactor. Prog. Nucl. Energy 27 1 (1992) 37-49
20. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning representations by back-propagation errors. Nature 323 (1986) 533-536
21. Werbos, P.J., 1974. Beyond regression: new tools for prediction and analysis in the behavioural sciences. Ph.D. Dissertation, Applied Mathematics, Harvard University, Cambridge.
22. Xu J.X., Lee T.H., and Tan Y. Enhancing trajectory tracking for a class of process control problems using iterative learning. Eng. Appl. Artif. Intell. 15 (2002) 53-64