Abstract
Safety regulations for nuclear reactor control are very strict, which makes it difficult to implement new control techniques. One such technique could be fuzzy logic control (FLC), which can provide very desirable advantages over classical control, like robustness, adaptation and the capability to include human experience into the controller. Simple fuzzy logic controllers have been implemented for a few nuclear research reactors, among which the Massachusetts Institute of Technology (MIT) research reactor [1] in 1988 and the first Belgian reactor (BR1) [2] in 1998, though only on a temporal basis.
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Keywords
- Membership Function
- Fuzzy Logic Controller
- Adaptive Controller
- Fuzzy Logic Control
- Soft Computing Technique
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References
J.A. Bernard. Use of a rule-based system for process control. IEEE Control Systems Magazine, 8(5):3–13, 1988.
D. Ruan. Initial experiments on fuzzy control for nuclear reactor operations at the belgian reactor 1. Nuclear Technology, 143:227–240, August 2003.
A.J. van der Wal D. Ruan. Controlling the power output of a nuclear reactor with fuzzy logic. Information Sciences, 110:151–177, 1998.
D. Ruan. Implementation of adaptive fuzzy control for a real-time control demomodel. Real-Time Systems, 21:219–239, 2001.
D. Ruan, editor. Fuzzy Systems and Soft Computing in Nuclear Engineering. Studies in Fuzzyness and Soft Computing. Physica-Verlag, 2000. ISBN 3-7908–1251–X.
M. Jamshidi S. Heger, N.K. Alang-Rashid. Application of fuzzy logic in nuclear reactor control, part i: An assessment of state-of-the-art. Nuclear Safety, 36(1): 109, 1996.
P.F. Fantoni D. Ruan, editor. Power Plant Surveillance and Diagnostics. Studies in Fuzzyness and Soft Computing. Springer, 2002. ISBN 3-540-43247-7.
Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer Verlag, 1997. ISBN 3-540–60676–9.
D. Beasley J. Heitktter. The hitch-hiker’s guide to evolutionary computation. http://www.cs.bham.ac.uk/Mirrors/ftp.de.uu.net/EC/clife/www/, 2000.
S.E. Haupt R.L. Haupt. Practical Genetic Algorithms. Wiley-Interscience, 1997. ISBN 0-471-18873-5.
D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989. ISBN 0-471-18873–5.
A.S. Wu & H. Yu D.C. Marinescu, H.J.Siegel. A genetic approach to planning in heterogeneous computing environments. In IPDPS, page 97, 2003.
W. Ruijter J.O. Entzinger, R. Spallino. Multilevel distributed structure optimization. Proceedings of the 24th International Congress of the Aeronautical Sciences (ICAS), Yokohama, 2004.
S.F. de Azevedo A. Andršik, A. Mszros. On-line tuning of a neural pid controller based on plant hybrid modeling. Computers and Chemical Engineering, 28 (2004):1499–1509, 2003.
S. Mukhopadhyay J.F. Briceno, H. El-Mounayri. Selecting an artificial neural network for effcient modeling and accurate simulation of the milling process. International Journal of Machine Tools & Manufacture, 42(2002):663–674, 2002.
R. Babuška H.B. Verbruggen J.A. Roubos, S. Mollov. Fuzzy model-based predictive control using takagi-sugeno models. International Journal of Approximate Reasoning, 22(1999):3–30, 1999.
A. Dourado H. Duarte-Ramos P. Gil, J. Henriques. Fuzzy model-based predictive control using takagi-sugeno models. Proceedings of ESIT′99 European Symposium on Intelligent Techniques, Crete, Greece, (1999), june 1999.
Inc The MathWorks. Matlab 6.5, the language of technical computing. Software with online help (www.mathworks.com), 1984–2002.
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Entzinger, J., Ruan, D. (2006). Optimizing Nuclear Reactor Operation Using Soft Computing Techniques. In: Kahraman, C. (eds) Fuzzy Applications in Industrial Engineering. Studies in Fuzziness and Soft Computing, vol 201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33517-X_5
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DOI: https://doi.org/10.1007/3-540-33517-X_5
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