Abstract
In the framework of the on-going R&D project on fuzzy control applications to the Belgian Reactor I (BR1) at the Belgian Nuclear Research Centre (SCK•CEN), we have constructed a real fuzzy-logic-control demo model. The demo model is suitable for us to test and compare some new algorithms of fuzzy control and intelligent systems, which is advantageous because it is always difficult and time consuming, due to safety aspects, to do all experiments in a real nuclear environment. In this chapter, we first report briefly on the construction of the demo model, and then introduce the results of a fuzzy control, a proportional-integral-derivative (PID) control and an advanced fuzzy control, in which the advanced fuzzy control is a fuzzy control with an adaptive function that can self-regulate the fuzzy control rules. Afterwards, we present a comparative study of those three methods. The results have shown that fuzzy control has more advantages in term of flexibility, robustness, and easily updated facilities with respect to the PID control of the demo model, but that PID control has much higher regulation resolution due to its integration term. The adaptive fuzzy control can dynamically adjust the rule base, therefore it is more robust and suitable to those very uncertain occasions.
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References
Batur, C. and V. Kasparian (1991), “Adaptive expert control,” International Journal of Control, 54 (4), pp. 867–881.
Berenji, H.R. and P. Khedkar (1992), “Learning and tuning fuzzy logic controllers through reinforcements,” IEEE Trans. Neural Networks, 3, pp. 724–740.
Bernard, J.A. (1988), “Use of a rule-based system for process control,” IEEE Control Systems Magazine, 8 (5), pp. 3–13.
Boverie S., B. Demaya and A. Titli (1991), “Fuzzy logic control compared with other automatic control approaches,” Proceedings of the 30th IEEE-CDC conference on decision and control, Brighton, UK, pp. 1212–1216.
Chao, C.T. and C.C. Teng (1997), “A PD-like self-tuning fuzzy controller without steady-state error,” Fuzzy Sets and Systems, 87, pp. 141–154.
Chou, C.H. and H.C. Lu (1994), “A heuristic self-tuning fuzzy controller,” Fuzzy Sets and Systems, 61, pp. 249–264.
Chung, B.M. and J.H. Oh (1993), “Control of dynamic systems using fuzzy learning algorithm,” Fuzzy Sets and Systems, 59, pp. 1–14.
Hah, Y.J. and B.W. Lee (1994), “Fuzzy power control algorithm for a pressurized water reactor,” Nuclear Technology, 106, pp. 242–252.
Halgamuge, K. and M. Glesner, “Neural networks in designing fuzzy systems for real world applications,” Fuzzy Sets and Systems, 65, pp. 1–12.
He, S.Z., S.H., Tan, C.C. Hang and P.Z. Wang (1993), “Control of dynamical processes using an on-line rule-adaptive fuzzy control system,” Fuzzy Sets and Systems, 54, pp. 11–22.
Jang, J.S.R. (1992), “Self-learning fuzzy controllers based on temporal back propagation,” IEEE Trans. on Neural Networks, 3 (5), pp. 714–723.
Karr, C.L. (1991), “Design of an adaptive fuzzy logic controller using a genetic algorithm,” Proc. 4th ICGA, pp. 450–457.
Kosko, B. (1992), Fuzzy Systems and Neural Networks, Prentice-Hall, Englewood Cliffs, NJ.
Li, X. and D. Ruan (1997), “Constructing a fuzzy logic control demo model at SCK•CEN,” Proceedinds of the 5th European Congress on Intelligent Techniques and Soft Computing, pp. 1408–1412.
Li, X., S. Bai and Z. Zhang (1996), “An introduction to a fuzzy adaptive control algorithm,” Chinese Journal of Advanced Software Research, 3 (1), pp. 1–11.
Li, X., S. Bai and Z. Liu (1995), “An algorithm for self-learning and selfcompleting fuzzy control rules,” Informatica 19, pp. 301–312.
Lim, M.H., S. Rahardja and B.H. Gwee (1996), “A GA paradigm for learning fuzzy rules,” Fuzzy Sets and Systems, 82, pp. 177–186.
Lin, C., F. Jeng and C. Lee (1997), “Hierarchical fuzzy logic water level control in advanced oiling water reactors,” Nuclear Technology, 118 (3), pp. 254–263.
Lin, C.T., C.J. Lin and C.S. George Lee (1995), “Fuzzy adaptive learning control network with on-line neural learning,” Fuzzy Sets and Systems, 71, pp. 25–45.
Matsuoka, H. (1990), “A simple fuzzy simulation model for nuclear reactor system dynamics,” Nuclear Technology, 94, pp. 228–241.
Misir, D., H.A. Malki and G. Chen (1996), “Design and analysis of a fuzzy proportional-integral-derivative controller,” Fuzzy Sets and Systems, 79, pp. 297–314.
Mizumoto, M. (1995), “Realization of PID controls by fuzzy control method,” Fuzzy Sets and Systems, 70, pp. 171–182.
Moon, B. S. (1995), “Equivalence between fuzzy logic controllers and PI controllers for single input systems,” Fuzzy Sets and Systems, 69, pp. 105–114.
Omron (1992), C200H-FZ001 Fuzzy Logic Unit, Operation Manual,Omron company.
Procyk, T.J. and E.H. Mamdani (1979), “A linguistic self-organizing process controller,” Automat., 15 (1), pp. 15–30.
Qi, X.M. and T.C. Chin (1997), “Genetic algorithms based fuzzy controller for high order systems,” Fuzzy Sets and Systems, 91, pp. 279–284.
Ruan, D. (1995), “Fuzzy logic in the nuclear research world,” Fuzzy Sets and Systems, 74 (1), pp. 5–13.
Ruan, D. and X. Li (1997), “The test of fuzzy logic control with a closed loop at BR1 reactor,” Proceedings of the 7th World Congress IFSA’97, Prague, Czech Republic, 3, pp. 126–131.
Ruan, D. and X. Li (1998), “Fuzzy-logic control applications to the Belgian reactor 1 (BR1),” Computers and Artificial Intelligence,17(2–3), pp. 127150.
Ruan, D. and A.J. van der Wal (1998), “Controlling the power output of a nuclear reactor with fuzzy logic,” Information Sciences, 110, pp. 151–177.
Ruan, D. and G. Van den Eynde (1999), “Adaptive fuzzy control for simulating a nuclear reactor operation,” 13th European Simulation Multiconference, Warsaw, Poland, June 1–4, 1999.
Shao, S. (1988), “Fuzzy self-organizing controller and its application for dynamic processes,” Fuzzy Sets and Systems, 26, pp. 151–164.
Takagi, H. and I. Hayashi (1991), “NN-driven fuzzy reasoning,” Internat. J. Approximate Reasoning, 5, pp. 191–212.
Tanscheit, R. and E.M. Scharf (1988), “Experiments with the use of a rule based self-organizing controller for robotic applications,” Fuzzy Sets and Systems, 26, pp. 195–214.
Tonshoff, H.K. and A. Walter (1994), “Self-tuning fuzzy-controller for process control in internal grinding,” Fuzzy Sets and Systems, 63, pp. 359373.
Wang, L.X. and J.M. Mendel (1992), “Generating fuzzy rules by learning from examples,” IEEE Trans. Systems Man Cybern., 22, pp. 1414–1427.
Wù, Z.Q., P.Z. Wang, H.H. Teh and S.S. Song (1992), “A rule self-regulating fuzzy controller,” Fuzzy Sets and Systems, 47, pp. 13–21.
Wu, Z.Q. and M. Mizumoto (1996), “PID type fuzzy controller and parameters adaptive method,” Fuzzy Sets and Systems, 78, pp. 23–35.
Zheng, L. (1992), “A practical guide to tune of proportional and integral (PI) like fuzzy controllers,” Proc. of the 1st IEEE Int. Conf. on Fuzzy Systems, pp. 633–640.
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Ruan, D., Li, X., Van den Eynde, G. (2000). Adaptive Fuzzy Control for a Simulation of Hydraulic Analogy of a Nuclear Reactor. In: Ruan, D. (eds) Fuzzy Systems and Soft Computing in Nuclear Engineering. Studies in Fuzziness and Soft Computing, vol 38. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1866-6_4
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DOI: https://doi.org/10.1007/978-3-7908-1866-6_4
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