Skip to main content
Log in

Implementation of Adaptive Fuzzy Control for a Real-Time Control Demo-Model

  • Published:
Real-Time Systems Aims and scope Submit manuscript

Abstract

From 1995–1999 a R&D project on fuzzy control applications to the Belgian Reactor 1 (BR1) was conducted at the Belgian Nuclear Research Centre (SCK·CEN). Due to the safety regulations of the nuclear reactor, it is not realistic to perform many experiments at BR1. In this situation, part of the pre-processing experiments had to be carried outside the reactor (e.g., comparisons of different methods and the preliminary choices of the parameters). Therefore a water-level control system, referred to as a real-time control demo-model, was designed and constructed. In this paper, the construction of the demo-model and related hardware aspects is firstly outlined, then the results of a fuzzy control (Mamdani-type) and an adaptive fuzzy control are presented. The adaptive fuzzy control is a fuzzy control with an adaptive function that can self-regulate the fuzzy control rules. Finally, an implementation of a computer simulation is introduced with an adaptive fuzzy control for this real-time control demo-model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Zadeh, L. A. 1965. Fuzzy sets. Information and Control 8(3); 338-353.

    Google Scholar 

  2. Ruan, D., and van der Wal, A. J. 1998. Controlling the power output of a nuclear reactor with fuzzy logic. Information Sciences 110; 151-177.

    Google Scholar 

  3. Li, X., and Ruan, D. 1997. Constructing a fuzzy logic control demo model at SCK' CEN. In Proceedings of the 5th European Congress on Intelligent Techniques and Soft Computing. Aachen, Germany, 1408-1412.

    Google Scholar 

  4. Ruan, D., and Li, X., 1998. Fuzzy-logic control applications to the Belgian Reactor 1 (BRl). Computers and Artificial Intelligence 17(2-3); 127-150.

    Google Scholar 

  5. Omron. 1992. C200H-FZfJOl Fuzzy Logic Unit, Operation Manual. Omron company.

  6. Batur, C., and Kasparian, V. 1991. Adaptive expert contro!. International Journal of Control 54(4); 867-881.

    Google Scholar 

  7. Tonshoff, H. K., and Walter, A. 1994. Self-tuning fuzzy-controller for process control in internal grinding. Fuzzy Sets and Systems 63; 359-373.

    Google Scholar 

  8. Berenji, H. R., and Khedkar, P. 1992. Learning and tuning fuzzy logic controllers through reinforcements. IEEE Trans. Neural Networks 3; 724-740.

    Google Scholar 

  9. Halgamuge, K., and Glesner, M. 1994. Neural networks in designing fuzzy systems for real world applications. Fuzzy Sets and Systems 65; 1-12.

    Google Scholar 

  10. Jang, J. S. R. 1992. Self-learning fuzzy controllers based on tempora1 back propagation. IEEE Trans. Neural Networks 3(5); 714-723.

    Google Scholar 

  11. Kosko, B. 1992. Fuzzy Systems and Neural Networks. Englewood Cliffs, NJ; Prentice-Ha1!.

    Google Scholar 

  12. Lin, C. T., Lin, C.J., and Lee, G. C. S. 1995. Fuzzy adaptive learning control network with on-line neural learning. Fuzzy Sets and Systems 71; 25-45

    Google Scholar 

  13. Takagi, H., and Hayashi, I. 1991. NN-driven fuzzy reasoning. International Journal of Approximate Reasoning 5; 191-212.

    Google Scholar 

  14. Wang, L. X., and Mendel, J. M. 1992. Generating fuzzy rules by learning from examples. IEEE Trans. Systems. Man and Cybernetics 22; 1414-1427.

    Google Scholar 

  15. Lim, M. H. Rahardja, S., and Gwee. B. H. 1995. A GA paradigm for learning fuzzy rules. Fuzzy Sets and Systems 82; 177-186.

    Google Scholar 

  16. Qi, X. M., and Chin, T. C. 1997. Genetic algorithms based fuzzy controller for high order systems. Fuzzy Sets and Systems 91; 279-284.

    Google Scholar 

  17. He, S. Z., Tan, S. H., Hang, C. C., and Wang, P. Z. 1993. Control of dynamical processes using an on-line rule-adaptive fuzzy control system. Fuzzy Sets and Systems 54; 11-22.

    Google Scholar 

  18. Li, X., Bai, S., and Zhang, Z. 1996. An introduction to a fuzzy adaptive control algorithm. Chinese Journal of Advanced Software Research 3(1); 1-11.

    Google Scholar 

  19. Procyk, T. J., and Mamdani, E. H. 1979. A linguistic self-organizing process controller. Automatica 15(1); 15-30.

    Google Scholar 

  20. Shao, S. 1988. Fuzzy self-organizing controller and its application for dynamic processes. Fuzzy Sets and Systems 26; 151-164.

    Google Scholar 

  21. Tanscheit, R., and Scharf, E. M. 1988. Experiments with the use of a rule based self-organizing controller for robotic applications. Fuzzy Sets and Systems 26; 151-164.

    Google Scholar 

  22. Wu, Z. Q., Wang, P. Z., Teh, H. H. and Song, S. S. 1992. A rule self-regulating fuzzy controller. Fuzzy Sets and Systems 47; 13-21.

    Google Scholar 

  23. Chung, B. M., and Oh, J. H. 1993. Control of dynamic systems using fuzzy learning algorithm. Fuzzy Sets and Systems 59; 1-14.

    Google Scholar 

  24. Li, X, and Ruan, D. 1998. Comparative study of fuzzy control, PID control, and advanced fuzzy control for simulating a nuclear reactor operation. In Proceedinds of the 3rd International FUNS Workshop. Antwerp, Belgium, 424-434.

    Google Scholar 

  25. Ruan, D., and Van den Eynde, G. 1999. Adaptive fuzzy control for simulating a nuclear reactor operation. In Proceedinds of the 13th European Simulation Multiconference. Warsaw, Poland, 311-315.

    Google Scholar 

  26. Jansen, W. J., Venema, R. S., ter Brugge, M. H., Diepenhorst. M, Nijhuis, J. A. G., and Spaanenburg, L. 1995. Stability properties of fuzzy controllers. Benelux Quarterly Journal on Automatic Control, 36(3); 2737.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ruan, D. Implementation of Adaptive Fuzzy Control for a Real-Time Control Demo-Model. Real-Time Systems 21, 219–239 (2001). https://doi.org/10.1023/A:1011180104228

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1011180104228

Navigation