Advertisement

Tuning fuzzy logic controllers by classical techniques

  • M. Santos
  • S. Dormido
  • A. P. de Madrid
  • F. Morilla
  • J. M. de la Cruz
CAST Methods
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1105)

Abstract

Fuzzy Control provides a good support to translate the knowledge of a skilled plant operator into rules, making intelligent control possible. But it is difficult to represent the expert's knowledge with no degradation, so a tuning phase is required. This is not an easy task, and there is not a general procedure for it. On the other hand, most of the control systems are still based on the conventional PID regulator. Aström has developed an empirical tool to predict the achievable performance of these controllers and to assess whether they are properly tuned. Based on Buckley's results, that have analytically proved the equivalence between one of the simplest fuzzy logic controller (FLC) and a PI, it is possible to apply Aström's tool to evaluate the performance of a FLC.

Key words

Fuzzy control Tuning fuzzy controllers PID control Self-tuning Expert control Adpative control 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    K. J. Aström, C. C. Hang, P. Person, and W. K. Ho: Towards intelligent PID control. Automatica 28, 1, 1–9 (1992).Google Scholar
  2. 2.
    J. J. Buckley: Universal fuzzy controllers. Automatica 28, 6, 1245–1248 (1992).Google Scholar
  3. 3.
    H. Ying, W. Siler, J. J. Buckley: Fuzzy control theory: a nonlinear case. Automatica 26, 3, 513–520 (1990).Google Scholar
  4. 4.
    H. Ying, J. J. Buckley: Fuzzy ontroller theory: Limit theorems for linear fuzzy control rules. Automatica 25, 3, 469–472 (1989).Google Scholar
  5. 5.
    S. Dormido, M. Santos, A. P. de Madrid, F. Morilla: Autosintonía de controladores borrosos utilizando técnicas clásicas basadas en reguladores PID. Proc. of III FLAT, España, 1993, pp. 217–225.Google Scholar
  6. 6.
    F. Morilla, S. Dormido, J.L. Fernández, M.A. Canto: A systematic study of PID controllers tuning methods. Proc. IASTED Int. Symp. Modeling Identification and Control, pp. 383–386, 1989.Google Scholar
  7. 7.
    F. Morilla: Controladores PID: ajuste de parámetros. Automática e Instrumentación, 207, 155–160 (1990).Google Scholar
  8. 8.
    K.J. Aström: Automatic tuning of PID Controllers. Instrument Society of America, 1988.Google Scholar
  9. 9.
    J.G. Ziegler, N.B. Nichols: Optimun setting for automatic controllers. Trans. ASME 64, 759–768 (1942).Google Scholar
  10. 10.
    M. Santos: Contribución a las técnicas de sintonía de los controladores basados en la Lógica Borrosa. PhD. Dissertation, 1994.Google Scholar
  11. 11.
    C.C. Lee: A self-learning rule-based controller employing approximate reasoning and neural net concepts. Int. Intelligent Systems, 6, 1, 71–92 (1991).Google Scholar
  12. 12.
    F. Herrera, M. Lozano, J.L. Verdegay: Un algotimo genético para el ajuste de controladores difusos. III FLAT, España, 1993, 251–258.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • M. Santos
    • 1
  • S. Dormido
    • 2
  • A. P. de Madrid
    • 2
  • F. Morilla
    • 2
  • J. M. de la Cruz
    • 1
  1. 1.Dpto. de Informática y Automática. Facultad de Físicas. (UCM.)Ciudad UniversitariaMadridSpain
  2. 2.Dpto. de Informática y Automática. Facultad de Ciencias. (UNED)Ciudad UniversitariaMadridSpain

Personalised recommendations