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A Novel Hybrid Intelligent Classifier to Obtain the Controller Tuning Parameters for Temperature Control

  • José Luis Calvo-Rolle
  • Emilio Corchado
  • Héctor Quintian-Pardo
  • Ramón Ferreiro García
  • Jesús Ángel Román
  • Pedro Antonio Hernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7208)

Abstract

This study presents a novel hybrid classifier method to obtain the best parameters of a PID controller for desired specifications. The study presents a hybrid system based on the organization of existing rules and classifier models that select the optimal expressions to improve specifications. The model achieved chooses the best controller parameters among different closed loop tuning methods. The classifiers are based on ANN and SVM. The proposal was tested on the temperature control of a laboratory stove.

Keywords

Hybrid classifier PID closed-loop tuning intelligent control 

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References

  1. 1.
    Hsu, C., Chen, G., Lee, T.: Robust intelligent tracking control with PID-type learning algorithm. Neurocomputing 71, 234–243 (2007)CrossRefGoogle Scholar
  2. 2.
    Gottwald, S.: Mathematical Fuzzy Control. A Survey of Some Recent Results. Logic Journal of IGPL 13, 525–541 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Zhang, J., Zhuang, J., Du, H., Wang, S.: Self-organizing genetic algorithm based tuning of PID controllers. Information Sciences 179, 1007–1018 (2009)zbMATHCrossRefGoogle Scholar
  4. 4.
    Juang, Y., Chang, Y., Huang, C.: Design of fuzzy PID controllers using modified triangular membership functions. Information Sciences 178, 1325–1333 (2008)zbMATHCrossRefGoogle Scholar
  5. 5.
    Liu, H., Coghill, G.: A model-based approach to robot fault diagnosis. Knowledge-Based Systems 18, 225–233 (2005)CrossRefGoogle Scholar
  6. 6.
    Lu, H., Chang, J., Yeh, M.: Design and analysis of direct-action CMAC PID controller. Neurocomputing 70, 2615–2625 (2007)CrossRefGoogle Scholar
  7. 7.
    Sala, A., Cuenca, Á., Salt, J.: A retunable PID multi-rate controller for a networked control system. Information Sciences 179, 2390–2402 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Sumar, R.R., Coelho, A.A.R., Coelho, L.D.S.: Computational intelligence approach to PID controller design using the universal model. Information Sciences 180, 3980–3991 (2010)CrossRefGoogle Scholar
  9. 9.
    Thangaraj, R., Chelliah, T.R., Pant, M., Abraham, A., Grosan, C.: Optimal gain tuning of PI speed controller in induction motor drives using particle swarm optimization. Logic Journal of IGPL 19, 343–356 (2010)CrossRefGoogle Scholar
  10. 10.
    Juang, Y., Chang, Y., Huang, C.: Design of fuzzy PID controllers using modified triangular membership functions. Information Sciences 178, 1325–1333 (2008)zbMATHCrossRefGoogle Scholar
  11. 11.
    Ye, J.: Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot. Neurocomputing 71, 1561–1565 (2008)CrossRefGoogle Scholar
  12. 12.
    Romero, J.A., Sanchis, R., Balaguer, P.: PI and PID auto-tuning procedure based on simplified single parameter optimization. Journal of Process Control 21, 840–851 (2011)CrossRefGoogle Scholar
  13. 13.
    Sun, J., Zhang, D., Li, X., Zhang, J., Du, D.: Smith Prediction Monitor AGC System Based on Fuzzy Self-Tuning PID Control. International Journal of Iron and Steel Research 17, 22–26 (2010)CrossRefGoogle Scholar
  14. 14.
    Astrom, K.J., Hagglund, T.: Advanced PID Control. Pearson Education, Madrid (2009)Google Scholar
  15. 15.
    Feng, Y.L., Tan, K.C.: Process identification and PID Control. John Wiley & Sons, USA (2009)Google Scholar
  16. 16.
    Visioli, A.: Practical PID Control. Springer, London (2010)Google Scholar
  17. 17.
    Johnson, M.A., Moradi, M.H.: PID Control: New identification and Design methods. Springer, London (2010)Google Scholar
  18. 18.
    Seki, H., Shigemasa, T.: Retuning oscillatory PID control loops based on plant operation data. J. Process Control 20, 217–227 (2010)CrossRefGoogle Scholar
  19. 19.
    Tyreus, B.D., Luyben, W.L.: Tuning PI controllers for integrator/dead time processes. Industrial Engineering Chemistry Research 11, 2625–2628 (1992)Google Scholar
  20. 20.
    Astrom, K.J., Hagglund, T.: Benchmark Systems for PID Control. In: Preprints FAC Workshop on Digital Control. Past, Present and Future of PID Control, Tarrasa, pp. 181–182 (2000)Google Scholar
  21. 21.
    Vapnic, V.: The nature of statistical learning theory. Springer, New York (1995)Google Scholar
  22. 22.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)Google Scholar
  23. 23.
    Parr, O.: Data Mining Cookbook. Modeling Data for Marketing, Risk, and Customer Relationship Management. John Wiley & Sons, Inc., New York (2001)Google Scholar
  24. 24.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, Inc., Canada (2001)zbMATHGoogle Scholar
  25. 25.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  26. 26.
    Frank, E., Witten, I.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann (2005)Google Scholar
  27. 27.
    Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. World Scientific Publishing, USA (2008)zbMATHGoogle Scholar
  28. 28.
    Alpaydin, E.: Introduction to Machine Learning. The MIT Press, Oxford (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • José Luis Calvo-Rolle
    • 1
  • Emilio Corchado
    • 2
  • Héctor Quintian-Pardo
    • 2
  • Ramón Ferreiro García
    • 1
  • Jesús Ángel Román
    • 2
  • Pedro Antonio Hernández
    • 3
  1. 1.Department de Ingeniería IndustrialUniversidad de La CoruñaFerrolSpain
  2. 2.Departamento de Informática y AutomáticaUniversidad de SalamancaSalamancaSpain
  3. 3.Departamento de Expresión Gráfica en la IngenieríaUniversidad de SalamancaZamoraSpain

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