Advertisement

A Hybrid Intelligent System for Generic Decision for PID Controllers Design in Open-Loop

  • José Luis Calvo-Rolle
  • Emilio Corchado
  • Amer Laham
  • Ramón Ferreiro García
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6679)

Abstract

This study presents a novel hybrid generic decision method to obtain the best parameters of a PID (Proportional-Integral-Derivative) controller for desired specifications. The method used is to develop a ruled-based conceptual model of knowledge based system for PID design in Open-Loop. The study shows a hybrid system based on the organization of existing rules and a new way to obtain other specific ones based on decision trees. The model achieved chooses the best controller parameters, between different open loop tuning methods. For this purpose an automatic classification of a huge dataset is used. Data was obtained by applying considered tuning methods to a collection of representative systems. The propose hybrid system has been tested on a temperature control of a ceramic furnace plant.

Keywords

Knowledge engineering PID open-loop tuning ruled-based system hybrid intelligent system 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Calvo-Rolle, J.L., et al.: Development of a conceptual model for a knowledge-based system for the design of closed-loop PID controllers. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 58–65. Springer, Heidelberg (2009)Google Scholar
  2. 2.
    Astrom, K.J., Hagglund, T.: Advanced PID Control. ISA, Research Triangle Park. North Carolina (2006)Google Scholar
  3. 3.
    Feng, Y.L., Tan, K.C.: PIDeasyTM and automated generation of optimal PID controllers. In: Third Asia-Pacific Conference on Measurement and Control, China, pp. 29–33 (1998)Google Scholar
  4. 4.
    Chun-Fei, H., Guan-Ming, C., Tsu-Tian, L.: Robust intelligent tracking control with PID-type learning algorithm. Neurocomputing 71, 234–243 (2007)CrossRefGoogle Scholar
  5. 5.
    Hung-Ching, L., Jui-Chi, C., Ming-Feng, Y.: Design and analysis of direct-action CMAC PID controller. Neurocomputing 70, 2615–2625 (2007)CrossRefGoogle Scholar
  6. 6.
    Jun, Y.: Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot. Neurocomputing 71, 1561–1565 (2008)CrossRefGoogle Scholar
  7. 7.
    Liu, H., Coghill, G.M.: A model-based approach to robot fault diagnosis. Knowledge-Based Systems 18, 225–233 (2005)CrossRefGoogle Scholar
  8. 8.
    Sala, A., Cuenca, A., Salt, J.: A retunable PID multi-rate controller for a networked control system. Information Sciences 179, 2390–2402 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Gottwald, S.: Mathematical Fuzzy Control. A Survey of Some Recent Results. Logic Journal of IGPL 13, 525–541 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Yau-Tarng, J., Yun-Tien, C., Chih-Peng, H.: Design of fuzzy PID controllers using modified triangular membership functions. Information Sciences 178, 1325–1333 (2008)CrossRefzbMATHGoogle Scholar
  11. 11.
    Rodrigues-Sumar, R., Rodrigues-Coelho, A.A., dosSantos-Coelho, L.: Computational intelligence approach to PID controller design using the universal model. Information Sciences 180, 3980–3991 (2010)CrossRefGoogle Scholar
  12. 12.
    Zhang, J., Zhuang, J., Du, H., Wang, S.: Self-organizing genetic algorithm based tuning of PID controllers. Information Sciences 179, 1007–1018 (2009)CrossRefzbMATHGoogle Scholar
  13. 13.
    Thangaraj, R., Chelliah, T., 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 (2010), doi:10.1093/jigpal/jzq031Google Scholar
  14. 14.
    Kareem-Jaradat, M.A., Langari, R.: A hybrid intelligent system for fault detection and sensor fusion. Applied Soft Computing 9, 415–422 (2009)CrossRefGoogle Scholar
  15. 15.
    Karr, C.L.: Control of a phosphate processing plant via a synergistic architecture for adaptive, intelligent control. Engineering Applications of Artificial Intelligence 16, 21–30 (2003)CrossRefGoogle Scholar
  16. 16.
    Hu, W., Starr, A.G., Zhou, Z., Leung, A.Y.T.: A systematic approach to integrated fault diagnosis of flexible manufacturing systems. International Journal of Machine Tools and Manufacture 40, 1587–1602 (2000)CrossRefGoogle Scholar
  17. 17.
    Gholamian, M.R., Fatemi Ghomi, S.M.T., Ghazanfari, M.: A hybrid intelligent system for multiobjective decision making problems. Computers & Industrial Engineering 51, 26–43 (2006)CrossRefzbMATHGoogle Scholar
  18. 18.
    Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72, 2729–2730 (2009)CrossRefGoogle Scholar
  19. 19.
    Corchado, E., Abraham, A., Ferreira de Carvalho, A.C.: Hybrid intelligent algorithms and applications. Information Science 180, 2633–2634 (2010)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wilson, D.I.: Towards intelligence in embedded PID controllers. In: Proceedings of the 8th IASTED Intelligent Systems & Control, Cambridge (2005)Google Scholar
  21. 21.
    Zhou, L., Li, X., Hu, T., Li, H.: Development of high-precision power supply based on expert self-tuning control. In: Control Systems and Robotics ICMIT 2005, Wuhan, China, pp. 60421T.1–60421T.6. (2005)Google Scholar
  22. 22.
    Epshtein, V.L.: Hypertext knowledge base for the control theory. Automation and Remote Control 61, 1928–1933 (2001)zbMATHGoogle Scholar
  23. 23.
    Astrom, K.J., Hagglund, T.: Benchmark Systems for PID Control. In: Preprints FAC Workshop on Dig. Control. Past, Present and Future of PID Control, Tarrasa, pp. 181–182 (2000)Google Scholar
  24. 24.
    Parr, O.: Data Mining Cookbook. Modeling Data for Marketing, Risk, and Customer Relationship Management. John Wiley & Sons, Inc., New York (2001)Google Scholar
  25. 25.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, Inc., Chichester (2001)zbMATHGoogle Scholar
  26. 26.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  27. 27.
    Frank, E., Witten, I.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  28. 28.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)Google Scholar
  29. 29.
    Chun-Fei, H., Guan-Ming, C., Tsu-Tian, L.: Robust intelligent tracking control with PID-type learning algorithm. Neurocomputing 71, 234–243 (2007)CrossRefGoogle Scholar
  30. 30.
    Hung-Ching, L., Jui-Chi, C., Ming-Feng, Y.: Design and analysis of direct-action CMAC PID controller. Neurocomputing 70, 2615–2625 (2007)CrossRefGoogle Scholar
  31. 31.
    Rodrigues Sumar, R., Rodrigues Coelho, A., dos Santos Coelho, L.: Computational intelligence approach to PID controller design using the universal model. Information Sciences 180, 3980–3991 (2010)CrossRefGoogle Scholar
  32. 32.
    Toscano, R.: Robust synthesis of a PID controller by uncertain multimodel approach. Information Sciences 177, 1441–1451 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Tsai, M.T., Tung, P.C., Chen, K.Y.: Experimental evaluations of proportional–integral–derivative type fuzzy controllers with parameter adaptive methods for an active magnetic bearing system. Expert Systems 28, 5–18 (2011)CrossRefGoogle Scholar
  34. 34.
    Hwa Kim, D.: Hybrid GA-BF based intelligent PID controller tuning for AVR system. Applied Soft Computing 11, 11–22 (2011)CrossRefGoogle Scholar
  35. 35.
    Verikas, A., Kalsyte, Z., Bacauskiene, M., Gelzinis, A.: Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey. Soft Computing 14, 995–1010 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • José Luis Calvo-Rolle
    • 1
  • Emilio Corchado
    • 2
  • Amer Laham
    • 2
  • Ramón Ferreiro García
    • 1
  1. 1.Department de Ingeniería IndustrialUniversidad de La CoruñaFerrolSpain
  2. 2.Departamento de Informática y AutomáticaUniversidad de SalamancaSalamancaSpain

Personalised recommendations