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PID-ITS: An Intelligent Tutoring System for PID Tuning Learning Process

  • Esteban Jove
  • Héctor Alaiz-Moretón
  • Isaías García-Rodríguez
  • Carmen Benavides-Cuellar
  • José Luis Casteleiro-Roca
  • José Luis Calvo-Rolle
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 649)

Abstract

A new developed tool for PID learning is described on this work. The main contribution is the possibility to assist non-experimented users on the PID tuning task. For its implementation, knowledge engineering was used by the conceptual model creation and its next formalization on the described tool. Very good results have been achieved in general terms when it was validated with users without expertise on the control field. Both aims were achieved, the right learning of the traditional PID tuning by empirical methods and the assistance during tuning over systems.

Keywords

Intelligent tutoring system PID controller Tuning procedure learning 

References

  1. 1.
    Åström, K.J., Wittenmark, B.: Computer-controlled systems: theory and design (2013)Google Scholar
  2. 2.
    Guzman, J.L., Astrom, K.J., Dormido, S., Hagglund, T., Berenguel, M., Piguet, Y.: Interactive learning modules for PID control [Lecture Notes]. IEEE Control Syst. 28, 118–134 (2008)CrossRefGoogle Scholar
  3. 3.
    Alaiz Moretón, H., Calvo Rolle, J.L., García, I., Alonso Alvarez, A.: Formalization and practical implementation of a conceptual model for PID controller tuning. Asian J. Control. 13, 773–784 (2011)CrossRefMATHGoogle Scholar
  4. 4.
    Machon-Gonzalez, I., Lopez-Garcia, H., Calvo-Rolle, J.L.: A hybrid batch SOM-NG algorithm. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–5 (2010)Google Scholar
  5. 5.
    Calvo-Rolle, J.L., Quintian-Pardo, H., Corchado, E., Meizoso-López, M.C., Ferreiro García, R.: Simplified method based on an intelligent model to obtain the extinction angle of the current for a single-phase half wave controlled rectifier with resistive and inductive load. J. Appl. Log. 13, 37–47 (2015)CrossRefGoogle Scholar
  6. 6.
    Crespo-Ramos, M.J., Machón-González, I., López-García, H., Calvo-Rolle, J.L.: Detection of locally relevant variables using SOM–NG algorithm. Eng. Appl. Artif. Intell. 26, 1992–2000 (2013)CrossRefGoogle Scholar
  7. 7.
    Jove, E., Aláiz-Moretón, H., Casteleiro-Roca, J.L., Corchado, E., Calvo-Rolle, J.L.: Modeling of bicomponent mixing system used in the manufacture of wind generator blades. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 275–285 (2014)Google Scholar
  8. 8.
    Casteleiro-Roca, J.L., Calvo-Rolle, J.L., Meizoso-López, M.C., Piñón-Pazos, A.P., Rodríguez-Gómez, B.A.: New approach for the QCM sensors characterization. Sens. Actuators A Phys. 207, 1–9 (2014)CrossRefGoogle Scholar
  9. 9.
    Guzmán, J.L., Hägglund, T., Åström, K.J., Dormido, S., Berenguel, M., Piguet, Y.: Understanding PID design through interactive tools. In: IFAC Proceedings, vol. 47, pp. 12243–12248 (2014)Google Scholar
  10. 10.
    Calvo-Rolle, J.L., Alonso-Alvarez, A., Ferreiro-Garcia, R.: Using knowledge enginering in a PID regulator in non linear process control. Ing. Quim. 32, 21–28 (2007)Google Scholar
  11. 11.
    Casteleiro-Roca, J.L., Calvo-Rolle, J.L., Meizoso-López, M.C., Piñón-Pazos, A.J., Rodríguez-Gómez, B.A.: Bio-inspired model of ground temperature behavior on the horizontal geothermal exchanger of an installation based on a heat pump. Neurocomputing 150, 90–98 (2015)CrossRefGoogle Scholar
  12. 12.
    Ferreiro García, R., Calvo Rolle, J.L., Romero Gomez, M., DeMiguel Catoira, A.: Expert condition monitoring on hydrostatic self-levitating bearings. Expert Syst. Appl. 40, 2975–2984 (2013)CrossRefGoogle Scholar
  13. 13.
    Casteleiro-Roca, J.L., Pérez, J.A.M., Piñón-Pazos, A.J., Calvo-Rolle, J.L., Corchado, E.: Modeling the electromyogram (EMG) of patients undergoing anesthesia during surgery. In: 10th International Conference on Soft Computing Models in Industrial and Environmental Applications, pp. 273–283 (2015)Google Scholar
  14. 14.
    Casteleiro-Roca, J.-L., Calvo-Rolle, J.L., Méndez Pérez, J.A., Roqueñí Gutiérrez, N., de Cos Juez, F.J.: Hybrid intelligent system to perform fault detection on bis sensor during surgeries. Sensors 17, 179 (2017)CrossRefGoogle Scholar
  15. 15.
    Quintián, H., Calvo-Rolle, J.L., Corchado, E.: A hybrid regression system based on local models for solar energy prediction. Informatica 25, 265–282 (2014)CrossRefGoogle Scholar
  16. 16.
    Quintián-Pardo, H., Calvo-Rolle, J.L., Fontenla-Romero, O.: Application of a low cost commercial robot in task of tracking of objects. DYNA 175, 24–33 (2012)Google Scholar
  17. 17.
    Karl, J.Å., Hägglund, T.: PID controllers: theory, design and tuning (1995)Google Scholar
  18. 18.
    Guzmán, J.L., García, P., Hägglund, T., Dormido, S., Albertos, P., Berenguel, M.: Interactive tool for analysis of time-delay systems with dead-time compensators. Control Eng. Pract. 16, 824–835 (2008)CrossRefGoogle Scholar
  19. 19.
    Guzman, J.L., Dormido, S., Berenguel, M.: Interactivity in education: an experience in the automatic control field. Comput. Appl. Eng. Educ. 21, 360–371 (2013)CrossRefGoogle Scholar
  20. 20.
    Calvo-Rolle, J.L., Alaiz-Moretón, H., Alfonso-Cendón, J., Alonso-Álvarez, Á., Ferreiro-García, R.: Development of a conceptual model for a knowledge-based system for the design of closed-loop PID controllers. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 58–65 (2009)Google Scholar
  21. 21.
    Calvo-Rolle, J.L.C., Ferreiro-García, R., López García, H., González, I.M.: Developed an expert system of an empirical method to choose correct expressions for PID controllers tuning in open loop. In: Industrial Electronics, 2009. IECON’09. 35th Annual Conference of IEEE, pp. 2044–2049 (2009)Google Scholar
  22. 22.
    Calvo-Rolle, J.L., Machón-González, I., López-García, H.: Neuro-Robust controller for non-linear systems. DYNA 86, 308–317 (2011)CrossRefGoogle Scholar
  23. 23.
    Calvo-Rolle, J., Corchado, E., Quintian-Pardo, H., García, R., Román, J., Hernández, P.: A novel hybrid intelligent classifier to obtain the controller tuning parameters for temperature control. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.B. (eds.) Hybrid Artificial Intelligent Systems, pp. 677–689. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Esteban Jove
    • 1
  • Héctor Alaiz-Moretón
    • 2
  • Isaías García-Rodríguez
    • 2
  • Carmen Benavides-Cuellar
    • 2
  • José Luis Casteleiro-Roca
    • 1
  • José Luis Calvo-Rolle
    • 1
  1. 1.Departamento de Ingeniería IndustrialUniversity of A CoruñaA CoruñaSpain
  2. 2.Department of Electrical, Systems and Automatics Engineering, School of EngineeringUniversity of LeónLeónSpain

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