PID Tuning with Neural Networks

  • Antonio Marino
  • Filippo NeriEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11431)


In this work we will report our initial investigation of how a neural network architecture could become an efficient tool to model Proportional-Integral-Derivative controller (PID controller). It is well known that neural networks are excellent function approximators, we will then be investigating if a recursive neural networks could be suitable to model and tune PID controllers thus could assist in determining the controller’s proportional, integral, and the derivative gains. A preliminary evaluation is reported.


PID tuning and approximation Machine learning Neural networks 


  1. 1.
    Huailin Shu, Y.P.: Decoupled temperature control system based on PID neural network. In: ACSE 05 Conference, CICC, Cairo, Egypt, 19–21 December 2005 (2005)Google Scholar
  2. 2.
    Ziegler, J.G., Nichols, N.B.: Optimum settings for automatic controllers. Trans. ASME 64, 759–768 (1942)Google Scholar
  3. 3.
    Boubertakh, H., Tadjine, M., Glorennec, P.Y., Labiod, S.: Tuning fuzzy PD and PI controllers using reinforcement learning. ISA Trans. 49, 543–551 (2010)CrossRefGoogle Scholar
  4. 4.
    Carlucho, I., Paula, M.D., Villar, S.A., Acosta, G.G.: Incremental Q-learning strategy for adaptive pid control of mobile robots. Expert Syst. Appl. 80, 183–199 (2017)CrossRefGoogle Scholar
  5. 5.
    Zhang, J., Wang, N., Wang, S.: A developed method of tuning PID controllers with fuzzy rules for integrating processes. In: Proceeding of the 2004 American Control Conference Boston, Massachusetts, 30 June -2 July 2 (2004)Google Scholar
  6. 6.
    Kim, J.S., Kim, J.H., Park, J.M., Park, S.M., Choe, W.Y., Heo, H.: Auto tuning PID controller based on improved genetic algorithm for reverse osmosis plant. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 211 (2008)Google Scholar
  7. 7.
    Salem, A., Hassan, M.A.M., Ammar, M.E.: Tuning PID controllers using artificial intelligence techniques applied to DC-motor and AVR system. Asian J. Eng. Technol. 22 (2014). ISSN 2321–2462Google Scholar
  8. 8.
    Muderrisoglu, K., Arisoy, D.O., Ahan, A.O., Akdogan, E.: PID parameters prediction using neural network for a linear quarter car suspension control. Int. J. Intell. Syst. Appl. Eng. (2014)Google Scholar
  9. 9.
    Scott, G.M., Shavlik, J.W., Ray, W.H.: Refining PID controllers using neural networks. National Science Foundation Graduate Fellowship, pp. 555–562 (1994)Google Scholar
  10. 10.
    Shen, J.C.: Fuzzy neural networks for tuning PID controller for plants with underdamped responses. IEEE Trans. Fuzzy Syst. 9(2), 333–342 (2001)CrossRefGoogle Scholar
  11. 11.
    Killingsworth, N., Krstic, M.: PID tuning using extremum seeking: online, model-free performance optimization. IEEE Control Syst. 26, 70–79 (2006)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Papoutsidakis, M., Piromalis, D., Neri, F., Camilleri, M.: Intelligent algorithms based on data processing for modular robotic vehicles control. WSEAS Trans. Syst. 13, 242–251 (2014)Google Scholar
  13. 13.
    Neri, F.: PIRR: a methodology for distributed network management in mobile networks. WSEAS Trans. Inf. Sci. Appl. 5, 306–311 (2008)Google Scholar
  14. 14.
    Draper, C., Li, Y.: Principles of optimalizing control systems and an application to the internal combustion engine. Optimal and Selfoptimizing Control (1951)Google Scholar
  15. 15.
    Rumelhart, D.E., Widrow, B., Lehr, M.A.: The basic ideas in neural networks. Commun. ACM 37, 87–92 (1994)CrossRefGoogle Scholar
  16. 16.
    Neri, F.: Learning and predicting financial time series by combining natural computation and agent simulation. In: Di Chio, C., et al. (eds.) EvoApplications 2011. LNCS, vol. 6625, pp. 111–119. Springer, Heidelberg (2011). Scholar
  17. 17.
    Neri, F.: A comparative study of a financial agent based simulator across learning scenarios. In: Cao, L., Bazzan, A.L.C., Symeonidis, A.L., Gorodetsky, V.I., Weiss, G., Yu, P.S. (eds.) ADMI 2011. LNCS (LNAI), vol. 7103, pp. 86–97. Springer, Heidelberg (2012). Scholar
  18. 18.
    Staines, A., Neri, F.: A matrix transition oriented net for modeling distributed complex computer and communication systems. WSEAS Trans. Syst. 13, 12–22 (2014)Google Scholar
  19. 19.
    Neri, F.: Agent-based modeling under partial and full knowledge learning settings to simulate financial markets. AI Commun. 25, 295–304 (2012)Google Scholar
  20. 20.
    Neri, F.: Case study on modeling the silver and nasdaq financial time series with simulated annealing. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds.) WorldCIST 2018. AISC, vol. 746, pp. 755–763. Springer, Cham (2018). Scholar
  21. 21.
    Neri, F.: Combining machine learning and agent based modeling for gold price prediction. In Cagnoni, S. (ed.) WIVACE 2018, Workshop on Artificial Life and Evolutionary Computation, vol. tbd. Springer (2018, in press)Google Scholar
  22. 22.
    Neri, F.: Can agent based models capture the complexity of financial market behavior. In: 42nd Annual Meeting of the AMASES Association for Mathematics Applied to Social and Economic Sciences, Napoli. University of Naples and Parthenope University Press (2018, in press)Google Scholar
  23. 23.
    Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.: A classification of hyper-heuristics approaches. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research and Management Science, vol. 146, pp. 449–468. Springer, Heidelberg (2009). In pressCrossRefGoogle Scholar
  24. 24.
    Camilleri, M., Neri, F., Papoutsidakis, M.: An algorithmic approach to parameter selection in machine learning using meta-optimization techniques. WSEAS Trans. Syst. 13, 203–212 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Information TechnologiesUniversity of NaplesNaplesItaly

Personalised recommendations