Implementation of a Neural Control System Based on PI Control for a Non-linear Process

  • Diego F. Sendoya-LosadaEmail author
  • Diana C. Vargas-Duque
  • Ingrid J. Ávila-Plazas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 833)


This paper explores the possibility of using a machine learning algorithm such as artificial neural networks to control a non-linear liquid level system. To achieve this objective, PI controllers were designed for two different scenarios: In the first, a single PI controller was used to control the system at one setpoint. In the second, 4 PI controllers were designed in order to cover a wider operating range of the plant. The input and output signals from the PI controllers were used to train a controller based on artificial neural networks. The neural network that presented greater simplicity and lower computational cost was selected. In this case, a neural network with 3 hidden layers and 20 neurons per layer was the one that best recreated the dynamics of the PI controllers. The root-mean-square error (RMSE) was used to validate the results obtained with the PI controllers and with the controller based on neural networks. In both scenarios the variations of the error were smaller when the neuronal controller was used than when the PI controllers were used. The results show that machine learning algorithms such as artificial neural networks can be used effectively to control processes whose dynamics are complex.


Artificial neural network Machine learning Neural control PI controller 


  1. 1.
    Shariff, R., Cudrak, A., Zhang, Q., Stanley, S.J.: Advanced process control techniques for water treatment using artificial neural networks. J. Environ. Eng. Sci. 3(S1), S61–S67 (2004)CrossRefGoogle Scholar
  2. 2.
    Naman, A.T., Abdulmuin, M.Z., Arof, H.: Development and application of a gradient descent method in adaptive model reference fuzzy control. In: TENCON 2000 Proceedings, vol. 3, pp. 358–363. IEEE (2000)Google Scholar
  3. 3.
    Xiao, Y., Hu, H., Jiang, H., Zhou, J., Yang, Q.: A adaptive control based neural network for liquid level of molten steel smelting noncrystlloid flimsy alloy line. In: Proceedings of 4th World Congress on Intelligent Control and Automation, China (2002)Google Scholar
  4. 4.
    Hagan, M.T., Demuth, H.B., Jesús, O.D.: An introduction to the use of neural networks in control systems. Int. J. Robust Nonlinear Control 12(11), 959–985 (2002)CrossRefGoogle Scholar
  5. 5.
    Hwang, J.N., Lay, S.R., Maechler, M., Martin, R.D., Schimert, J.: Regression modeling in back-propagation and projection pursuit learning. IEEE Trans. Neural Netw. 5(3), 342–353 (1994)CrossRefGoogle Scholar
  6. 6.
    Denuth, H., Beale, M.: Neural Network Toolbox User’s Guide for Use with MATLAB. The Math Works Inc., Marde (1996)Google Scholar
  7. 7.
    Xu, J., Ho, D.W., Zheng, Y.: A constructive algorithm for feedforward neural networks. In: 5th Asian Control Conference 2004, vol. 1, pp. 659–664. IEEE (2004)Google Scholar
  8. 8.
    Prasad, V., Bequette, B.W.: Nonlinear system identification and model reduction using artificial neural networks. Comput. Chem. Eng. 27(12), 1741–1754 (2003)CrossRefGoogle Scholar
  9. 9.
    Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning-a new frontier in artificial intelligence research [research frontier]. IEEE Comput. Intell. Mag. 5(4), 13–18 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Electronic Engineering, Faculty of EngineeringSurcolombiana UniversityNeiva, HuilaColombia

Personalised recommendations