Enhanced RECCo Controller with Integrated Removing Clouds Mechanism

  • Oualid Lamraoui
  • Hacene Habbi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 64)


The original RECCo controller algorithm evolves with data streams by adding new clouds and tuning the controller parameters in the consequent part autonomously. While performing the control of a given plant, useless information might be involved in the process of evolving the controller structure, which is a problematic issue with regard to control protocol implementation and big data processing. To deal with, in this work, a RECCo controller with removing clouds mechanism is designed. The enhanced RECCo controller is checked for performance from structural viewpoint and compared to the original RECCo controller by considering the problem of temperature control in a parallel heat exchanger.


Robust evolving cloud-based controller Removing clouds Self evolving controller Heat-exchanger 


  1. 1.
    Angelov, P., Škrjanc, I., Blažič, S.: Robust evolving cloud-based controller for a hydraulic plant. In: The 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1–8. IEEE (2013)Google Scholar
  2. 2.
    Andonovski, G., Angelov, P., Blažič, S., Škrjanc, I.: A practical implementation of robust evolving cloud-based controller with normalized data space for heat-exchanger plant. Appl. Soft Comput. 48, 29–38 (2016)CrossRefGoogle Scholar
  3. 3.
    Andonovski, G., Mušič, G., Blažič, S., Škrjanc, I.: On-line evolving cloud-based model identification for production control. IFAC-PapersOnLine 49(5), 79–84 (2016)CrossRefGoogle Scholar
  4. 4.
    Lamraoui, O., Boudouaoui, Y., Habbi, H.: Data-driven approaches for fuzzy prediction of temperature variations in heat exchanger process. In: The 2017 International Conference on Control, Automation and Diagnosis (ICCAD’17). IEEE (2017)Google Scholar
  5. 5.
    Habbi, H., Boudouaoui, Y.: Hybrid artificial bee colony and least squares method for rule-based systems learning. Waset Int. J. Comput. Control Quantum Inf. Eng. 8(12), 1968–1971 (2014)Google Scholar
  6. 6.
    Oravec, J., Bakošová, M., Mészáros, A., Míková, N.: Experimental investigation of alternative robust model predictive control of a heat exchanger. Appl. Therm. Eng. 105, 774–782 (2016)CrossRefGoogle Scholar
  7. 7.
    Jamal, A., Syahputra, R.: Heat exchanger control based on artificial intelligence approach. Int. J. Appl. Eng. Res. (2016)Google Scholar
  8. 8.
    Andonovski, G., Bayas, A., Sáez, D., Blažič, S., Škrjanc, I.: Robust evolving cloud-based control for the distributed solar collector field. In: The 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1570–1577. IEEE (2016)Google Scholar
  9. 9.
    Angelov, P., Yager, R.: Simplified fuzzy rule-based systems using non-parametric antecedents and relative data density. In: The 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS), pp. 62–69. IEEE (2011)Google Scholar
  10. 10.
    Škrjanc, I., Blažič, S., Angelov, P.: Robust evolving cloud-based pid control adjusted by gradient learning method. In: The 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1–8. IEEE (2014)Google Scholar
  11. 11.
    Costa, B., Skrjanc, I., Blazic, S., Angelov, P.: A practical implementation of self-evolving cloud-based control of a pilot plant. In: The 2013 IEEE International Conference on Cybernetics (CYBCONF), pp. 7–12. IEEE (2013)Google Scholar
  12. 12.
    Andonovski, G., Blažič, S., Angelov, P., Škrjanc, I.: Analysis of adaptation law of the robust evolving cloud-based controller. In: The 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1–7. IEEE (2015)Google Scholar
  13. 13.
    Dovžan, D., Logar, V., Škrjanc, I.: Implementation of an evolving fuzzy model (eFuMo) in a monitoring system for a waste-water treatment process. IEEE Trans. Fuzzy Syst. 23(5), 1761–1776 (2015)CrossRefGoogle Scholar
  14. 14.
    Habbi, H., Kidouche, M., Kinnaert, M., Zelmat, M.: Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger. Int. J. Syst. Sci. 42(4), 587–599 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Habbi, H., Kinnaert, M., Zelmat, M.: A complete procedure for leak detection and diagnosis in a complex heat exchanger using data-driven fuzzy models. ISA Trans. 48(3), 354–361 (2009)CrossRefGoogle Scholar
  16. 16.
    Habbi, H., Boudouaoui, Y., Karaboga, D., Ozturk, C.: Self-generated fuzzy systems design using artificial bee colony optimization. Inf. Sci. 295, 145–159 (2015)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Van Overschee, P., De Moor, B.: N4sid: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica 30(1), 75–93 (1994)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Applied Automation LaboratoryFHC, M’hamed Bougara University of BoumerdèsBoumerdèsAlgeria

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