Performance Monitoring of Closed-Loop Controlled Systems Using dFasArt

  • Jose Manuel Cano-Izquierdo
  • Julio Ibarrola
  • Miguel Almonacid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4528)


This paper analyzes the behaviour of closed-loop controlled systems. Starting from the measured data, the aim is to establish a classification of the system operation states. Digital Signal Processing is used to represent temporal signal with spatial patterns. A neuro-fuzzy scheme (dFasArt) is proposed to classify these patterns, in an on-line way, characterizing the state of controller performance. A real scale plant has been used to carry out several experiments with good results.


dFasArt neuro-fuzzy temporal analysis closed-loop systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bendat, J., Piersol, A.: Random Data: Analysis and Measurement Procedures. Wiley Interscience, Hoboken (2000)zbMATHGoogle Scholar
  2. 2.
    Cano, J., Dimitriadis, Y., Gómez, E., Coronado, J.: Learning from noisy information in FasArt and FasBack neuro-fuzzy systems. Neural Networks 14, 407–425 (2001)CrossRefGoogle Scholar
  3. 3.
    Cano-Izquierdo, J., Almonacid, M., Ibarrola, J., Pinzolas, M.: Use of dynamic neuro fuzzy model dFasArt for identification of stationary states in closed-loop controlled systems. In: Proceedings of EUROFUSE 2007, Jaen, Spain (2007)Google Scholar
  4. 4.
    Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley, Chichester (2000)Google Scholar
  5. 5.
    Harris, T., Seppala, C., Desborough, L.D.: A review of performance monitoring and assessment techniques for univariate and multivariate control systems. Journal of Process Control 9, 1–17 (1999)CrossRefGoogle Scholar
  6. 6.
    Landau, I.: Identification in closed loop: A powerful design tool (better design models, simpler controllers. Control Engineering Practice 9, 51–65 (2001)CrossRefGoogle Scholar
  7. 7.
    Rossi, M., Scali, C.: A comparison of techniques for automatic detection of stiction: Simulation and application to industrial data. Journal of Process Control 15, 505–514 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jose Manuel Cano-Izquierdo
    • 1
  • Julio Ibarrola
    • 1
  • Miguel Almonacid
    • 1
  1. 1.Department of Systems Engineering and Automatic Control, Technical University of Cartagena, Campus Muralla del Mar, 30202 Cartagena, MurciaSpain

Personalised recommendations