A Neural Model for Delay Correction in a Distributed Control System

  • Ana Antunes
  • Fernando Morgado Dias
  • Alexandre Mota
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5164)


Distributed control systems frequently suffer from variable sampling to actuation delay. This delay can degrade the control quality of such systems. In some distributed control systems it is possible to know, at control time, the value of the delay. The work reported in this paper proposes to build a model of the behavior of the system in the presence of the variable delay and to use this model to compensate the control signal in order to avoid the delay effect. This model can be used as a compensator that can easily be added to an existing control system that does not account for the sampling to actuation delay effect. The compensator can be applied to distributed systems using online or off-line scheduling policies provided that the sampling to actuation delay can be evaluated. The effectiveness of the neural network delay compensator is evaluated using a networked control system with a pole-placement controller.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cervin, A.: Integrated Control and Real-Time Scheduling. PhD thesis, Lund Institute of Technologie (2003)Google Scholar
  2. 2.
    Tipsuwan, Y., Chow, M.Y.: Control methodologies in networked control systems. IFAC Control engineering practice 11, 1099–1111 (2003)CrossRefGoogle Scholar
  3. 3.
    Colom, P.: Analysis and Design of Real-Time Control Systems with Varying Control Timing Constraints. PhD thesis, Universitat Politècnica de Catalunya (2002)Google Scholar
  4. 4.
    Sanfridson, M.: Timing problems in distributed real-time computer control systems. Technical Report ISSN 1400-1179, Royal Institute of Techology (2000)Google Scholar
  5. 5.
    Antunes, A., Dias, F.: Influence of the sampling period in the performance of a real-time distributed system under jitter conditions. WSEAS Transactions on communications 3, 248–253 (2004)Google Scholar
  6. 6.
    Antunes, A., Mota, A.: Control performance of a real-time adaptive distributed control system under jitter conditions. In: Proc. of the Control 2004 Conference (2004)Google Scholar
  7. 7.
    Almutairi, N., Chow, M.Y., Tipsuwan, Y.: Networked-based controlled DC motor with fuzzy compensation. In: Proc. of the 27th annual conference of the IEEE Industrial Electronics Society, vol. 3, pp. 1844–1849 (2001)Google Scholar
  8. 8.
    Lin, C.L., Chen, C.H., Huang, H.C.: Stabilizing control of networks with uncertain time varying communication delays. Control Engineering Practice 16, 56–66 (2008)CrossRefGoogle Scholar
  9. 9.
    Antunes, A., Dias, F., Vieira, J., Mota, A.: Delay compensator: An approach to reduce the variable sampling to actuation delay effect in distributed real-time control systems. In: 11th IEEE International Conference on Emerging Technologies and Factory Automation, Prague, Czech Republic (2006)Google Scholar
  10. 10.
    Sørensen, O.: Neural networks in control applications. PhD Thesis, Department of Control Engineering, Institute of Electronic Systems, Aalborg University, Denmark (1994)Google Scholar
  11. 11.
    Astrom, K.J., Wittenmark, B.: Computer Controlled Systems: Theory and Design, 3rd edn. Prentice Hall, Englewood Cliffs (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ana Antunes
    • 1
  • Fernando Morgado Dias
    • 2
  • Alexandre Mota
    • 3
  1. 1.Departamento de Engenharia ElectrotécnicaEscola Superior de Tecnologia de Setúbal do Instituto Politécnico de SetúbalSetúbalPortugal
  2. 2.Centro de Ciências Matemáticas - CCM and Departamento de Matemática e EngenhariasUn. da MadeiraMadeiraPortugal
  3. 3.DETI/Universidade de AveiroAveiroPortugal

Personalised recommendations