An Approach to Centralized Control Systems Based on Cellular Automata

  • Rosaura Palma-Orozco
  • Gisela Palma-Orozco
  • José de Jesús Medel-Juárez
  • José Alfredo Jiménez-Benítez
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 63)


Computational Intelligence (CI) embraces techniques that use Fractals and Chaos Theory, Artificial immune systems, Wavelets, etc. CI combines elements of learning, adaptation and evolution to create programs that are, in some sense, intelligent. Cellular Automata is an element of Fractals and Chaos Theory that is adaptive and evolutionary, so we are interested in using this approach for solving problems of centralized control. The main objective of the paper is to present the cellular automata as a model of centralized control of dynamic systems. Any dynamic system is subjected to conditions of internal and external behavior that modify its operation and control. This implies that the system can be observable and controllable. The authors take on the task of analysis of an example control traffic system. For the approach, is proposed a one-dimensional cellular automaton in an open-loop scheme.


Cellular Automaton Computational Intelligence Artificial Immune System Chaos Theory External Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Schiff, J.L.: Cellular Automata: A Discrete View of the World. Wiley & Sons, Inc., ChichesterGoogle Scholar
  2. 2.
    Chopard, B., Droz, M.: Cellular Automata Modeling of Physical Systems. Cambridge University Press, Cambridge (1998)zbMATHCrossRefGoogle Scholar
  3. 3.
    Kuo, B.C.: Automatic Control Systems, 6th edn. Prentice-Hall, Englewood Cliffs (1991)Google Scholar
  4. 4.
    Chandrasekharan, P.C.: Robust Control of Linear Dynamical Systems. Academic Press, London (1996)Google Scholar
  5. 5.
    Ogata, K.: Modern Control Engineering. Prentice-Hall, Englewood Cliffs (1993)Google Scholar
  6. 6.
    Wolf, D.E., Schreckenberg, M., Bachem, A. (eds.): Workshop in Traffic and Granular Flow. World Scientific, Singapore (1996)Google Scholar
  7. 7.
    Wolfram, S.: Theory and Applications of Cellular Automata. World Scientific, Singapore (1986)zbMATHGoogle Scholar
  8. 8.
    Biham, O., Middleton, A.A., Levine, D.: Self-organization and a dynamic transition in traffic-flow models. Physical Review A 46, R6124–R6127 (1992)CrossRefGoogle Scholar
  9. 9.
    Lieu, H.: Traffic-Flow Theory. Public Roads, US Dept. of Transportation 62(4) (January/February 1999)Google Scholar
  10. 10.
    May, A.: Traffic Flow Fundamentals. Prentice Hall, Englewood Cliffs (1990)Google Scholar
  11. 11.
    Dorf, R.C., Bishop, R.H.: Modern Control Systems. Addison-Wesley, Reading (1998)zbMATHGoogle Scholar
  12. 12.
    D’azzo, J.J., Houpis, C.H.: Linear Control System Analysis and Design. McGraw-Hill, New York (1995)Google Scholar
  13. 13.
    Phillips, C.L., Harbor, R.D.: Feedback Control Systemas. Prentice-Hall, Englewood Cliffs (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rosaura Palma-Orozco
    • 1
  • Gisela Palma-Orozco
    • 1
  • José de Jesús Medel-Juárez
    • 2
  • José Alfredo Jiménez-Benítez
    • 2
  1. 1.Escuela Superior de Cómputo del IPNZacatencoMéxico
  2. 2.Centro de Investigación de Ciencia Aplicada y Tecnología Avanzada del IPNIrrigaciónMéxico

Personalised recommendations