Advertisement

An Evolutionary Intelligent Approach for the LTI Systems Identification in Continuous Time

  • Luis MoralesEmail author
  • Oscar Camacho
  • Danilo Chávez
  • José Aguilar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 895)

Abstract

Identification and modeling of systems are the first stage for development and design of controllers. For this purpose, as an alternative to conventional modeling approaches we propose using two methods of evolutionary computing: Genetic Algorithms (GA) and Particle Swarm Optimization (PSO to create an algorithm for modeling Linear Time Invariant (LTI) systems of different types. Integral Square Error (ISE) is the objective function to minimize, which is calculated between the outputs of the real system and the model. Unlike other works, the algorithms make a search of the most approximate model based on four of the most common ones found in industrial processes: systems of first order, first order plus time delay, second order and inverse response. The estimated models by our algorithms are compared with the obtained by other analytical and heuristic methods, in order to validate the results of our approach.

Keywords

System modeling System identification Genetic algorithms Particle swarm optimization 

References

  1. 1.
    Smith, C., Corripio, A.: Principles and Practice of Automatic Process Control, 3rd edn. Wiley, New York (2006)Google Scholar
  2. 2.
    Johnson, M., Moradi, M.: PID Control - New Identification and Design Methods. Springer, London (2005).  https://doi.org/10.1007/1-84628-148-2CrossRefGoogle Scholar
  3. 3.
    Kristinsson, K., Dumont, G.A.: System identification and control using genetic algorithms. IEEE Trans. Syst. Man. Cybern. 22(5), 1033–1046 (1992)CrossRefGoogle Scholar
  4. 4.
    Johnson, T., Husbands, P.: System identification using genetic algorithms. In: Parallel Problem Solving from Nature, no. 1, pp. 85–89. Springer, Heidelberg (1991)Google Scholar
  5. 5.
    Zhang, R., Tao, J.: A nonlinear fuzzy neural network modeling approach using an improved genetic algorithm. IEEE Trans. Ind. Electron. 65(7), 5882–5892 (2018)CrossRefGoogle Scholar
  6. 6.
    Alfi, A., Modares, H.: System identification and control using adaptive particle swarm optimization. Appl. Math. Model. 35(3), 1210–1221 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Dub, M., Stefek, A.: Mechatronics 2013. Springer, Cham (2014)Google Scholar
  8. 8.
    Hassan, R., Cohanim, B., de Weck, O., Venter, G.: A comparison of particle swarm optimization and the genetic algorithm. In: 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, no. April, pp. 1–13 (2005)Google Scholar
  9. 9.
    Marlin, T.: Process Control. Design Processes and Control System for Dynamic Performance. McGraw Hill, New York (1995)Google Scholar
  10. 10.
    Balaguer, P., Alfaro, V., Arrieta, O.: Second order inverse response process identification from transient step response. ISA Trans. 50(2), 231–238 (2011)CrossRefGoogle Scholar
  11. 11.
    Aguilar, J., Cerrada, M.: Genetic programming-based approach for system identification. Adv. Fuzzy Syst. Evol. Comput. Artif. Intell. 329–324 (2001)Google Scholar
  12. 12.
    Carabalí, C.A., Tituaña, L., Aguilar, J., Camacho, O., Chavez, D.: Inverse response systems identification using genetic programming. In: Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics, vol. 1, no. Icinco, pp. 238–245 (2017)Google Scholar
  13. 13.
    Tang, H., Xue, S., Fan, C.: Differential evolution strategy for structural system identification. Comput. Struct. 86(21–22), 2004–2012 (2008)CrossRefGoogle Scholar
  14. 14.
    Venter, G., Sobieszczanski-Sobieski, J.: Particle swarm optimization. AIAA J. 41(8), 1583–1589 (2003)CrossRefGoogle Scholar
  15. 15.
    Aguilar, J.: The evolutionary programming in the identification of discreet events dynamic systems. IEEE Lat. Am. Trans. 5(5), 301–310 (2007)CrossRefGoogle Scholar
  16. 16.
    Garnier, H., Mensler, M., Richard, A.: Continuous-time model identification from sampled data: implementation issues and performance evaluation. Int. J. Control 76(13), 1337–1357 (2003)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Seborg, D., Edgar, T., Mellichamp, D., Doyle, F.: Process Dynamics and Control, 3rd edn. Wiley, New York (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Luis Morales
    • 1
    Email author
  • Oscar Camacho
    • 1
  • Danilo Chávez
    • 1
  • José Aguilar
    • 2
  1. 1.Dpto. de Automatización y Control IndustrialEscuela Politécnica NacionalQuitoEcuador
  2. 2.Universidad de Los AndesMéridaVenezuela

Personalised recommendations