Master Slave LMPM Position Control Using Genetic Algorithms

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 188)

Abstract

Recently, in the era of high speed computers, nanotechnology and intelligent control; genetic algorithms belong to the essential part of this high tech world. Therefore, this paper sticks two actual topics together - linear motor and genetic algorithm. It is generally known that linear motors are maintenance free and they are able to evolve high velocity and precision which is why we made closer look on this topic. To make the linear motor more precise, genetic algorithm was applied. The GA role was to design optimal parameters for PID regulator, lead compensator and Luenberger observer to ensure the most precise positioning. Eventually, some experiments were done to demonstrate the impact of Luenberger observer and it will be also shown responses of position, velocity, force, and position error, which were gained from the experiment using GA.

Keywords

linear motor genetic algorithm master-slave control Luenberger observer 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sekaj: Evolučné výpočty a ich využitie v praxi. Iris, Bratislava (2005)Google Scholar
  2. 2.
    Ellis, G.H.: Observers in Control Systems. Academic Press (2002)Google Scholar
  3. 3.
    Mitchell, M.: Introduction to genetic algorithms. MIT Press (1998)Google Scholar
  4. 4.
    Žalman, M.: Akčné členy, STU Bratislava (2003)Google Scholar
  5. 5.
    Khater, F., Shaltout, A., Hendawi, E., Abu El-Sebah, M.: PI controller based on genetic algorithm for PMSM drive system. In: IEEE International Symposium on Industrial Electronics, ISIE 2009, July 5-8, pp. 250–255 (2009), http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5217925&isnumber=5213059, doi:10.1109/ISIE, 5217925
  6. 6.
    Solano, J., Jones, D.I.: Parameter determination for a genetic algorithm applied to robot control. In: International Conference on Control 1994, March 21-24, vol. 1, pp. 765–770 (1994), http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=327048&isnumber=7757, doi: 10.1049/cp:19940229
  7. 7.
    Wang, Y.P., Hur, D.R., Chung, H.H., Watson, N.R., Arrillaga, J., Matair, S.S.: A genetic algorithms approach to design an optimal PI controller for static VAr compensator. In: International Conference on Power System Technology, Proceedings, PowerCon 2000, vol. 3, pp. 1557–1562 (2000), http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=898203&isnumber=19429, doi:10.1109/ICPST.2000.898203
  8. 8.
    Introduction to Genetic Algorithms, Internet article, http://www.obitko.com/tutorials/genetic-algorithms/biological-background.php
  9. 9.
    Zhou, Y.-F., Song, B., Chen, X.-D.: Position/force control with a lead compensator for PMLSM drive system. Springer-Verlag London Limited (November 18, 2005)Google Scholar
  10. 10.
    Sekaj, I., Foltin, M.: Matlab toolbox – Genetické algoritmy. Konferencia Matlab 2003. Praha (2003)Google Scholar
  11. 11.
    Radičová, T., Žalman, M.: LMPM Position Control with Luenberger Observer Using Genetic Algorithms. In: ELEKTRO 2012, Rajecké Teplice (2012)Google Scholar
  12. 12.
    Radičová, T., Žalman, M.: Master-slave position servo-drive design of aircore linear motor with permanent magnets. AT&P Journal Plus (January 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Information TechnologySlovak University of Technology in BratislavaBratislavaSlovakia

Personalised recommendations