PID Tuning for LOS Stabilization System Controller Based on BBO Algorithm

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 337)

Abstract

This paper is a discussion on a novel controller tuning method for the PID-based BBO method. The proposed approach had superior characteristics, including stable convergence characteristic, easy implementation, and good computational efficiency. From experimental results, the designed PID controllers-based BBO have less overshoot and short response time compared to that of the classical method. Therefore, BBO approach is taken as a better solution to improve the performance of the PID controller.

Keywords

PID controller Biogeography-based optimization Line-of-sight 

References

  1. 1.
    Asuntha A, Srinivasan A (2014) Intelligent PID controller tuning using PSO for linear system. Int J Innovative Sci Eng Technol 1(5):166–174Google Scholar
  2. 2.
    Visioli A (2001) Tuning of PID controllers with fuzzy logic. Proc Inst Elect Eng Contr Theory Appl 148(1):1–8Google Scholar
  3. 3.
    Seng TL, Khalid MB, Yusof R (1999) Tuning of a neuro-fuzzy controller by genetic algorithm. IEEE Trans Syst Man Cybern B 29:226–236Google Scholar
  4. 4.
    Solihin MI, Tack LF Kean ML (2011) Tuning of PID controller using particle swarm optimization (PSO). In: Proceeding of the international conference on advanced science, Malaysia, pp 458–461Google Scholar
  5. 5.
    Krohling RA, Rey JP (2001) Design of optimal disturbance rejection PID controllers using genetic algorithm. IEEE Trans Evol Comput 5:78–82Google Scholar
  6. 6.
    Mitsukura Y, Yamamoto T, Kaneda M (1999) A design of self-tuning PID controllers using a genetic algorithm. In: Proceedings of American control conference, San Diego, CA, pp 1361–1365Google Scholar
  7. 7.
    Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 6(12):702–713CrossRefGoogle Scholar
  8. 8.
    Simon D, Ergezer M, Dawei D (2009) Markov analysis of biogeography-based optimization. http://academic.csuohio.edu/simond/bbo
  9. 9.
    Simon D, Ergezer M, Dawei D (2009) Markov models for biogeography-based optimization and genetic algorithms with global uniform recombination. http://academic.csuohio.edu/simond/bbo/markov/MarkovJournal.pdf
  10. 10.
    Simon D (2009) A probabilistic analysis of a simplified biogeography-based optimization algorithm. http://academic.csuohio.edu/simond/bbo/simplified/bbosimplified.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Auto-control Engineering DepartmentArmed Force Engineering InstituteBeijingChina

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