Skip to main content
SpringerLink
Log in

Biogeography Optimization Algorithm for DC Motor PID Control

  • Conference paper
  • First Online:
Advances in Swarm and Computational Intelligence (ICSI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9140))

Included in the following conference series:

  • 1731 Accesses

Abstract

Biogeography optimization algorithm (BBO) is a new optimization algorithm based on biogeography. Unique migration pattern of BBO makes good habitat feature information can be widely distributed among multiple habitats, showing a diversity of solutions. It is applied to the DC motor PID control problems and compared with genetic algorithms (GA), differential evolution (DE), particle swarm optimization (PSO). Experimental results show that BBO has the ability of searching optimal solution in a small local neighborhood space. The output of PID control system of DC motor optimized under BBO has no overshoot, no steady-state error and has the shortest system dynamic response time.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Simon, D.: Biogeography based optimization. IEEE Transaction on Evolutionary Computation. 12, 702–713 (2008)

    Article  Google Scholar 

  2. Xu, Z.D., Mo, H.W.: Disturbance multi-objective biogeography optimization algorithm. Control and Decision. 29(2), 231–235 (2014)

    MathSciNet  Google Scholar 

  3. Xu, Z.D., Mo, H.W.: Biogeography information optimization algorithm to improve the operator’s migration. Pattern Recognition and Artificial Intelligence 25(3), 544–548 (2012)

    Google Scholar 

  4. Sun, J., Gao, Y.H., Wang, C.: Biogeography based optimization algorithm for reactive power optimization. Nanchang University (Engineering & Technology) 35(4), 380–384, 391 (2013)

    Google Scholar 

  5. Mo, H.W., Li, Z.Z.: Bio-geography based differential evolution for robot path planning. In: 2012 IEEE International Conference on Information and Automation, ICIA, pp. 1–6 (2012)

    Google Scholar 

  6. Panchal, V.K., Singh, P.: Biogeography based satellite image classification. International Journal of Computer Science and Information Security. 6(2), 269–274 (2009)

    Google Scholar 

  7. Ashrafinia, S., Naeem, M., Lee, D.C.: Biogeography based optimization algorithm for computational efficient symbol detection in multi-device STBC-MIMO systems. Master Thesis, Sharif University of Technology (2007)

    Google Scholar 

  8. Lee, B.: Biogeography based optimization algorithm for image segmentation technologies and applications. Master Thesis, Harbin Engineering University (2013)

    Google Scholar 

  9. Zhao, Y.J.: Study biogeography neural network fault diagnosis method based on optimization algorithms. Northeast Petroleum University. Master Thesis (2013)

    Google Scholar 

  10. Ruan, Y., Chen, B.S.: Electric drive automatic control system : Motion control systems, 4th edn. Mechanical Industry Press, Beijing (2010)

    Google Scholar 

  11. Ru, Z.X., Zhang, Z.L., Qi, Y.C.: Direct identification of DC motor model parameters. Computer Simulation 23(6), 113–115 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Automation College, Harbin Engineering University, Harbin, 150001, China

    Hongwei Mo

  2. Engineering Training Center, Harbin Engineering University, Harbin, 150001, China

    Lifang Xu

Authors
  1. Hongwei Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lifang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongwei Mo .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Universidade de Pernambuco, Recife, Brazil

    Fernando Buarque

  4. Instituto Politécnico Nacional, Mexico, Mexico

    Alexander Gelbukh

  5. Indian Statistical Institute, Kolkata, India

    Swagatam Das

  6. University of Pretoria, Pretoria, South Africa

    Andries Engelbrecht

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Mo, H., Xu, L. (2015). Biogeography Optimization Algorithm for DC Motor PID Control. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9140. Springer, Cham. https://doi.org/10.1007/978-3-319-20466-6_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-20466-6_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20465-9

  • Online ISBN: 978-3-319-20466-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation