Skip to main content
SpringerLink
Log in

A Hybrid Differential Artificial Bee Colony Algorithm based tuning of fractional order controller for Permanent Magnet Synchronous Motor drive

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

In this paper a novel Hybrid Differential Artificial Bee Colony Algorithm (HDABCA) has been proposed for designing a fractional order proportional-integral (FO-PI) speed controller in a Permanent Magnet Synchronous Motor (PMSM) drive. FO-PI controllers’ parameters involve proportionality constant, integral constant and integral order, and hence its design is more complex than that of the usual Integral-order proportional-integral controller. To overcome this complexity in designing, we had used the proposed hybrid algorithm, such that all the design specifications of the motor are satisfied. In order to digitally realize the FO-PI controller, an Oustaloup approximation method has been used. Simulations and comparisons of proposed HDABCA with conventional methods and also other state-of-art methods demonstrate the competence of the proposed approach, especially for actuating fractional order controller for integer order plants.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Bose BK (1993) Power electronics and motion control-technology status and recent trends. IEEE Trans Ind Appl 29(5):902–909

    Article  Google Scholar 

  2. Vas P (1998) Sensor less Vector and Direct Torque Control, 1st edn, Oxford University Press, Oxford

  3. Leonhard W (1986) Microcomputer control of high dynamic performance ac drives—a survey. Automatica 22(1):1–19

    Article  MATH  Google Scholar 

  4. Slotine JJ, Li W (1991). Applied nonlinear control. Pearson Education, Englewood Cliffs

  5. Astrom KJ, Hugglund T (2001) The future of PID control. Control Eng Pract 9:1163–1175

    Article  Google Scholar 

  6. Podlubny I (1994) Fractional-order systems and fractional-order controllers. The Academy of Sciences Institute of Experimental Physics, UEF-03-94, Kosice, Slovak Republic

  7. Goldberg D (1986) Genetic algorithms in search optimization and machine learning. Addision Wesley Publishing Company, Massachutes

  8. Jatoth RK, Rajasekhar A (2010) Adaptive bacterial foraging optimization based tuning of optimal PI speed controller for PMSM drive. International Conference on Contemporary Computing, IC3-2010, pp 588–599

  9. Lin C-M, Li M-C, Ting A-B, lin M-H (2011) A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm. Int J Mach Learn Cybern 2(4):225–234

    Article  Google Scholar 

  10. Wang X, He Y, Dong L, Zhao H (2011) Particle Swarm optimization for determining fuzzy measures from data. Inf Sci 181(19):4230–4252

    Article  MATH  Google Scholar 

  11. Price K, Storn R (1995) Differential evolution-a simple and efficient adaptive scheme for global optimization over continous spaces, Technical Report, International Computer Science Institue, Berkley

  12. Karaboga D (2005) An idea based on bee swarm for numerical optimization. Technical Report, TR-06, Erciyes University Engineering Faculty, Computer Engineering Department

  13. Das S, Biswas A, Abraham A, Dasgupta S (2009) Design of fractional order PI λDμ controllers with an improved differential evolution. Eng Appl Artif Intell 22(2):343–350

    Article  MathSciNet  Google Scholar 

  14. Sabat S, Udgata S, Abraham A (2010) Artificial Bee Colony Algorithm for small signal model parameter extraction of MESFET. Eng Appl Artif Intell 23(5):689–694

    Article  Google Scholar 

  15. Rajasekhar A, Das S, Suganthan P (2012) Design of fractional order controller for a servohydraulic positioning system with micro Artificial Bee Colony Algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp 1–8

  16. Sebastian T, Slemon GR (1986) Transient modeling and performance of variable—speed permanent-magnet motors. IEEE Trans Ind Appl 25(1):101–106

    Article  Google Scholar 

  17. Pillay P, Krishnan R (1989) Modeling, simulation, and analysis of permanent-magnet motor drives, part i: the permanent-magnet synchronous motor drive. IEEE Trans Ind Appl 25(2):265–273

    Article  Google Scholar 

  18. Zamani M, Ghartemani MK, Sadati N, Parniani M (2009) Design of a fractional order PID controller for an AVR using particle swarm optimization. Control Eng Pract 17(12):1380–1387

    Article  Google Scholar 

  19. Oldham KB, Spanier J (1974) The fractional calculus. Academic Press, New York

  20. Petras I (1999) The fractional order controllers: methods for their synthesis and application. J Electr Eng 50(910):284–288.

    Google Scholar 

  21. Podlubny I (1999) Fractional-order systems and PI λ D μ -controllers. IEEE Trans Autom Control 44(1):208–214

    Article  MATH  MathSciNet  Google Scholar 

  22. Chen Y., Xue D., Dou H. (2004). Fractional calculus and biomimetic control. In: Proceedings of the First IEEE International Conference on Robotics and Biomimetics (RoBio04), robio2004, pp 347, Shengyang, China

  23. Krohling R A, Rey J P (2001) Design of optimal disturbance rejection PID controller using genetic algorithms. IEEE Trans Evol Comput 5(1):78–82

    Article  Google Scholar 

  24. Price K, Storn R, Lampinen J (2005) Differential evolution— a practical approach to global optimization. Springer, New York

  25. Lampinen J, Zelinka I (2000) On stagnation of the differential evolution algorithm. In: Proceedings of the MENDEL, 6th international mendel conference on soft computing, Brno, Czech Republic, pp 76–83

  26. Dasguta S, Das S, Abraham A, Biswas A (2009) Adaptive computational chemotaxis in bacterial foraging optimization: an analysis. IEEE Trans Evol Comput 13(4):919–941

    Article  Google Scholar 

  27. Rajasekhar A, Pant M, Abraham A (2011) A Hybrid Differential Artificial Bee Algorithm based tuning of fractional order controller for PMSM drive. Third World Congress on Nature and Biologically Inspired Computing, NaBIC-2011, pp 19–21

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, National Institute of Technology, Warangal, 506004, Andhra Pradesh, India

    Anguluri Rajasekhar

  2. Department of Paper and Pulp Technology, Indian Institute of Technology, Roorkee, 247667, India

    Millie Pant

  3. Faculty of Computer Science and Electrical Engineering, VSB---Technical University of Ostrava, Ostrava, Czech Republic

    Ajith Abraham

  4. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

Authors
  1. Anguluri Rajasekhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ajith Abraham
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Millie Pant
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ajith Abraham.

Additional information

Part of this work was published in the Proceedings of the Third World Congress on Nature and Biologically Inspired Computing (NABIC-2011) [27].

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rajasekhar, A., Abraham, A. & Pant, M. A Hybrid Differential Artificial Bee Colony Algorithm based tuning of fractional order controller for Permanent Magnet Synchronous Motor drive. Int. J. Mach. Learn. & Cyber. 5, 327–337 (2014). https://doi.org/10.1007/s13042-012-0136-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-012-0136-2

Keywords

Search

Navigation