Skip to main content
SpringerLink
Log in

Chaotic flower pollination algorithm based optimal PID controller design for a buck converter

  • Published:
Analog Integrated Circuits and Signal Processing Aims and scope Submit manuscript

Abstract

This paper presents a solution based on optimal PID coefficients including anti-wind up for buck converter presents using meta-heuristic algorithm and chaos theory. A hybrid algorithm is called chaotic based flower pollination algorithm is provided by combining flower pollination algorithm and chaos theory with different maps. Five different choatic maps are used in the aim of increasing the efficacy and efficiency of flower pollination algorithm. In order to adjust the parameters combined in the flower pollination algorithm, random number sequences from Henon, Logistic, Sine, Tent and Tinkerbell chaotic maps are used. The success of the developed algorithm is evaluated by solving 23 different benchmark problems which are very common in the literature. The results of comparative experiments show that henon chaotic map based flower pollination algorithm is sufficiently effective in solving these benchmark problems. Thus desinging optimal PID with anti-windup for buck converter, henon chaotic map based flower pollination algorithm is used. Also henon chaotic map based flower pollination algorithm has better performance than firelfy algorithm and whale optimization algorithm existing in literature.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Bao, B., Zhang, X., Bao, H., Wu, P., Wu, Z., & Chen, M. (2019). Dynamical effects of memristive load on peak current mode buck-boost switching converter. Chaos, Solitons & Fractals, 122, 69–79.

    Article  MathSciNet  Google Scholar 

  2. Alkrunz, M., et al. (2016). Design of discrete time controllers for the dc-dc boost converter. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 20(1), 75–82.

    Article  Google Scholar 

  3. Yang, X.-S. (2010). Nature-inspired metaheuristic algorithms. United Kingdom: Luniver press.

    Google Scholar 

  4. X.-S. Yang (2013). Metaheuristic optimization: Nature-inspired algorithms and applications. In: Artificial intelligence, evolutionary computing and metaheuristics, Springer, Berlin pp. 405–420.

  5. Talatahari, S., Azar, B. F., Sheikholeslami, R., & Gandomi, A. (2012). Imperialist competitive algorithm combined with chaos for global optimization. Communications in Nonlinear Science and Numerical Simulation, 17(3), 1312–1319.

    Article  MathSciNet  Google Scholar 

  6. Gandomi, A. H., & Yang, X.-S. (2014). Chaotic bat algorithm. Journal of Computational Science, 5(2), 224–232.

    Article  MathSciNet  Google Scholar 

  7. Batık, Z. G., Cimen, M. E., Karayel, D., & Boz, A. F. (2019). The chaos-based whale optimization algorithms global optimization. Chaos Theory and Applications, 1(1), 51–63.

    Google Scholar 

  8. Çimen, M. E., & Boz, A. F. (2017). PSO, CS ve FA algoritmalarıyla ortak emiterli bjt’li yükselteç tasarımı. Cumhuriyet Üniversitesi Fen-Edebiyat Fakültesi Fen Bilimleri Dergisi, 38(1), 119–130.

  9. Çimen, M. E., & Boz, A. F. (2019). İkinci dereceden ölü zamanlı ve geri tepmeli sistem parametrelerinin, röle testi ve pso, cs, fa algoritmaları ile belirlenmesi. Journal of the Faculty of Engineering & Architecture of Gazi University, 34(1), 461.

    Google Scholar 

  10. Liao, G.-C., & Tsao, T.-P. (2006). Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting. IEEE Transactions on Evolutionary Computation, 10(3), 330–340.

    Article  Google Scholar 

  11. dos Santos Coelho, L., & de Andrade Bernert, D. L. (2009). An improved harmony search algorithm for synchronization of discrete-time chaotic systems. Chaos, Solitons & Fractals, 41(5), 2526–2532.

    Article  Google Scholar 

  12. Pluhacek, M., Senkerik, R., Zelinka, I., & Davendra, D. (2013) Chaos pso algorithm driven alternately by two different chaotic maps-an initial study. In: (2013) IEEE congress on evolutionary computation. (pp. 2444–2449). IEEE

  13. Shayeghi, H., & Ghasemi, A. (2014). A modified artificial bee colony based on chaos theory for solving non-convex emission/economic dispatch. Energy Conversion and Management, 79, 344–354.

    Article  Google Scholar 

  14. Gong, W., & Wang, S.(2009). Chaos ant colony optimization and application. In: 2009 Fourth international conference on internet computing for science and engineering, IEEE, pp. 301–303.

  15. Gandomi, A. H., Yang, X.-S., Talatahari, S., & Alavi, A. H. (2013). Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, 18(1), 89–98.

    Article  MathSciNet  Google Scholar 

  16. Bhowate, A., & Deogade, S. (2015). Comparison of pid tuning techniques for closed loop controller of dc-dc boost converter. International Journal of Advances in Engineering & Technology, 8(1), 2064.

    Google Scholar 

  17. Sundareswaran, K., Kuruvinashetti, K., Hariprasad, B., Sankar, P., Nayak, P., & Vigneshkumar, V. (2014). Optimization of dual input buck converter control through genetic algorithm. IFAC Proceedings Volumes, 47(1), 142–146.

    Article  Google Scholar 

  18. Seo, K., & Choi, H.-H. (2012). Simple fuzzy pid controllers for dc-dc converters. Journal of Electrical Engineering and Technology, 7(5), 724–729.

    Article  Google Scholar 

  19. Swathy, M. K., Jantre, M. S., Jadhav, M. Y., Labde, M. S. M., & Kadam, M. P. (2018).Design and hardware implementation of closed loop buck converter using fuzzy logic controller. In: 2018 Second international conference on electronics, communication and aerospace technology (ICECA), IEEE, 2018, pp. 175–180.

  20. Hekimoğu, B., Ekinci, S., & Kaya, S. (2019). Balina optimizasyon algoritması kullanılarak dada düşürücü dönüştürücünün optimum pid denetleyici tasarımı. In: 2018 International conference on artificial intelligence and data processing (IDAP)

  21. Mühürcü, G., Köse, E., Muhurcu, A., & Özdemir, M. (2018). Pi parameter optimization by fairly algorithm for optimal controlling of a buck converter’s output state variable. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(5), 1267–1273.

    MATH  Google Scholar 

  22. Altinoz, O., & Erdem, H. (2010). Evaluation function comparison of particle swarm optimization for buck converter. In: SPEEDAM 2010, IEEE, pp. 798–802.

  23. Gupta, A. K., Kumar, D., Reddy, B. M., & Samuel, P. (2017). BBBC based optimization of PI controller parameters for buck converter. In: 2017 Innovations in power and advanced computing technologies (i-PACT) (pp. 1–6). IEEE.

  24. Skvarenina, T. L. (2018). The power electronics handbook. Boca Raton: CRC Press.

    Book  Google Scholar 

  25. Erdem, Z. (2009). Maximum power point tracker. Master Thesis, Electrical and Electronics Engineering, Sakarya University.

  26. Erdem, Z. (2014). Developing MDA formulations in PID design and DC-DC boost converter control for MPPT. Phd Thesis, Electrical and Electronics Engineering, Sakarya University.

  27. Kökçam, E. (2018). Optimal control of output voltage of buck converter in the Matlab-simulink environment. Master Thesis, Electrical and Electronics Engineering, Sakarya University.

  28. Yang, X.-S. (2012). Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation, Springer, pp. 240–249.

  29. Yang, X.-S., Karamanoglu, M., & He, X. (2014). Flower pollination algorithm: A novel approach for multiobjective optimization. Engineering Optimization, 46(9), 1222–1237.

    Article  MathSciNet  Google Scholar 

  30. He, X.-S., Fan, Q.-W., Karamanoglu, M., Yang, X.-S. (2019) Comparison of constraint-handling techniques for metaheuristic optimization. In: International conference on computational science, Springer, pp. 357–366.

Download references

Author information

Authors and Affiliations

  1. Electrical and Electronic Engineering Department, Sakarya University of Applied Sciences, 54050, Serdivan, Sakarya, Turkey

    Murat Erhan Çimen & Ali Fuat Boz

  2. Computer Engineering Department, Sakarya University of Applied Sciences, 54050, Serdivan, Sakarya, Turkey

    Zeynep B. Garip

Authors
  1. Murat Erhan Çimen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zeynep B. Garip
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Fuat Boz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zeynep B. Garip.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Çimen, M.E., Garip, Z.B. & Boz, A.F. Chaotic flower pollination algorithm based optimal PID controller design for a buck converter. Analog Integr Circ Sig Process 107, 281–298 (2021). https://doi.org/10.1007/s10470-020-01751-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10470-020-01751-5

Keywords

Search

Navigation