Experimental Investigation on Buck Converter Using Neuro – Fuzzy Controller

Authors

DOI:

https://doi.org/10.18201/ijisae.2019751245

Keywords:

Buck type DC-DC Converter, Neuro-Fuzzy Control, PI Control

Abstract

Buck type DC-DC converter circuit topology is non-linear due to their switched circuit structure. Conventional control systems are insufficient to control non-linear systems. Neural Networks have important abilities such as learning, optimizing and adaptability. Fuzzy logic and neural networks are used as an adaptive structure based on the fuzzy logic controller. This adaptive structure adjusts the properties of the fuzzy rules and the characteristics of the control system so that the Neuro-Fuzzy controller can be adapted to all different system conditions. In this study, experimental studies were carried out on the dSPACE experiment platform to show the dynamic performance of the Neuro-Fuzzy controller and the conventional PI controller in different system conditions (such as reference voltage tracking and output load change) of buck type DC-DC converter.

Downloads

Download data is not yet available.

References

G. Hua, C.S. Leu, Y. Jiang, and F.C.Y. Lee “ Novel Zero-Voltage-Transition PWM Converters ”, IEEE Trans. on Power Electron., vol. 9, no. 2, pp. 213-219, 1994.

A.J.Calderon,B.M.Vinagre,V.Feliu,“Fractional order control strategies for power electronic buck converters”, Signal Processing, ELSEVIER 2803–28190165-1684 , 2006.

S. Singh, D. Fulwani, ve V. Kumar, “Robust sliding-mode control of dc/dc boost converter feeding a constant power load ”,IET Power Electronics, vol. 8, no. 7, pp. 1230–1237, July 2015.

I. Yazici ve E. K. Yaylaci, “Fast ve robust voltage control of DC-DC boost converter by using fast terminal sliding mode controller,” IET Power Electronics, vol. 9, no. 1, pp. 120–125.2016.

H.Acikgoz, O.F. Kececioglu, A. Gani, C. Yildiz, M. Sekkeli, “Improved Control Configuration of PWM Rectifiers Based on Neuro Fuzzy Controller,” SpringerPlus, vol. 5, no. 1, pp. 1–19, July 2016.

N. Mohan, T.M. Unlead, W.P. Robbins, “Power Electronics,” John Wiley & Sons Ltd., England, 185-191.2002.

R.W. Erickson. “Fundamentals of Power Electronics,” New York, Chapman and Hall.1997.

K.W.E.Cheng, “Classical switched mode and resonant power converters”, Hong Kong Polytechnic University, ISBN 962-367-364-7.

M. Gökbulut, B. Dandil, C. Bal, “A Hybrid Neuro-Fuzzy Controller for Brushless DC Motors”, Lecture Notes in Computer Science, vol. 3949, pp 125-132, 2005.

R.Coteli, H.Acikgoz, F. Ucar, B. Dandil. "Design and implementation of type-2 fuzzy neural system controller for PWM rectifiers", International Journal of Hydrogen Energy, vol.42, no.32, pp.20759-20771, August 2017.

R.Coteli, H.Acikgoz, B.Dandil, S.Tuncer, "Real-time implementation of three-level inverter-based D-STATCOM using neuro-fuzzy controller," Turk J Elec Eng , & Comp Sci, vol.26, no.4, pp.2088-2103, July 2018.

H.Acikgoz,, G.Kale, O.F.Kececioglu, A.Gani, M. Sekkeli, "Performance analysis and design of robust controller for PWM rectifier," presented at the 9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, Nov.26-28 ,2015.

A.Koca, H..F. Oztop, Y. Varol, G.O.Koca, “Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey," Expert Systems with Applications.vol.38, no.7, pp.8756-8762, July 2011.

Downloads

Published

20.03.2019

How to Cite

Kececioglu, O. F., Acikgoz, H., Gani, A., & Sekkeli, M. (2019). Experimental Investigation on Buck Converter Using Neuro – Fuzzy Controller. International Journal of Intelligent Systems and Applications in Engineering, 7(1), 1–6. https://doi.org/10.18201/ijisae.2019751245

Issue

Section

Research Article