Articles
Design and simulation of a neural control applied to a flyback converter for voltage regulation
Juan David Gómez-Buitrago- Nelson Leonardo Díaz-Aldana
Published 2021-07-16
Keywords
- Intelligent control,
- flyback converter,
- machine learning,
- neural network,
- voltage regulator
- Simulink,
- Simulink; effectiveness of neuronal control,
- four neurons ...More
How to Cite
López-Manchola, Óscar E., Gómez-Buitrago, J. D., Gaona-Barrera, A. E., & Díaz-Aldana, N. L. (2021). Design and simulation of a neural control applied to a flyback converter for voltage regulation. Revista UIS Ingenierías, 20(4), 111–126. https://doi.org/10.18273/revuin.v20n4-2021009
Abstract
This article explains the design and simulation of a controller based on neural networks to regulate the output voltage of a flyback converter. Neural networks are used since they do not require a mathematical model of the converter, with the advantage of a greater operating range than traditional control methods. In the training process, changes were made in the database and in the neural network architecture to get a more appropriate controller that the guaranteed line and load regulation of the converter. The functional neural controller validation was made on Simulink with the circuital model of a flyback converter, putting it to changes of output load and input voltage. The results obtained show the effectiveness of neuronal control with its ability to regulate lines in a range of 20V to 50V, load regulation between 8Ω and 12Ω, and whose architecture is made up of four neurons.
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References
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[31] J. Amrutha, A. S. Remya Ajai, “Performance analysis of Backpropagation Algorithm of Artificial Neural Networks in Verilog”, en 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2018, pp. 1547-1550, doi: 10.1109/RTEICT42901.2018.9012614.
[32] S. Basterrech, S. Mohammed, G. Rubino, M. Soliman, “Levenberg—Marquardt Training Algorithms for Random Neural Networks”, The Computer Journal, vol. 54, no. 1, pp. 125-135, 2011, doi: 10.1093/comjnl/bxp101.
[33] Z. Dlugosz, R. Dlugosz, “Nonlinear Activation Functions for Artificial Neural Networks Realized in Hardware”, en 2018 25th International Conference "Mixed Design of Integrated Circuits and System" (MIXDES), 2018, pp. 381-384, doi: 10.23919/MIXDES.2018.8436869.
[34] K. Khalil, O. Eldash, B. Dey, A. Kumar, M. Bayoumi, “A Novel Reconfigurable Hardware Architecture of Neural Network”, en 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), 2019, pp. 618-621, doi: 10.1109/MWSCAS.2019.8884809.
[2] C. M. Pappalardo, D. Guida, “Use of the adjoint method for controlling the mechanical vibrations of nonlinear systems”, Machines, vol. 6, no. 2, pp. 19, 2018, doi: 10.3390/machines6020019.
[3] J. S. Gill, D. K. Owen, M. Braylovskiy, “Apparatus, system, and method for improving the power efficiency of telecommunications devices”, U.S. Patent No 10,063,049, 28-Ago-2018.
[4] J. Schoukens, L. Ljung, “Nonlinear system identification: A user-oriented road map”, IEEE Control Syst. Mag., vol. 39, no. 6, pp. 28-99, 2019, doi: 10.1109/MCS.2019.2938121.
[5] X. Yue, B. Su, “Predictive Functional Control of Nonlinear Systems Based on Multiple LPV Models”, en 2019 Chinese Automation Congress (CAC), 2019, pp. 5210-5214, doi: 10.1109/CAC48633.2019.8997055.
[6] K. M. Tsang, W. L. Chan, y X. L. Wei, “Robust DC/DC buck converter using conditional integrator compensator”, Electron. Letters, vol. 44, no. 2, pp. 152-154, 2008.
[7] Z. Yu, J. Zeng, J. Liu, F. Luo, “Terminal sliding mode control for dual active bridge DC-DC converter with structure of voltage and current double closed loop”, en 2018 Australian & New Zealand Control Conference (ANZCC), 2018, pp. 11-15, doi: 10.1109/ANZCC.2018.8606608.
[8] M. M. Seron, J. H. Braslavsky, Sistemas no lineales. Bogotá, Colombia: Universidad Nacional del Rosario, 2000.
[9] M. Santos, “Un enfoque aplicado del control inteligente”, Rev. Iberoamericana de Automática e Informática Industrial RIAI, vol. 8, no. 4, pp. 283-296, 2011, doi: 10.1016/j.riai.2011.09.016.
[10] J. J. Montaño Moreno et. al, “Redes neuronales artificiales aplicadas al análisis de datos”, tesis doctoral, Universitat de les Illes Balears, 2017.
[11] P. Cossutta, M. P. Aguirre, A. Cao, M. A. Engelhardt, M. I. Valla, “Implementación de un algoritmo DSOGI-PLL en una FPGA para sincronización con la red de convertidores de potencia”, en 2014 IEEE Biennial. Congress of. Argentina, ARGENCON 2014, 2014, pp. 651-656, doi: 10.1109/ARGENCON.2014.6868566.
[12] M. Salimi, M. Hamedi, “Adaptive nonlinear control of the flyback switch mode power supplies”, en 2017 Int. Conf. Mech. Syst. Control Eng. ICMSC 2017, 2017, pp. 392-396, doi: 10.1109/ICMSC.2017.7959508.
[13] S. Messalti, A. Harrag, A. Loukriz, “A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation”, Renew. Sustain. Energy Rev., vol. 68, no. 1, pp. 221-233, 2017, doi: 10.1016/j.rser.2016.09.131.
[14] A. Pagni, R. Poluzzi, G. Rizzotto, M. Lo Presti, “DC/DC converters fuzzy control”, IFIS 1993 - 3rd International Conference on Industrial Fuzzy Control Intelligent Systems, 1993, pp. 14-17, doi: 10.1109/IFIS.1993.324222.
[15] M. Salimi, J. Soltani, A. Zakipour, V. Hajbani, "Sliding mode control of the DC-DC flyback converter with zero steady-state error", en 4th Annual International Power Electronics, Drive Systems and Technologies Conference, 2013, pp. 158-163, doi: 10.1109/PEDSTC.2013.6506695.
[16] R. W. Erickson, “Fundamentals of Power Eletronics”, en Fundamentals of Power Eletronics, Second., KluwerAcademic Publishers, 2001, pp. 146-168.
[17] I. Javier, A. Guacaneme, “Análisis y diseño de los conversores DC / DC básicos: el reductor, el elevador y el reductor-elevador. Consideraciones de tensión, corriente y potencia de cada elemento que compone el circuito”, tesis de grado, Universidad Distrital Francisco José de Caldas, 2018.
[18] D. Hart, Electrónica de Potencia. Madrid, España: Prentice Hall, 2001.
[19] F. Semiconductor et al., “The Flyback Converter,” Appl. Mech. Mater., vol. 734, no. September, p. 11, 2013.
[20] M. H. Rashid, “Electronica de Potencia Rashid”, en Electrónica de potencia, Pretince Hall, 1995, pp. 478-481.
[21] F. Villada, N. Muñoz, E. García, “Redes neuronales artificiales aplicadas a la predicción del precio del oro”, Información Tecnológica., vol. 27, no. 5, pp. 143-150, 2016, doi: 10.4067/S0718-07642016000500016.
[22] D. J. Matich, Redes Neuronales: Conceptos básicos y aplicaciones vol. 41. México: Universidad Tecnológica Nacional de México, 2001.
[23] S. D. Hernández, F. T. Córdova, “Simulación de una red neuronal con mapas auto-organizados (som) de kohonen”, Jóvenes en la Ciencia., vol. 3, no. 2, pp. 2650-2653, 2017.
[24] R. Salas, Redes neuronales artificiales vol. 1. Chile: Univ. Valparaíso, 2004.
[25] G. Deshpande, P. Wang, D. Rangaprakash, B. Wilamowski, “Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data”, IEEE Transactions on Cybernetic., vol. 45, no. 12, pp. 2668-2679, 2015, doi: 10.1109/TCYB.2014.2379621.
[26] F. H. Martínez, D. Gómez, M. Castiblanco, “Evaluation of a neural control with optimal architecture for a dc/dc converter”, en The ieee 2010 international power electronics conference-ecce asia-ipec-sapporo, 2010, pp. 53-57.
[27] Y. M. Buswig, A. K. B. H. Othman, N. Bin Julai, S. S. Yi, W. M. Utomo, A. J. L. M. Siang, “Voltage tracking of a multi-input interleaved buck-boost DC-DC converter using artificial neural network control”, J. Telecommunications Electron. Computer Engineering, vol. 10, no. 1-12, pp. 29-32, 2018.
[28] W. M. Utomo, A. Bakar, M. Ahmad, T. Taufik, R. Heriansyah, “Online learning neural network control of buck-boost converter”, en Proc. - 2011 8th International Conference on Information Technology: New Generations. ITNG 2011, pp. 485-489, doi: 10.1109/ITNG.2011.216.
[29] W. M. Utomo, S. S. Yi, Y. M. Y. Buswig, Z. A. Haron, A. A. Bakar, M. Z. Ahmad, “Voltage Tracking of a DC-DC Flyback Converter Using Neural Network Control”, Int. J. Power Electron. Drive Syst., vol. 2, no. 1, pp. 35-42, 2012.
[30] M. T. Hagan, H. B. Demuth, O. D. E. Jesús, “An introduction to the use of neural networks in control systems”, International Journal of Robust and Nonlinear Control: IFAC‐Affiliated Journal, vol. 12, no 11, pp. 959-985, 2002, doi: 10.1002/rnc.727.
[31] J. Amrutha, A. S. Remya Ajai, “Performance analysis of Backpropagation Algorithm of Artificial Neural Networks in Verilog”, en 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2018, pp. 1547-1550, doi: 10.1109/RTEICT42901.2018.9012614.
[32] S. Basterrech, S. Mohammed, G. Rubino, M. Soliman, “Levenberg—Marquardt Training Algorithms for Random Neural Networks”, The Computer Journal, vol. 54, no. 1, pp. 125-135, 2011, doi: 10.1093/comjnl/bxp101.
[33] Z. Dlugosz, R. Dlugosz, “Nonlinear Activation Functions for Artificial Neural Networks Realized in Hardware”, en 2018 25th International Conference "Mixed Design of Integrated Circuits and System" (MIXDES), 2018, pp. 381-384, doi: 10.23919/MIXDES.2018.8436869.
[34] K. Khalil, O. Eldash, B. Dey, A. Kumar, M. Bayoumi, “A Novel Reconfigurable Hardware Architecture of Neural Network”, en 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), 2019, pp. 618-621, doi: 10.1109/MWSCAS.2019.8884809.