{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:42:37Z","timestamp":1760060557257,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research of a system for active and optimal management of electrical energy using battery storage system","award":["APVV-22-0330","Vega 1\/0314\/24"],"award-info":[{"award-number":["APVV-22-0330","Vega 1\/0314\/24"]}]},{"name":"Research of a system for active management of electrical energy using battery storage system","award":["APVV-22-0330","Vega 1\/0314\/24"],"award-info":[{"award-number":["APVV-22-0330","Vega 1\/0314\/24"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This article presents a neural network-based control method for maintaining the required output voltage of a synchronous buck converter. The goal was to replace a traditional PID controller with a neural network that calculates the duty cycle based on real-time data. Several versions of the neural network were tested. The final version, which included the input voltage, reference, and output current as inputs and compensated for dead time, was successfully validated on real hardware. It was able to respond to changes in load and input voltage within a limited operating range.<\/jats:p>","DOI":"10.3390\/a18090555","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T14:16:55Z","timestamp":1756822615000},"page":"555","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Output Voltage Control of a Synchronous Buck DC\/DC Converter Using Artificial Neural Networks"],"prefix":"10.3390","volume":"18","author":[{"given":"Juraj","family":"\u0160imko","sequence":"first","affiliation":[{"name":"Department of Mechatronics and Electronics, Faculty of Electrical Engineering and Information Technologies, University of \u017dilina, 010 01 \u017dilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0894-7090","authenticated-orcid":false,"given":"Michal","family":"Pra\u017eenica","sequence":"additional","affiliation":[{"name":"Department of Mechatronics and Electronics, Faculty of Electrical Engineering and Information Technologies, University of \u017dilina, 010 01 \u017dilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roman","family":"Ko\u0148arik","sequence":"additional","affiliation":[{"name":"Department of Mechatronics and Electronics, Faculty of Electrical Engineering and Information Technologies, University of \u017dilina, 010 01 \u017dilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7294-0438","authenticated-orcid":false,"given":"Slavom\u00edr","family":"Ka\u0161\u010d\u00e1k","sequence":"additional","affiliation":[{"name":"Department of Mechatronics and Electronics, Faculty of Electrical Engineering and Information Technologies, University of \u017dilina, 010 01 \u017dilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5386-2243","authenticated-orcid":false,"given":"Peter","family":"Kl\u010do","sequence":"additional","affiliation":[{"name":"Department of Mechatronics and Electronics, Faculty of Electrical Engineering and Information Technologies, University of \u017dilina, 010 01 \u017dilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"120362","DOI":"10.1016\/j.eswa.2023.120362","article-title":"Adaptive neural network control of DC-DC power converter","volume":"229","author":"Nizami","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_2","first-page":"012019","article-title":"Application of RBF neural network PID control on buck DC-DC converter","volume":"2918","author":"Pan","year":"2024","journal-title":"J. Phys."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Banerjee, S., Chandwani, A., and Mallik, A. (2020, January 16\u201319). Artificial Neural Network based Direct Inverse Control for a Novel 48V-1V DC\/DC Converter. Proceedings of the 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Jaipur, India.","DOI":"10.1109\/PEDES49360.2020.9379661"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e27405","DOI":"10.1016\/j.heliyon.2024.e27405","article-title":"Deep learnin based buck-boost converter for PV modules","volume":"10","author":"Muhammad","year":"2024","journal-title":"Heliyon"},{"key":"ref_5","first-page":"35","article-title":"Voltage Tracking of a DC-DC Flyback Converter Using Neural Network Control","volume":"2","author":"Utomo","year":"2012","journal-title":"Int. J. Power Electron. Drive Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gaied, H., Aymen, F., Kraiem, H., El-Bayeh, C.Z., Said, Y., and Almalki, M.M. (2024). Three phase bidirectional DC-DC converters based neural network controller for renewable energy sources. Front. Energy Res., 12.","DOI":"10.3389\/fenrg.2024.1391310"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1109\/TCSI.2021.3053468","article-title":"Control of a Buck DC\/DC Converter Using Approximate Dynamic Programming and Artificial Neural Networks","volume":"68","author":"Dong","year":"2021","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_8","first-page":"1","article-title":"Neural Network Control of Switch Mode Dc Dc Converter","volume":"2","author":"Rahman","year":"2019","journal-title":"Int. J. Mod. Res. Eng. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Saadatmand, S., Shamsi, P., and Ferdowsi, M. (2020, January 6\u20137). The Voltage Regulation of a Buck Converter Using a Neural Network Predictive Controller. Proceedings of the 2020 IEEE Texas Power and Energy Conference (TPCE), College Station, TX, USA.","DOI":"10.1109\/TPEC48276.2020.9042588"},{"key":"ref_10","first-page":"1273","article-title":"Neural Network Based Solar-Wind Energy Using Buck Boost-Sepic Converter","volume":"12","author":"Ramesh","year":"2016","journal-title":"Glob. J. Pure Appl. Math."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Khan, H.S., Mohamed, I.S., Kauhaniemi, K., and Liu, L. (2021, January 8\u201310). Artificial Neural Network-Based Voltage Control of DC\/DC Converter for DC Microgrid Applications. Proceedings of the 6th IEEE Workshop on the Electronic Grid (eGRID), New Orleans, LA, USA.","DOI":"10.1109\/eGRID52793.2021.9662132"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6815","DOI":"10.1007\/s00202-024-02882-y","article-title":"Control of a DC-DC buck converter using adaptive neural network","volume":"107","author":"Quan","year":"2024","journal-title":"Electr. Eng."},{"key":"ref_13","unstructured":"Liu, Z., and Yu, W. (2024). Design and Implementation of DC-DC Buck Converter based on Deep Neural Sliding Mode Control. Electr. Eng. Syst. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bote-Vazquez, M.Y., Ramirez-Hernandez, J., Hernandez-Gonzalez, L., Delgado-Pi\u00f1a, E.D., and Juarez-Sandoval, O.U. (2022, January 9\u201311). Artificial Neural Network-Based Voltage Control in a DC-DC Converter using a Predictive Model. Proceedings of the 2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), Ixtapa, Mexico.","DOI":"10.1109\/ROPEC55836.2022.10018649"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"97","DOI":"10.2507\/IJSIMM11(2)4.210","article-title":"Modeling and control of tinning line entry section using neural networks","volume":"11","author":"Zilkova","year":"2012","journal-title":"Int. J. Simul. Model."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Girovsky, P., Timko, J., Zilkova, J., and Fedak, V. (2010, January 6\u20138). Neural estimators for shaft sensorless FOC control of induction motor. Proceedings of the 14th International Power Electronics and Motion Control Conference EPE-PEMC, Ohrid, Macedonia.","DOI":"10.1109\/EPEPEMC.2010.5606907"},{"key":"ref_17","unstructured":"(2024, October 04). What Is a Recurrent Neural Network?. Available online: https:\/\/www.ibm.com\/topics\/recurrent-neural-networks."},{"key":"ref_18","unstructured":"(2021, September 15). The Basics of Modular Neural Networks. Available online: https:\/\/www.analyticssteps.com\/blogs\/basics-modular-neural-networks."},{"key":"ref_19","unstructured":"Nielsen, M.A. (2015). Neural Networks and Deep Learning, Determination Press."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rojas, R. (1996). Neural Networks: A Systematic Introduction, Springer.","DOI":"10.1007\/978-3-642-61068-4"},{"key":"ref_21","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/9\/555\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:38:12Z","timestamp":1760035092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/9\/555"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,2]]},"references-count":21,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["a18090555"],"URL":"https:\/\/doi.org\/10.3390\/a18090555","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2025,9,2]]}}}