118190 20240319081020.0 doi 10.1109/ACCESS.2022.3183746 sideral 129578 ART-2022-129578 eng Guillen, P. Universidad de Zaragoza (orcid)0000-0003-4886-9461 Deep Learning Implementation of Model Predictive Control for Multi-Output Resonant Converters 2022 Access copy available to the general public Unrestricted Flexible-surface induction cooktops rely on multi-coil structures which are powered by means of advanced resonant power converters that achieve high versatility while maintaining high efficiency and power density. The study of multi-output converters has led to cost-effective and reliable implementations even if they present complex control challenges to provide high performance. For this scenario, model predictive control arises as a modern control technique that is capable of handling multivariable problems while dealing with nonlinearities and constraints. However, these controllers are based on the computationally-demanding solution of an optimization problem, which is a challenge for high-frequency real-time implementations. In this context, deep learning presents a potent solution to approximate the optimal control policy while achieving a time-efficient evaluation, which permits an online implementation. This paper proposes and evaluates a multi-output-resonant-inverter model predictive controller and its implementation on an embedded system by means of a deep neural network. The proposal is experimentally validated by a resonant converter applied to domestic induction heating featuring a two-coil 3.6 kW architecture controlled by means of a FPGA. Author info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 3.9 2022 COMPUTER SCIENCE, INFORMATION SYSTEMS 73 / 158 = 0.462 2022 Q2 T2 TELECOMMUNICATIONS 41 / 88 = 0.466 2022 Q2 T2 ENGINEERING, ELECTRICAL & ELECTRONIC 100 / 274 = 0.365 2022 Q2 T2 0.926 2022 Computer Science (miscellaneous) 2022 Q1 Materials Science (miscellaneous) 2022 Q1 Engineering (miscellaneous) 2022 Q1 9.0 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Fiedler, F. Sarnago Andía, H. Universidad de Zaragoza (orcid)0000-0001-8399-4650 Lucia, S. Lucía Gil, O. Universidad de Zaragoza (orcid)0000-0002-1284-9007 5008 785 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Tecnología Electrónica 10 (2022), 65228 [10 pp] IEEE Access IEEE Access 2169-3536 2628031 http://zaguan.unizar.es/record/118190/files/texto_completo.pdf Versión publicada 2600358 http://zaguan.unizar.es/record/118190/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:118190 articulos driver 2024-03-18-16:03:47 ARTICLE