132374 20250116144524.0 doi 10.1109/TII.2020.2969729 sideral 121239 ART-2021-121239 eng Lucia, S. Deep Learning-Based Model Predictive Control for Resonant Power Converters 2021 Access copy available to the general public Unrestricted Resonant power converters offer improved levels of efficiency and power density. In order to implement such systems, advanced control techniques are required to take the most of the power converter. In this context, model predictive control arises as a powerful tool that is able to consider nonlinearities and constraints, but it requires the solution of complex optimization problems or strong simplifying assumptions that hinder its application in real situations. Motivated by recent theoretical advances in the field of deep learning, this article proposes to learn, offline, the optimal control policy defined by a complex model predictive formulation using deep neural networks so that the online use of the learned controller requires only the evaluation of a neural network. The obtained learned controller can be executed very rapidly on embedded hardware. We show the potential of the presented approach on a hardware-in-the-loop setup of an field-programmable gate array-controlled resonant power converter. info:eu-repo/grantAgreement/ES/DGA-FSE/LMP106-18 info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/RTC-2017-5965-6 info:eu-repo/grantAgreement/ES/MINECO/TEC2016-78358-R info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 11.648 2021 AUTOMATION & CONTROL SYSTEMS 3 / 65 = 0.046 2021 Q1 T1 ENGINEERING, INDUSTRIAL 2 / 50 = 0.04 2021 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 4 / 112 = 0.036 2021 Q1 T1 4.333 2021 Computer Science Applications 2021 Q1 Information Systems 2021 Q1 Control and Systems Engineering 2021 Q1 21.3 2021 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Navarro, D. Universidad de Zaragoza (orcid)0000-0002-0795-8743 Karg, B. Sarnago, H. Universidad de Zaragoza (orcid)0000-0001-8399-4650 Lucia, O. Universidad de Zaragoza (orcid)0000-0002-1284-9007 5008 785 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Tecnología Electrónica 17, 1 (2021), 409-420 IEEE Trans. Ind. Inform. IEEE Transactions on Industrial Informatics 1551-3203 1343377 http://zaguan.unizar.es/record/132374/files/texto_completo.pdf Postprint 3084216 http://zaguan.unizar.es/record/132374/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:132374 articulos driver 2025-01-16-14:42:52 ARTICLE