000132374 001__ 132374
000132374 005__ 20250116144524.0
000132374 0247_ $$2doi$$a10.1109/TII.2020.2969729
000132374 0248_ $$2sideral$$a121239
000132374 037__ $$aART-2021-121239
000132374 041__ $$aeng
000132374 100__ $$aLucia, S.
000132374 245__ $$aDeep Learning-Based Model Predictive Control for Resonant Power Converters
000132374 260__ $$c2021
000132374 5060_ $$aAccess copy available to the general public$$fUnrestricted
000132374 5203_ $$aResonant 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.
000132374 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/LMP106-18$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/RTC-2017-5965-6$$9info:eu-repo/grantAgreement/ES/MINECO/TEC2016-78358-R
000132374 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000132374 590__ $$a11.648$$b2021
000132374 591__ $$aAUTOMATION & CONTROL SYSTEMS$$b3 / 65 = 0.046$$c2021$$dQ1$$eT1
000132374 591__ $$aENGINEERING, INDUSTRIAL$$b2 / 50 = 0.04$$c2021$$dQ1$$eT1
000132374 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b4 / 112 = 0.036$$c2021$$dQ1$$eT1
000132374 592__ $$a4.333$$b2021
000132374 593__ $$aComputer Science Applications$$c2021$$dQ1
000132374 593__ $$aInformation Systems$$c2021$$dQ1
000132374 593__ $$aControl and Systems Engineering$$c2021$$dQ1
000132374 594__ $$a21.3$$b2021
000132374 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000132374 700__ $$0(orcid)0000-0002-0795-8743$$aNavarro, D.$$uUniversidad de Zaragoza
000132374 700__ $$aKarg, B.
000132374 700__ $$0(orcid)0000-0001-8399-4650$$aSarnago, H.$$uUniversidad de Zaragoza
000132374 700__ $$0(orcid)0000-0002-1284-9007$$aLucia, O.$$uUniversidad de Zaragoza
000132374 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000132374 773__ $$g17, 1 (2021), 409-420$$pIEEE Trans. Ind. Inform.$$tIEEE Transactions on Industrial Informatics$$x1551-3203
000132374 8564_ $$s1343377$$uhttps://zaguan.unizar.es/record/132374/files/texto_completo.pdf$$yPostprint
000132374 8564_ $$s3084216$$uhttps://zaguan.unizar.es/record/132374/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000132374 909CO $$ooai:zaguan.unizar.es:132374$$particulos$$pdriver
000132374 951__ $$a2025-01-16-14:42:52
000132374 980__ $$aARTICLE