000118190 001__ 118190
000118190 005__ 20240319081020.0
000118190 0247_ $$2doi$$a10.1109/ACCESS.2022.3183746
000118190 0248_ $$2sideral$$a129578
000118190 037__ $$aART-2022-129578
000118190 041__ $$aeng
000118190 100__ $$0(orcid)0000-0003-4886-9461$$aGuillen, P.$$uUniversidad de Zaragoza
000118190 245__ $$aDeep Learning Implementation of Model Predictive Control for Multi-Output Resonant Converters
000118190 260__ $$c2022
000118190 5060_ $$aAccess copy available to the general public$$fUnrestricted
000118190 5203_ $$aFlexible-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
000118190 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000118190 590__ $$a3.9$$b2022
000118190 592__ $$a0.926$$b2022
000118190 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b73 / 158 = 0.462$$c2022$$dQ2$$eT2
000118190 591__ $$aTELECOMMUNICATIONS$$b41 / 88 = 0.466$$c2022$$dQ2$$eT2
000118190 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b100 / 274 = 0.365$$c2022$$dQ2$$eT2
000118190 593__ $$aComputer Science (miscellaneous)$$c2022$$dQ1
000118190 593__ $$aMaterials Science (miscellaneous)$$c2022$$dQ1
000118190 593__ $$aEngineering (miscellaneous)$$c2022$$dQ1
000118190 594__ $$a9.0$$b2022
000118190 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000118190 700__ $$aFiedler, F.
000118190 700__ $$0(orcid)0000-0001-8399-4650$$aSarnago Andía, H.$$uUniversidad de Zaragoza
000118190 700__ $$aLucia, S.
000118190 700__ $$0(orcid)0000-0002-1284-9007$$aLucía Gil, O.$$uUniversidad de Zaragoza
000118190 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000118190 773__ $$g10 (2022), 65228 [10 pp]$$pIEEE Access$$tIEEE Access$$x2169-3536
000118190 8564_ $$s2628031$$uhttps://zaguan.unizar.es/record/118190/files/texto_completo.pdf$$yVersión publicada
000118190 8564_ $$s2600358$$uhttps://zaguan.unizar.es/record/118190/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000118190 909CO $$ooai:zaguan.unizar.es:118190$$particulos$$pdriver
000118190 951__ $$a2024-03-18-16:03:47
000118190 980__ $$aARTICLE