doi:10.1109/ACCESS.2022.3183746engGuillen, P.Fiedler, F.Sarnago Andía, H.Lucia, S.Lucía Gil, O.Deep Learning Implementation of Model Predictive Control for Multi-Output Resonant ConvertersART-2022-129578Flexible-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. Author2022http://zaguan.unizar.es/record/11819010.1109/ACCESS.2022.3183746http://zaguan.unizar.es/record/118190oai:zaguan.unizar.es:118190IEEE Access 10 (2022), 65228 [10 pp]by-nc-ndhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccess