Deep Learning Implementation of Model Predictive Control for Multi-Output Resonant Converters

Guillen, P. (Universidad de Zaragoza) ; Fiedler, F. ; Sarnago Andía, H. (Universidad de Zaragoza) ; Lucia, S. ; Lucía Gil, O. (Universidad de Zaragoza)

Resumen: 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

Idioma: Inglés

DOI: 10.1109/ACCESS.2022.3183746

Año: 2022

Publicado en: IEEE Access 10 (2022), 65228 [10 pp]
ISSN: 2169-3536

Factor impacto JCR: 3.9 (2022)

Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 73 / 158 = 0.462 (2022) - Q2 - T2
Categ. JCR: TELECOMMUNICATIONS rank: 41 / 88 = 0.466 (2022) - Q2 - T2
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 100 / 274 = 0.365 (2022) - Q2 - T2

Factor impacto CITESCORE: 9.0 - Engineering (Q1) - Computer Science (Q1) - Materials Science (Q1)

Factor impacto SCIMAGO: 0.926 - Computer Science (miscellaneous) (Q1) - Materials Science (miscellaneous) (Q1) - Engineering (miscellaneous) (Q1)

Tipo y forma: Article (Published version)

Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)