In this paper a comparison is presented between two strategies for the control of a Dual Active Bridge (DAB) converter regulated through Single Phase Shift (SPS) modulation. The purpose of the controller is to regulate the phase-shift between the voltage impressed to the inductor from the primary and the secondary converter to obtain the desired output current. To achieve this goal, two control strategies based on different approaches are proposed. The first one performs the regulation using a Proportional-Integral (PI) controller, while the second one, called Model Reference Control (MRC), performs the regulation through Neural Networks (NN). The obtained results show that the Artificial Intelligence (AI) based technique has comparable reference current tracking performance and better dynamic characteristics. Furthermore, this technique does not require the mathematical model of the converter and can be used in a wider range of operating conditions.

Comparison Between PI and Neural Network Controller for Dual Active Bridge Converter / Bindi M.; Garcia C.I.; Corti F.; Piccirilli M.C.; Luchetta A.; Grasso F.; Manetti S.. - ELETTRONICO. - (2021), pp. 1-6. ((Intervento presentato al convegno 2021 IEEE 15th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) [10.1109/CPE-POWERENG50821.2021.9501168].

Comparison Between PI and Neural Network Controller for Dual Active Bridge Converter

Bindi M.;Corti F.;Piccirilli M. C.;Luchetta A.;Grasso F.;Manetti S.
2021

Abstract

In this paper a comparison is presented between two strategies for the control of a Dual Active Bridge (DAB) converter regulated through Single Phase Shift (SPS) modulation. The purpose of the controller is to regulate the phase-shift between the voltage impressed to the inductor from the primary and the secondary converter to obtain the desired output current. To achieve this goal, two control strategies based on different approaches are proposed. The first one performs the regulation using a Proportional-Integral (PI) controller, while the second one, called Model Reference Control (MRC), performs the regulation through Neural Networks (NN). The obtained results show that the Artificial Intelligence (AI) based technique has comparable reference current tracking performance and better dynamic characteristics. Furthermore, this technique does not require the mathematical model of the converter and can be used in a wider range of operating conditions.
2021 IEEE 15th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)
2021 IEEE 15th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)
Bindi M.; Garcia C.I.; Corti F.; Piccirilli M.C.; Luchetta A.; Grasso F.; Manetti S.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1283050
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? ND
social impact