@ARTICLE{Laidi_Kamel_Smart_2021,
 author={Laidi, Kamel and Bouchhida, Ouahid and Nibouche, Mokhtar and Benmansour, Khelifa},
 volume={vol. 70},
 number={No 3},
 journal={Archives of Electrical Engineering},
 pages={531-550},
 howpublished={online},
 year={2021},
 publisher={Polish Academy of Sciences},
 abstract={A smart control based on neural networks for multicellular converters has been developed and implemented. The approach is based on a behavioral description of the different converter operating modes. Each operating mode represents a well-defined configuration for which an operating zone satisfying given invariance conditions, depending on the capacitors’ voltages and the load current of the converter, is assigned. A control vector, whose components are the control signals to be applied to the converter switches is generated for each mode. Therefore, generating the control signals becomes a classification task of the different operating zones. For this purpose, a neural approach has been developed and implemented to control a 2-cell converter then extended to a 3-cell converter. The developed approach has been compared to super-twisting sliding mode algorithm. The obtained results demonstrate the approach effectiveness to provide an efficient and robust control of the load current and ensure the balancing of the capacitors voltages.},
 type={Article},
 title={Smart control based on neural networks for multicellular converters},
 URL={http://czasopisma.pan.pl/Content/120517/art03_corr.pdf},
 doi={10.24425/aee.2021.137572},
 keywords={multicellular converters, neural networks, smart control},
}