Smart control based on neural networks for multicellular converters

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

Abstracts of 1st International Conference on Computational & Applied Physics

This book contains the abstracts of the papers presented at the International Conference on Computational & Applied Physics (ICCAP’2021) Organized by the Surfaces, Interfaces and Thin Films Laboratory (LASICOM), Department of Physics, Faculty of Science, University Saad Dahleb Blida 1, Algeria, held on 26–28 September 2021. The Conference had a variety of Plenary Lectures, Oral sessions, and E-Poster Presentations. Conference Title: 1st International Conference on Computational & Applied PhysicsConference Acronym: ICCAP’2021Conference Date: 26–28 September 2021Conference Location: Online (Virtual Conference)Conference Organizer: Surfaces, Interfaces, and Thin Films Laboratory (LASICOM), Department of Physics, Faculty of Science, University Saad Dahleb Blida 1, Algeria