Contribution to modeling, analysis and control law optimization for DC-DC power converters

The use of power converters for real life applications is continuously increasing. Technological requirements include high precision levels and very good performances at the same time, where DC-DC converters have always played an important role in energy conversion-based systems. Our interest, throughout this thesis, is to analyze modeling and control law synthesis approaches in order to provide efficient control laws that are stable within the operating range, in response to certain specifications and also taking into account the problem of being industrially applicable. The aim of our research is hence to propose control law synthesis based on formalized {modeling + control} approaches, and adaptable to the operating point change. The exploited principles deal with Sliding Mode Observation and Control on one hand, and with the Passivity theory for control law synthesis coupled with the Immersion and Invariance principle for synthesizing {observers + load estimators} on the other. Also, the ease of implementing and validating the control law structures with common hardware available in the industry has always been a main issue throughout our study. In the view of illustrating the efficacy of the proposed methods, their experimental validation has been carried out on the SEPIC. This type of converter has many advantages compared to other converters. However, despite its advantages, it is still not well-exploited due to the difficulty in obtaining control laws capable of stabilizing its output voltage within a wide operating range.

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Source https://theses.hal.science/tel-00644419
Author Jaafar, Ali
Maintainer CCSD
Last Updated May 15, 2026, 11:16 (UTC)
Created May 15, 2026, 11:16 (UTC)
Identifier NNT: 2011SUPL0017
Language fr
Rights https://about.hal.science/hal-authorisation-v1/
contributor Supélec Sciences des Systèmes (E3S) ; Ecole Supérieure d'Electricité - SUPELEC (FRANCE)
creator Jaafar, Ali
date 2011-11-14T00:00:00
harvest_object_id ba0a31ab-5ae3-4c40-97d7-026f9de58567
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-03-31T00:00:00
set_spec type:THESE