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Titre : Commande predictive par la theorie des intervalles flous et metaheuristiques Type de document : texte imprimé Auteurs : Halim Merabti, Auteur ; Khaled Belarbi, Directeur de thèse Editeur : Constantine : Université Mentouri Constantine Année de publication : 2015 Importance : 88 f. Format : 30 cm. Note générale : 2 copies imprimées disponibles
Langues : Français (fre) Catégories : Français - Anglais
ElectroniqueTags : commande prédictive intervalle flou métaheuristiques optimisation multiobjectifs predictive control fuzzy interval metaheuristics multi objective optimization التحكم التنبئي المجال المبھم طرق الاستكشاف التحسین متعدد الأھداف Index. décimale : 621 Electronique Résumé : In this work, a robust predictive controller was developed based on fuzzy intervals theory.
The drawback of this method is that it is time consuming. To raise this problem, the applicability of metaheuristics to determine the online predictive control optimal solution was studied. For this, a comparison between three metaheuristics was carried out: ant colony optimization and particle swarm optimization and gravitational search algorithm. Results show that particle swarm optimization algorithm converges faster. The latter was applied (by an experimental study) for the control of two wheel mobile robot. After that, a simulation study was carried out on the applicability of multi objective metaheuristics for solving multi objective predictive control. The results show that the multi objective particle swarm optimization algorithm is encouraging for real-time applications and can be an alternative to other methods which are generally more difficult to implement
Diplôme : Doctorat en sciences En ligne : ../theses/electronique/MER6780.pdf Format de la ressource électronique : Permalink : https://bu.umc.edu.dz/md/index.php?lvl=notice_display&id=9829 Commande predictive par la theorie des intervalles flous et metaheuristiques [texte imprimé] / Halim Merabti, Auteur ; Khaled Belarbi, Directeur de thèse . - Constantine : Université Mentouri Constantine, 2015 . - 88 f. ; 30 cm.
2 copies imprimées disponibles
Langues : Français (fre)
Catégories : Français - Anglais
ElectroniqueTags : commande prédictive intervalle flou métaheuristiques optimisation multiobjectifs predictive control fuzzy interval metaheuristics multi objective optimization التحكم التنبئي المجال المبھم طرق الاستكشاف التحسین متعدد الأھداف Index. décimale : 621 Electronique Résumé : In this work, a robust predictive controller was developed based on fuzzy intervals theory.
The drawback of this method is that it is time consuming. To raise this problem, the applicability of metaheuristics to determine the online predictive control optimal solution was studied. For this, a comparison between three metaheuristics was carried out: ant colony optimization and particle swarm optimization and gravitational search algorithm. Results show that particle swarm optimization algorithm converges faster. The latter was applied (by an experimental study) for the control of two wheel mobile robot. After that, a simulation study was carried out on the applicability of multi objective metaheuristics for solving multi objective predictive control. The results show that the multi objective particle swarm optimization algorithm is encouraging for real-time applications and can be an alternative to other methods which are generally more difficult to implement
Diplôme : Doctorat en sciences En ligne : ../theses/electronique/MER6780.pdf Format de la ressource électronique : Permalink : https://bu.umc.edu.dz/md/index.php?lvl=notice_display&id=9829 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MER/6780 MER/6780 Thèse Bibliothèque principale Thèses Disponible Modelisation et optimisation d'une machine a reluctance variable par algorithmes intelligents / Sihem Mouellef
Titre : Modelisation et optimisation d'une machine a reluctance variable par algorithmes intelligents Type de document : texte imprimé Auteurs : Sihem Mouellef, Auteur ; Ammar Bentounsi, Directeur de thèse ; Hocine Benalla, Directeur de thèse Editeur : جامعة الإخوة منتوري قسنطينة Année de publication : 2016 Importance : 143 f. Format : 30 cm. Note générale : 2 copies imprimées disponibles
Langues : Français (fre) Catégories : Français - Anglais
Electro-techniqueTags : Variable reluctance machine Analytical modeling Finites elements method hybride modeling Optimization metaheuristics Genetic algorithm particle swarm
optimization Machine à réluctance variable Modèles analytiques Méthode des éléments finis Modélisation hybride Optimisation méta-heuristiques Algorithme génétique Optimisation
par essaim particulaireIndex. décimale : 622 Electro-Technique Résumé : The theme dealt with in this thesis focuses on the modeling of a variable reluctance
motor (VRM) adapted to the process of optimizing its performance we have developed and
tested. Knowing that the performance of VRM depends as well on the geometric structure, the
nonlinear characteristics of the materials used and the converter control parameters, we
therefore paid particular attention to the associated mathematical model, based on an
analytical approach taking into account the peculiarity of the VRM run in saturated state.
Despite the accuracy of numerical approaches such as finite element method,
researchers are moving increasingly towards hybrid methods combining their analytical
models as better suited to an optimization process; this is the approach we have adopted here.
Thus we have oriented our work towards finding hybrid models (analytical- numerical)
sufficiently accurate while being rapid execution. The validation of these models was made
by comparing the results obtained and the numerical simulations by finite elements in Flux-
2D.
Validation and exploitation of results of different simulations allowed the continuation
of work to optimize the geometric parameters for stator and rotor teeth of the studied
prototype. After state of the art different algorithms of optimizations present in the literature,
our choice fell on Genetic Algorithms and Swarm particles. The various simulations in
MATLAB environment allowed get optimized dental angles and significantly improve the
average couple.
Diplôme : Doctorat en sciences En ligne : ../theses/electrotec/MOU7020.pdf Format de la ressource électronique : Permalink : https://bu.umc.edu.dz/md/index.php?lvl=notice_display&id=10574 Modelisation et optimisation d'une machine a reluctance variable par algorithmes intelligents [texte imprimé] / Sihem Mouellef, Auteur ; Ammar Bentounsi, Directeur de thèse ; Hocine Benalla, Directeur de thèse . - جامعة الإخوة منتوري قسنطينة, 2016 . - 143 f. ; 30 cm.
2 copies imprimées disponibles
Langues : Français (fre)
Catégories : Français - Anglais
Electro-techniqueTags : Variable reluctance machine Analytical modeling Finites elements method hybride modeling Optimization metaheuristics Genetic algorithm particle swarm
optimization Machine à réluctance variable Modèles analytiques Méthode des éléments finis Modélisation hybride Optimisation méta-heuristiques Algorithme génétique Optimisation
par essaim particulaireIndex. décimale : 622 Electro-Technique Résumé : The theme dealt with in this thesis focuses on the modeling of a variable reluctance
motor (VRM) adapted to the process of optimizing its performance we have developed and
tested. Knowing that the performance of VRM depends as well on the geometric structure, the
nonlinear characteristics of the materials used and the converter control parameters, we
therefore paid particular attention to the associated mathematical model, based on an
analytical approach taking into account the peculiarity of the VRM run in saturated state.
Despite the accuracy of numerical approaches such as finite element method,
researchers are moving increasingly towards hybrid methods combining their analytical
models as better suited to an optimization process; this is the approach we have adopted here.
Thus we have oriented our work towards finding hybrid models (analytical- numerical)
sufficiently accurate while being rapid execution. The validation of these models was made
by comparing the results obtained and the numerical simulations by finite elements in Flux-
2D.
Validation and exploitation of results of different simulations allowed the continuation
of work to optimize the geometric parameters for stator and rotor teeth of the studied
prototype. After state of the art different algorithms of optimizations present in the literature,
our choice fell on Genetic Algorithms and Swarm particles. The various simulations in
MATLAB environment allowed get optimized dental angles and significantly improve the
average couple.
Diplôme : Doctorat en sciences En ligne : ../theses/electrotec/MOU7020.pdf Format de la ressource électronique : Permalink : https://bu.umc.edu.dz/md/index.php?lvl=notice_display&id=10574 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MOU/7020 MOU/7020 Thèse Bibliothèque principale Thèses Disponible
