| Titre : |
Commande Adaptative Floue des Systèmes Non Linéaires Basée sur les Réseaux à Mémoire Associative à Fonction de Base Radiale |
| Type de document : |
texte imprimé |
| Auteurs : |
Mohamed Bahita, Auteur ; Khaled Belarbi, Directeur de thèse |
| Editeur : |
Constantine : Université Mentouri Constantine |
| Année de publication : |
2011 |
| Importance : |
91 f. |
| Format : |
31 cm |
| Note générale : |
2 copies imprimées disponibles |
| Langues : |
Français (fre) |
| Catégories : |
Français - Anglais
Electronique
|
| Tags : |
Contrôle des Systèmes Linéarisation par retour d’état Commande adaptative des systèmes non linéaires Logique floue (Takagi-Sugeno, Mamdani) Réseau de neurones (RBF) Algorithme des kmeans Stabilité de Lyapunov Linearization by state feedback Adaptive control of nonlinear systems Fuzzy
logic (Takagi-Sugeno, Mamdani) Neural network (RBF) k-means algorithm Lyapunov Stability الخطية بإ رجاع المدخلات التحكم المتكيف للأنظمة غير الخطية المنطق الغامض )-Sugeno
.Lyapunov استقرارk-means ( خوارزميةRBF) ( الشبكة العصبيةMamdani، Takag |
| Index. décimale : |
621 Electronique |
| Résumé : |
In this thesis, we considered the control of nonlinear systems with single input, single output (SISO) and affine in the control input. As a first contribution in this work, two direct approaches are introduced. In the first, we used as a direct controller a fuzzy inference system of Takagi-Sugeno (TS) type. In the second approach, a neural network of Radial Basis Function (RBF) type is introduced as an alternative to the TS controller. In these two first approaches, the virtual control gain is constant. As a main contribution in this thesis, two approaches are addressed: The indirect approach and the direct approach. In the indirect approach, we introduced a fuzzy system of TS type to approximate the ideal control signal and an RBF network to estimate the virtual control gain. In the direct approach, we used a TS fuzzy inference system to approximate the feedback linearization law and a second fuzzy system of Mamdani type to estimate the control error signal that appears in the adaptation law of the TS controller parameters. The rule base of the Mamdani fuzzy estimator is constructed from expert knowledge. Asymptotic stability
based on Lyapunov theory is always guaranteed. |
| Note de contenu : |
Annexe: Théorie de Stabilité de Lyapunov. |
| Diplôme : |
Doctorat en sciences |
| En ligne : |
../theses/electronique/BAH6007.pdf |
| Format de la ressource électronique : |
pdf |
| Permalink : |
index.php?lvl=notice_display&id=5895 |
Commande Adaptative Floue des Systèmes Non Linéaires Basée sur les Réseaux à Mémoire Associative à Fonction de Base Radiale [texte imprimé] / Mohamed Bahita, Auteur ; Khaled Belarbi, Directeur de thèse . - Constantine : Université Mentouri Constantine, 2011 . - 91 f. ; 31 cm. 2 copies imprimées disponibles Langues : Français ( fre)
| Catégories : |
Français - Anglais
Electronique
|
| Tags : |
Contrôle des Systèmes Linéarisation par retour d’état Commande adaptative des systèmes non linéaires Logique floue (Takagi-Sugeno, Mamdani) Réseau de neurones (RBF) Algorithme des kmeans Stabilité de Lyapunov Linearization by state feedback Adaptive control of nonlinear systems Fuzzy
logic (Takagi-Sugeno, Mamdani) Neural network (RBF) k-means algorithm Lyapunov Stability الخطية بإ رجاع المدخلات التحكم المتكيف للأنظمة غير الخطية المنطق الغامض )-Sugeno
.Lyapunov استقرارk-means ( خوارزميةRBF) ( الشبكة العصبيةMamdani، Takag |
| Index. décimale : |
621 Electronique |
| Résumé : |
In this thesis, we considered the control of nonlinear systems with single input, single output (SISO) and affine in the control input. As a first contribution in this work, two direct approaches are introduced. In the first, we used as a direct controller a fuzzy inference system of Takagi-Sugeno (TS) type. In the second approach, a neural network of Radial Basis Function (RBF) type is introduced as an alternative to the TS controller. In these two first approaches, the virtual control gain is constant. As a main contribution in this thesis, two approaches are addressed: The indirect approach and the direct approach. In the indirect approach, we introduced a fuzzy system of TS type to approximate the ideal control signal and an RBF network to estimate the virtual control gain. In the direct approach, we used a TS fuzzy inference system to approximate the feedback linearization law and a second fuzzy system of Mamdani type to estimate the control error signal that appears in the adaptation law of the TS controller parameters. The rule base of the Mamdani fuzzy estimator is constructed from expert knowledge. Asymptotic stability
based on Lyapunov theory is always guaranteed. |
| Note de contenu : |
Annexe: Théorie de Stabilité de Lyapunov. |
| Diplôme : |
Doctorat en sciences |
| En ligne : |
../theses/electronique/BAH6007.pdf |
| Format de la ressource électronique : |
pdf |
| Permalink : |
index.php?lvl=notice_display&id=5895 |
|
Affiner la recherche Interroger des sources externesCommande Adaptative Floue des Systèmes Non Linéaires Basée sur les Réseaux à Mémoire Associative à Fonction de Base Radiale / Mohamed Bahita
