Topology identification of network systems
Type de document :
Communication dans un congrès avec actes
Titre :
Topology identification of network systems
Auteur(s) :
Langueh, Kokou [Auteur]
Non-Asymptotic estimation for online systems [NON-A]
Zheng, Gang [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Deformable Robots Simulation Team [DEFROST ]
Floquet, Thierry [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Non-Asymptotic estimation for online systems [NON-A]
Zheng, Gang [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Deformable Robots Simulation Team [DEFROST ]
Floquet, Thierry [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
CDC 2017 - 56th IEEE Conference on Decision and Control
Ville :
Melbourne
Pays :
Australie
Date de début de la manifestation scientifique :
2017-12-12
Discipline(s) HAL :
Informatique [cs]/Automatique
Résumé en anglais : [en]
— This paper investigates the topology identification problem for network systems, whose subsystems are described by ODEs. In our study, the topology connections are represented as constant parameters, therefore the topology ...
Lire la suite >— This paper investigates the topology identification problem for network systems, whose subsystems are described by ODEs. In our study, the topology connections are represented as constant parameters, therefore the topology identification is equivalent to identify the unknown parameters. A sufficient condition on parameter identifiability is firstly deduced in this paper, and then a uniform differentiator with fixed-time convergence is proposed to estimate the unknown parameters.Lire moins >
Lire la suite >— This paper investigates the topology identification problem for network systems, whose subsystems are described by ODEs. In our study, the topology connections are represented as constant parameters, therefore the topology identification is equivalent to identify the unknown parameters. A sufficient condition on parameter identifiability is firstly deduced in this paper, and then a uniform differentiator with fixed-time convergence is proposed to estimate the unknown parameters.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :