Consistent and computationally efficient ...
Type de document :
Communication dans un congrès avec actes
Titre :
Consistent and computationally efficient estimation for stochastic LPV state-space models: realization based approach
Auteur(s) :
Mejari, Manas [Auteur]
Petreczky, Mihaly [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Petreczky, Mihaly [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
58th IEEE Conference on Decision and Control (CDC 2019)
Ville :
Nice
Pays :
France
Date de début de la manifestation scientifique :
2019-12-11
Titre de la revue :
2019 IEEE 58th Conference on Decision and Control (CDC)
Date de publication :
2019
Discipline(s) HAL :
Mathématiques [math]/Optimisation et contrôle [math.OC]
Sciences de l'ingénieur [physics]/Automatique / Robotique
Sciences de l'ingénieur [physics]/Automatique / Robotique
Résumé en anglais : [en]
The article presents an identification algorithm for stochastic Linear Parameter-Varying State-Space Affine (LPVSSA) representations, where the dependency of state-space matrices on scheduling signals is affine. Based on ...
Lire la suite >The article presents an identification algorithm for stochastic Linear Parameter-Varying State-Space Affine (LPVSSA) representations, where the dependency of state-space matrices on scheduling signals is affine. Based on stochastic realization theory, a computationally efficient and statistically consistent identification algorithm is proposed to estimate the LPV model matrices, which are computed from the empirical covariance matrices of outputs and scheduling signal observations. The effectiveness of the proposed realization algorithm is shown via a numerical case study.Lire moins >
Lire la suite >The article presents an identification algorithm for stochastic Linear Parameter-Varying State-Space Affine (LPVSSA) representations, where the dependency of state-space matrices on scheduling signals is affine. Based on stochastic realization theory, a computationally efficient and statistically consistent identification algorithm is proposed to estimate the LPV model matrices, which are computed from the empirical covariance matrices of outputs and scheduling signal observations. The effectiveness of the proposed realization algorithm is shown via a numerical case study.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
- https://hal.archives-ouvertes.fr/hal-02398575/document
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- Realization_Stoch_LPV_V12_Final_cdc_reduced.pdf
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