Model reduction of linear hybrid systems
Document type :
Communication dans un congrès avec actes
Title :
Model reduction of linear hybrid systems
Author(s) :
Gosea, Ion Victor [Auteur]
Max Planck Institute for Dynamics of Complex Technical Systems
Petreczky, Mihaly [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Leth, John [Auteur]
Wisniewski, Rafael [Auteur]
Antoulas, Athanasios C. [Auteur]
Max Planck Institute for Dynamics of Complex Technical Systems
Max Planck Institute for Dynamics of Complex Technical Systems
Petreczky, Mihaly [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Leth, John [Auteur]
Wisniewski, Rafael [Auteur]
Antoulas, Athanasios C. [Auteur]
Max Planck Institute for Dynamics of Complex Technical Systems
Conference title :
59th IEEE Conference on Decision and Control (CDC)
City :
Jeju Island (virtual)
Country :
Corée du Sud
Start date of the conference :
2020-12-08
Book title :
2020 59th IEEE Conference on Decision and Control (CDC)
Publication date :
2020-12
HAL domain(s) :
Informatique [cs]/Automatique
English abstract : [en]
The paper proposes a model reduction algorithm for linear hybrid systems, i.e., hybrid systems with externally induced discrete events, with linear continuous subsystems, and linear reset maps. The model reduction algorithm ...
Show more >The paper proposes a model reduction algorithm for linear hybrid systems, i.e., hybrid systems with externally induced discrete events, with linear continuous subsystems, and linear reset maps. The model reduction algorithm is based on balanced truncation. Moreover, the paper also proves an analytical error bound for the difference between the input-output behaviors of the original and the reduced order model. This error bound is formulated in terms of singular values of the Gramians used for model reduction.Show less >
Show more >The paper proposes a model reduction algorithm for linear hybrid systems, i.e., hybrid systems with externally induced discrete events, with linear continuous subsystems, and linear reset maps. The model reduction algorithm is based on balanced truncation. Moreover, the paper also proves an analytical error bound for the difference between the input-output behaviors of the original and the reduced order model. This error bound is formulated in terms of singular values of the Gramians used for model reduction.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :
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