This paper presents an algorithm for continuous-time identification of linear dynamical systems using kernel methods. When the system is asymptotically stable, also the identified model is guaranteed to share such a property. The approach embeds the selection of the model complexity through optimization of the marginal likelihood of the data thanks to its Bayesian interpretation. The output of the algorithm is the continuous-time transfer function of the estimated model. In this work, we show the algorithmic and computational details of the approach, and test it on real experimental data from an Electro Hydro-Static Actuator (EHSA).

(2021). Nonparametric continuous-time identification of linear systems: theory, implementation and experimental results . Retrieved from http://hdl.handle.net/10446/199410

Nonparametric continuous-time identification of linear systems: theory, implementation and experimental results

Mazzoleni, Mirko;Scandella, Matteo;Previdi, Fabio
2021-01-01

Abstract

This paper presents an algorithm for continuous-time identification of linear dynamical systems using kernel methods. When the system is asymptotically stable, also the identified model is guaranteed to share such a property. The approach embeds the selection of the model complexity through optimization of the marginal likelihood of the data thanks to its Bayesian interpretation. The output of the algorithm is the continuous-time transfer function of the estimated model. In this work, we show the algorithmic and computational details of the approach, and test it on real experimental data from an Electro Hydro-Static Actuator (EHSA).
mirko.mazzoleni@unibg.it
2021
Inglese
Modeling, Estimation and Control Conference MECC 2021
Wang, Junmin; Fathy, Hosam; Wang, Qian; Ren, Beibei
54
20
699
704
online
Netherlands
Amsterdam
Elsevier B.V.
MECC 2021: Modeling, Estimation and Control Conference, Austin, USA, 24-27 October 2021
Austin (USA)
24-27 October 2021
International Federation of Automatic Control
internazionale
contributo
Settore ING-INF/04 - Automatica
Software for system identification; Kernel methods
indice consultabile alla pagina degli atti
info:eu-repo/semantics/conferenceObject
4
Mazzoleni, Mirko; Scandella, Matteo; Formentin, Simone; Previdi, Fabio
1.4 Contributi in atti di convegno - Contributions in conference proceedings::1.4.01 Contributi in atti di convegno - Conference presentations
open
Non definito
273
(2021). Nonparametric continuous-time identification of linear systems: theory, implementation and experimental results . Retrieved from http://hdl.handle.net/10446/199410
File allegato/i alla scheda:
File Dimensione del file Formato  
2021 IFAC MECC - kernel continuous sw.pdf

accesso aperto

Versione: publisher's version - versione editoriale
Licenza: Creative commons
Dimensione del file 776.59 kB
Formato Adobe PDF
776.59 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/199410
Citazioni
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact