This paper presents an adaptive observer-based robust estimation methodology of the amplitudes, frequencies and phases of biased multi-sinusoidal signals in presence of bounded perturbations on the measurement. The parameters of the sinusoidal components are estimated on-line and the update laws are individually controlled by an excitation-based switching logic enabling the update of a parameter only when the measured signal is sufficiently informative. This way doing, the algorithm is able to tackle the problem of over-parametrization (i.e., when the internal model accounts for a number of sinusoids that is larger than the true spectral content) or temporarily fading sinusoidal components. The stability analysis proves the existence of a tuning parameter set for which the estimator’s dynamics are input-to-state stable with respect to bounded measurement disturbances. The performance of the proposed estimation approach is evaluated and compared with other existing tools by extensive simulation trials and real-time experiments.

An Adaptive Observer-based Robust Estimator of Multi-sinusoidal Signals

T. Parisini
2018-01-01

Abstract

This paper presents an adaptive observer-based robust estimation methodology of the amplitudes, frequencies and phases of biased multi-sinusoidal signals in presence of bounded perturbations on the measurement. The parameters of the sinusoidal components are estimated on-line and the update laws are individually controlled by an excitation-based switching logic enabling the update of a parameter only when the measured signal is sufficiently informative. This way doing, the algorithm is able to tackle the problem of over-parametrization (i.e., when the internal model accounts for a number of sinusoids that is larger than the true spectral content) or temporarily fading sinusoidal components. The stability analysis proves the existence of a tuning parameter set for which the estimator’s dynamics are input-to-state stable with respect to bounded measurement disturbances. The performance of the proposed estimation approach is evaluated and compared with other existing tools by extensive simulation trials and real-time experiments.
2018
giu-2018
Pubblicato
File in questo prodotto:
File Dimensione Formato  
Chen_Pin_Ng_Hui_Parisini_TAC_Accepted_Version_27_8_2017.pdf

accesso aperto

Descrizione: Articolo accettato
Tipologia: Bozza finale post-referaggio (post-print)
Licenza: Copyright Editore
Dimensione 1.1 MB
Formato Adobe PDF
1.1 MB Adobe PDF Visualizza/Apri
Chen_Pin_Ng_Hui_Parisini_TAC_2018.pdf

Accesso chiuso

Descrizione: Articolo pubblicato
Tipologia: Documento in Versione Editoriale
Licenza: Copyright Editore
Dimensione 1.64 MB
Formato Adobe PDF
1.64 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2917201
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 16
  • ???jsp.display-item.citation.isi??? 14
social impact