In this paper, the state estimation problem of linear continuous-time systems is dealt with by a nonasymptotic state observer, which allows the state estimation error to decay within an arbitrarily-small finite time without resorting to high-gain injection. By processing the measured input and output signals through modulation integrals, a number of auxiliary signals not affected by the initial conditions are obtained, from which the system state can be computed by simple algebra. The problem of internal instability of modulation integrals is addressed by resorting to a periodic rescaling mechanism that prevents error accumulation and singularities. We show that the combination of modulation integrals with periodic rescaling can be implemented as a jump-linear system. The robustness of the devised method with respect to additive measurement perturbations on the system’s input/output is characterized by Input-to-State Stability arguments.
Robust Deadbeat Continuous-Time Observer Design Based on Modulation Integrals
G. Pin;T. Parisini
2019-01-01
Abstract
In this paper, the state estimation problem of linear continuous-time systems is dealt with by a nonasymptotic state observer, which allows the state estimation error to decay within an arbitrarily-small finite time without resorting to high-gain injection. By processing the measured input and output signals through modulation integrals, a number of auxiliary signals not affected by the initial conditions are obtained, from which the system state can be computed by simple algebra. The problem of internal instability of modulation integrals is addressed by resorting to a periodic rescaling mechanism that prevents error accumulation and singularities. We show that the combination of modulation integrals with periodic rescaling can be implemented as a jump-linear system. The robustness of the devised method with respect to additive measurement perturbations on the system’s input/output is characterized by Input-to-State Stability arguments.File | Dimensione | Formato | |
---|---|---|---|
Pin_Chen_Parisini_Automatica_Accepted_Version_5_4_2019.pdf
Open Access dal 01/06/2021
Descrizione: Articolo accettato
Tipologia:
Bozza finale post-referaggio (post-print)
Licenza:
Creative commons
Dimensione
664.5 kB
Formato
Adobe PDF
|
664.5 kB | Adobe PDF | Visualizza/Apri |
1-s2.0-S0005109819302493-main.pdf
Accesso chiuso
Tipologia:
Documento in Versione Editoriale
Licenza:
Copyright Editore
Dimensione
1 MB
Formato
Adobe PDF
|
1 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.