This paper deals with convergence analysis of the extended Kalman filters (EKFs) for sensorless motion control systems with induction motor (IM). An EKF is tuned according to a six-order discrete-time model of the IM, affected by system and measurement noises, obtained by applying a first-order Euler discretization to a six-order continuous-time model. Some properties of the discrete-time model have been explored. Among these properties, the observability property is relevant, which leads to conditions that can be directly linked with the working conditions of the machine. Starting from these properties, the convergence of the stochastic state estimation process, in mean square sense, has been shown. The convergence is also explored with reference to the difference between the samples of the state of the continuous-time model and that estimated by the EKF. The results theoretically achieved have been also validated by means of experimental tests carried out on an IM prototype.

Alonge, F., Cangemi, T., D'Ippolito, F., Fagiolini, A., Sferlazza, A. (2015). Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 62(4), 2341-2352 [10.1109/TIE.2014.2355133].

Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor

Alonge, Francesco;D'Ippolito, Filippo;Fagiolini, Adriano;Sferlazza, Antonino
2015-01-01

Abstract

This paper deals with convergence analysis of the extended Kalman filters (EKFs) for sensorless motion control systems with induction motor (IM). An EKF is tuned according to a six-order discrete-time model of the IM, affected by system and measurement noises, obtained by applying a first-order Euler discretization to a six-order continuous-time model. Some properties of the discrete-time model have been explored. Among these properties, the observability property is relevant, which leads to conditions that can be directly linked with the working conditions of the machine. Starting from these properties, the convergence of the stochastic state estimation process, in mean square sense, has been shown. The convergence is also explored with reference to the difference between the samples of the state of the continuous-time model and that estimated by the EKF. The results theoretically achieved have been also validated by means of experimental tests carried out on an IM prototype.
2015
Alonge, F., Cangemi, T., D'Ippolito, F., Fagiolini, A., Sferlazza, A. (2015). Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 62(4), 2341-2352 [10.1109/TIE.2014.2355133].
File in questo prodotto:
File Dimensione Formato  
06893030.pdf

Solo gestori archvio

Descrizione: Articolo principale
Dimensione 1.68 MB
Formato Adobe PDF
1.68 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

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/10447/332699
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 128
  • ???jsp.display-item.citation.isi??? 102
social impact