Archivio istituzionale della ricerca dell'Università degli Studi di Palermo
IRIS è il sistema di gestione integrata dei dati della ricerca (persone, pubblicazioni, progetti, attività) adottato dall’Università di Palermo, e ha lo scopo di raccogliere, conservare, documentare, e diffondere ad accesso aperto le informazioni relative alla produzione scientifica degli autori afferenti all’Ateneo.
EnglishThis paper deals with the design of an extended complex Kalman filter (ECKF) for estimating the state of an induction motor (IM) model, and for sensorless control of systems employing this type of motor as an actuator. A complex-valued model is adopted that simultaneously allows a simpler observability analysis of the system and a more effective state estimation. The observability analysis of this model is first performed by assuming that a third order ECKF has to be designed, by neglecting the mechanical equation of the IM model, which is a valid hypothesis when the motor is operated at constant rotor speed. It is shown that this analysis is more effective and easier than the one performed on the corresponding real-valued model, as it allows the observability conditions to be directly obtained in terms of stator current and rotor flux complex-valued vectors. Necessary observability conditions are also obtained along with the well-known sufficient ones. It is also shown that the complex-valued implementation allows a reduction of 35% in the computation time w.r.t. the standard real-valued one, which is obtained thanks to the lower dimensions of the matrices of the ECKF w.r.t. the ones of the real- valued implementation and the fact that no matrix inversion is required. The effectiveness of the proposed ECKF is shown by means of simulation in Matlab/Simulink environment and through experiments on a real low-power drive.
Alonge, F., D'Ippolito, F., Fagiolini, A., & Sferlazza, A. (2014). Extended complex Kalman filter for sensorless control of an induction motor. CONTROL ENGINEERING PRACTICE, 27, 1-10.
| Data di pubblicazione: | 2014 |
| Titolo: | Extended complex Kalman filter for sensorless control of an induction motor |
| Autori: | |
| Citazione: | Alonge, F., D'Ippolito, F., Fagiolini, A., & Sferlazza, A. (2014). Extended complex Kalman filter for sensorless control of an induction motor. CONTROL ENGINEERING PRACTICE, 27, 1-10. |
| Rivista: | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/j.conengprac.2014.02.007 |
| Abstract: | This paper deals with the design of an extended complex Kalman filter (ECKF) for estimating the state of an induction motor (IM) model, and for sensorless control of systems employing this type of motor as an actuator. A complex-valued model is adopted that simultaneously allows a simpler observability analysis of the system and a more effective state estimation. The observability analysis of this model is first performed by assuming that a third order ECKF has to be designed, by neglecting the mechanical equation of the IM model, which is a valid hypothesis when the motor is operated at constant rotor speed. It is shown that this analysis is more effective and easier than the one performed on the corresponding real-valued model, as it allows the observability conditions to be directly obtained in terms of stator current and rotor flux complex-valued vectors. Necessary observability conditions are also obtained along with the well-known sufficient ones. It is also shown that the complex-valued implementation allows a reduction of 35% in the computation time w.r.t. the standard real-valued one, which is obtained thanks to the lower dimensions of the matrices of the ECKF w.r.t. the ones of the real- valued implementation and the fact that no matrix inversion is required. The effectiveness of the proposed ECKF is shown by means of simulation in Matlab/Simulink environment and through experiments on a real low-power drive. |
| Settore Scientifico Disciplinare: | Settore ING-INF/04 - Automatica |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| ECKF Alonge.pdf | Articolo principale | N/A | Administrator Richiedi una copia |
http://hdl.handle.net/10447/97987
Copyright © 2021