This paper proposes to use a differential evolution (DE) algorithm to obtain the optimal covariance matrices of a new reduced delayed-state Kalman filter (DSKF) based algorithm to realize sensorless induction motor drives. We propose to use a novel simple stator flux oriented-sliding mode (SFOSM) control scheme in conjunction with the optimized DSKF-based algorithm, estimating the stator flux linkage components. This control scheme has closed loops of torque and stator flux without PI-type controllers and a minimum number of controller gains is required to obtain good performance without fine tuning. The DSKF-based algorithm estimates the stator flux components in the stationary reference frame, using the derivatives of the stator flux components as mathematical model and the stator voltage equations as observation model. The experiments show a comparison between DE and genetic algorithms (GAs) with different settings. The DE outperformed the best known GAs on proposed optimization problem. The paper concentrates on a low-speed training test and the experiments show the low-speed performance of the sensorless control scheme using the new optimized DSKF-based algorithm.

Optimization of DSKF-based algorithm for sensorless SFO-SM control of ims using Differential Evolution

Cascella, G. L.;STASI, Silvio;
2008

Abstract

This paper proposes to use a differential evolution (DE) algorithm to obtain the optimal covariance matrices of a new reduced delayed-state Kalman filter (DSKF) based algorithm to realize sensorless induction motor drives. We propose to use a novel simple stator flux oriented-sliding mode (SFOSM) control scheme in conjunction with the optimized DSKF-based algorithm, estimating the stator flux linkage components. This control scheme has closed loops of torque and stator flux without PI-type controllers and a minimum number of controller gains is required to obtain good performance without fine tuning. The DSKF-based algorithm estimates the stator flux components in the stationary reference frame, using the derivatives of the stator flux components as mathematical model and the stator voltage equations as observation model. The experiments show a comparison between DE and genetic algorithms (GAs) with different settings. The DE outperformed the best known GAs on proposed optimization problem. The paper concentrates on a low-speed training test and the experiments show the low-speed performance of the sensorless control scheme using the new optimized DSKF-based algorithm.
18th International Conference on Electrical Machines, ICEM 2008
978-1-4244-1735-3
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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: http://hdl.handle.net/11589/19899
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 0
social impact