<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Due to the highly nonlinearity of the flux-linkage characteristics of Switched Reluctance Motor drives (SRM), accurately modelling is cumbersome. In this paper, the offline- trained and the online-trained Radial Basis function (RBF) neural network model are proposed for estimating the SRM flux-linkage under running conditions. To investigate the performance of the modelling schemes, the simulation and experiments have been implemented in a 12/8 structure SRM prototype. The results show that the online-trained model exhibits much better estimation accuracy and robustness than the offline-trained model. Thus, the online-trained RBF model is more suitable for SRM performance prediction and analyzing.</p></abstract>

Due to the highly nonlinearity of the flux-linkage characteristics of Switched Reluctance Motor drives (SRM), accurately modelling is cumbersome. In this paper, the offline- trained and the online-trained Radial Basis function (RBF) neural network model are proposed for estimating the SRM flux-linkage under running conditions. To investigate the performance of the modelling schemes, the simulation and experiments have been implemented in a 12/8 structure SRM prototype. The results show that the online-trained model exhibits much better estimation accuracy and robustness than the offline-trained model. Thus, the online-trained RBF model is more suitable for SRM performance prediction and analyzing.

Offline and Online Modelling of Switched Reluctance Motor Based on RBF Neural Networks

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

Journal of Electrical Engineering

Due to the highly nonlinearity of the flux-linkage characteristics of Switched Reluctance Motor drives (SRM), accurately modelling is cumbersome. In this...

Offline and Online Modelling of Switched Reluctance Motor Based on RBF Neural Networks

Online veröffentlicht: 08. Juni 2013

Seitenbereich: 186 - 190

DOI: https://doi.org/10.2478/jee-2013-0027

Schlüsselwörter
offline modelling, online modelling, RBF neural network, flux-linkage, switched reluctance motor (SRM)

This content is open access.

Offline and Online Modelling of Switched Reluctance Motor Based on RBF Neural Networks

Online veröffentlicht: 08. Juni 2013

Seitenbereich: 186 - 190

DOI: https://doi.org/10.2478/jee-2013-0027

Schlüsselwörteroffline modelling, online modelling, RBF neural network, flux-linkage, switched reluctance motor (SRM)

This content is open access.

Schlüsselwörter
offline modelling, online modelling, RBF neural network, flux-linkage, switched reluctance motor (SRM)