Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/69096
Title: A study on torque modelling of switched reluctance motors
Authors: Zheng, Q.
Xu, J.-X. 
Panda, S.K. 
Keywords: artificial neural network
genetic algorithm
Levenberg-Marquardt gradient expansion method
Switched reluctance motor
torque modeling
Issue Date: 2013
Source: Zheng, Q.,Xu, J.-X.,Panda, S.K. (2013). A study on torque modelling of switched reluctance motors. Proceedings of the American Control Conference : 321-326. ScholarBank@NUS Repository.
Abstract: In this paper we develop and verify the suitability of two torque models of the switched reluctance motor (SRM). The first torque model is constructed analytically in terms of the well known flux saturation characteristics of the SRM. The torque modeling problem renders to an optimization process: minimizing the discrepancy between the model estimated torque and measured torque by means of tuning 18 coefficients in the torque model. Both statistic search-Genetic Algorithm (GA), and deterministic search-Levenberg-Marquardt (LM) gradient expansion method, are employed to search the optimal solution. Through comparative study, we show that the combination of the two: GA searches the neighborhood of the global minimum and LM refines, gives the best results. The second torque model is constructed using artificial neural network (ANN), which provides a model-free black-box approach. While the simulation results show the effectiveness of both models, the experimental results indicate that the analytic model using domain knowledge outperforms the ANN model. © 2013 AACC American Automatic Control Council.
Source Title: Proceedings of the American Control Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/69096
ISBN: 9781479901777
ISSN: 07431619
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

18
checked on Dec 9, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.