Abstract
Application of conventional neural network (NN) in modeling and control of switched reluctance motor (SRM) has been limited due to its structure of low degree of freedom, which results in a huge network with large numbers of neurons. In this paper, a flexible neural network (FNN), which uses flexible sigmoid function, is proposed to improve the learning ability of network, and the learning algorithm is derived. It greatly simplifies the network with fewer neurons and reduces iterative learning epochs. FNN based desired-current-waveform control for SRM, where FNN provides the inverse torque model, is presented. Simulation results verify the proposed method, and show that FNN gives better performances than conventional NN and the torque output of the control system has a very small ripple.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rahman, K.M., Gopalakrishnan, S., Fahimi, B., Velayutham Rajarathnam, A., Ehsani, M.: Optimized Torque Control of Switched Reluctance Motor at All Operational Regimes using Neural Network. IEEE Transactions on Industry Applications 37, 904–913 (2001)
Teshnehlab, M., Watanabe, K.: Intelligent Control Based on Flexible Neural Networks. Kluwer Publishers, Dordrecht (1999)
Bevrani, H.: A Novel Approach for Power System Load Frequency Controller Design. In: Transmission and Distribution Conference and Exhibition 2002, Asia Pacific, vol. 1, pp. 184–189. IEEE/PES (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ge, B., de Almeida, A.T., Ferreira, F.J.T.E. (2005). Torque Control of Switched Reluctance Motors Based on Flexible Neural Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_27
Download citation
DOI: https://doi.org/10.1007/11427469_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)