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Torque Control of Switched Reluctance Motors Based on Flexible Neural Network

  • Baoming Ge
  • Aníbal T. de Almeida
  • Fernando J. T. E. Ferreira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

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.

Keywords

Hide Layer Learning Ability Torque Control Torque Ripple Switch Reluctance Motor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Teshnehlab, M., Watanabe, K.: Intelligent Control Based on Flexible Neural Networks. Kluwer Publishers, Dordrecht (1999)zbMATHGoogle Scholar
  3. 3.
    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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Baoming Ge
    • 1
  • Aníbal T. de Almeida
    • 2
  • Fernando J. T. E. Ferreira
    • 2
  1. 1.School of Electrical EngineeringBeijing Jiaotong UniversityBeijingChina
  2. 2.Department of Electrical EngineeringUniversity of CoimbraCoimbraPortugal

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