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)


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.


Hide Layer Learning Ability Torque Control Torque Ripple Switch Reluctance Motor 
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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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