Design, modeling, and position control of a single-phase reluctance machine

  • Authors:

  • Siyu Leng;Wenxin Liu;II-Yop Chung;David Cartes;Chris S. Edrington

  • Affiliations:

  • Center for Advanced Power Systems, Florida State University, Tallahassee, FL;Center for Advanced Power Systems, Florida State University, Tallahassee, FL;Center for Advanced Power Systems, Florida State University, Tallahassee, FL;Center for Advanced Power Systems, Florida State University, Tallahassee, FL;Center for Advanced Power Systems, Florida State University, Tallahassee, FL

  • Venue:
  • IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
  • Year:
  • 2009

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Abstract

This paper discus ses the whole design process of a position control system of a single-phase reluctance machine under mechanical and electrical constraints. A MIMO Neural network is used to model the nonlinear properties of the machine. Based on it, a neural network based control scheme is developed to precisely control the rotor position of the designed single-phase reluctance machine. Simulation results show that a MIMO neural network model can effectively capture the nonlinear characteristics of the designed machine and the proposed neural network control scheme can control the rotor position precisely.