NDLI: Sensorless position detection using neural networks for the control of switched reluctance motors

Content Provider	IEEE Xplore Digital Library
Author	Reay, D.S. Williams, B.W.
Copyright Year	1999
Description	Author affiliation: Dept. of Comput. & Electr. Eng., Heriot-Watt Univ., Edinburgh, UK (Reay, D.S.)
Abstract	For high performance position or torque control, or for many of the different possible approaches to torque ripple and acoustic noise reduction in a switched reluctance motor (SRM), position feedback is essential. However, optical position encoders add to the complexity and cost of SRMs, compromising some of their main advantages. The paper describes a novel method of sensorless position detection requiring no special converter or sensor circuitry, and which does not rely on accurate prior knowledge of the magnetic characteristics of the motor. The approach described is novel in two respects. Firstly, it does not rely on accurate prior knowledge of phase winding inductance but merely makes the assumption that it varies substantially as sin(N/sub r//spl theta/), where N/sub r/ is the number of rotor poles and /spl theta/ is rotor angle. Secondly, the approach learns from previous good estimates of position and, once it has done so, makes use of this knowledge where performance of the basic estimation algorithm degrades (principally at low speeds of rotation). The technique has been investigated in simulation and a hardware implementation is under development.
Starting Page	1073
Ending Page	1077
File Size	324086
Page Count	5
File Format	PDF
ISBN	078035446X
DOI	10.1109/CCA.1999.801059
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-08-22
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Sensorless control Reluctance motors Acoustic signal detection Reluctance machines Optical feedback Torque control Acoustic noise Neurofeedback Optical computing
Content Type	Text
Resource Type	Article

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