Title page for ETD etd-08242005-160113

Type of Document Master's Thesis
Author Hudson, Christopher Allen
Author's Email Address chhudso1@vt.edu
URN etd-08242005-160113
Title Single-Phase, Single-Switch, Sensorless switched Reluctance Motor Drive utilizing a Minimal Artificial Neural Net
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Ramu, Krishnan Committee Chair
Baumann, William T. Committee Member
Kachroo, Pushkin Committee Member
  • FLUX
  • SRM
Date of Defense 2005-08-23
Availability unrestricted
Artificial Neural Networks (ANNs) have proved to be useful in approximating non-

linear systems in many applications including motion control. ANNs advocated in

switched reluctance motor (SRM) control typically have a large number of neurons

and several layers which impedes their real time implementation in embedded sys-

tems. Real time estimation at high speeds using these ANNs is diffcult due to the

high number of operations required to process the ANN controller. An insuffcient

availability of time between two sampling intervals limits the available computation

time for both processing the neural net and the other functions required for the motor

drive. One ideal application of ANNs in SRM control is rotor position estimation. Due

to reliability issues, elimination of the rotor position sensors is absolutely required for

high volume, high speed and low cost applications of SRM's. ANNs provide a means

by which drive designers can implement position sensorless drive technology that is

both robust and easily implemented.

It is demonstrated that a new and novel ANN configuration can be implemented

for accurate rotor position estimation in a sensorless SRM drive. Consisting of just

4 neurons, the neural estimator is the smallest of its kind for SRM rotor position

estimation. The breakthrough that provided the reduction was the addition of a non-

linear input. Typical input spaces for SRM position neural estimators consist of both

current,and fux-linkage. The neural network was trained on-line using these

inputs and a third, non-linear input provided by a preprocessed product of the two

typical inputs.

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