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
Keywords	NEURAL NETWORK MOTOR DRIVES MOTORS CONTROLS FLUX SRM
Date of Defense	2005-08-23
Availability	unrestricted
Abstract 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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