Single-Phase, Single-Switch, Sensorless Switched Reluctance Motor Drive Utilizing a Minimal Artificial Neural Net

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dc.contributor.authorHudson, Christopher Allenen
dc.contributor.committeechairRamu, Krishnanen
dc.contributor.committeememberBaumann, William T.en
dc.contributor.committeememberKachroo, Pushkinen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2014-03-14T20:44:06Zen
dc.date.adate2005-09-20en
dc.date.available2014-03-14T20:44:06Zen
dc.date.issued2005-08-23en
dc.date.rdate2006-09-20en
dc.date.sdate2005-08-24en
dc.description.abstractArtificial 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.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-08242005-160113en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08242005-160113/en
dc.identifier.urihttp://hdl.handle.net/10919/34735en
dc.publisherVirginia Techen
dc.relation.haspartThesis.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectNeural Networken
dc.subjectMotor Drivesen
dc.subjectMotorsen
dc.subjectControlsen
dc.subjectFluxen
dc.subjectSRMen
dc.titleSingle-Phase, Single-Switch, Sensorless Switched Reluctance Motor Drive Utilizing a Minimal Artificial Neural Neten
dc.typeThesisen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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