{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105643"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105643","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Position estimation of an outer rotor permanent magnet synchronous machine using linear hall-effect sensors and neural networks","abstract":"This thesis presents and evaluates a new method for estimating the angular position for an outer rotor permanent magnet synchronous machine (PMSM). PMSMs are increasingly used as prime movers in electric vehicles such as cars and bicycles, and the precise control of these machines requires reliable feedback of the rotational position of the rotor. Conventional methods of achieving this feedback signal rely on either physically connected sensors or the implementation of sensorless methods, each of which has certain drawbacks. The proposed method uses an array of linear Hall-effect sensors located in the leakage magnetic field of the rotor. These sensors detect the rotation-dependent changing field, which is fed into a machine-learning based neural network algorithm to interpret the signals. Due to the use of machine-learning, the algorithm will first need to be trained to properly correlate the sensor signals to the rotor angle. Data sets of training signals are acquired with commercial sensors and an outer rotor PMSM, and offline training steps and results are discussed. The main objective is to design a cost-effective position estimation system that is comparable to encoders and resolvers in functionality and performance, without the limitations of sensorless position estimation methods.","abstract_html":"This thesis presents and evaluates a new method for estimating the angular position for an outer rotor permanent magnet synchronous machine (PMSM). PMSMs are increasingly used as prime movers in electric vehicles such as cars and bicycles, and the precise control of these machines requires reliable feedback of the rotational position of the rotor. Conventional methods of achieving this feedback signal rely on either physically connected sensors or the implementation of sensorless methods, each of which has certain drawbacks. The proposed method uses an array of linear Hall-effect sensors located in the leakage magnetic field of the rotor. These sensors detect the rotation-dependent changing field, which is fed into a machine-learning based neural network algorithm to interpret the signals. Due to the use of machine-learning, the algorithm will first need to be trained to properly correlate the sensor signals to the rotor angle. Data sets of training signals are acquired with commercial sensors and an outer rotor PMSM, and offline training steps and results are discussed. The main objective is to design a cost-effective position estimation system that is comparable to encoders and resolvers in functionality and performance, without the limitations of sensorless position estimation methods.","abstract_has_math":false,"creators":["Wang, Yuyao"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Haran, Kiruba S"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-11-26T20:33:50Z","date_published":"2019-11-26T20:33:50Z","updated_at":"2026-07-22T22:24:44Z","subjects":["Permanent magnet synchronous machine, position estimation, Hall effect, neural networks"],"languages":["en"],"rights":["Copyright 2019 Yuyao Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105643","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Haran, Kiruba S"]},{"key":"dc:creator","label":"Author","values":["Wang, Yuyao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-11-26T20:33:50Z","2019-07-03","2019-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Permanent magnet synchronous machine, position estimation, Hall effect, neural networks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Yuyao Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105643"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis presents and evaluates a new method for estimating the angular position for an outer rotor permanent magnet synchronous machine (PMSM). PMSMs are increasingly used as prime movers in electric vehicles such as cars and bicycles, and the precise control of these machines requires reliable feedback of the rotational position of the rotor. Conventional methods of achieving this feedback signal rely on either physically connected sensors or the implementation of sensorless methods, each of which has certain drawbacks. The proposed method uses an array of linear Hall-effect sensors located in the leakage magnetic field of the rotor. These sensors detect the rotation-dependent changing field, which is fed into a machine-learning based neural network algorithm to interpret the signals. Due to the use of machine-learning, the algorithm will first need to be trained to properly correlate the sensor signals to the rotor angle. Data sets of training signals are acquired with commercial sensors and an outer rotor PMSM, and offline training steps and results are discussed. The main objective is to design a cost-effective position estimation system that is comparable to encoders and resolvers in functionality and performance, without the limitations of sensorless position estimation methods.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-11-26 without embargo terms","The student, Yuyao Wang, accepted the attached license on 2019-07-03 at 12:50.","The student, Yuyao Wang, submitted this Thesis for approval on 2019-07-03 at 12:54.","This Thesis was approved for publication on 2019-07-03 at 16:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14153 on 2019-11-26 at 12:50:46","Made available in DSpace on 2019-11-26T20:33:50Z (GMT). No. of bitstreams: 2 WANG-THESIS-2019.pdf: 8660884 bytes, checksum: 1039a2d392d989132b17f40b58501b21 (MD5) LICENSE.txt: 4207 bytes, checksum: bed98d123d75476410e61591f59ef15d (MD5) Previous issue date: 2019-07-03"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Position estimation of an outer rotor permanent magnet synchronous machine using linear hall-effect sensors and neural networks"]}]}],"canonical_facts":{"dc:contributor":["Haran, Kiruba S"],"dc:creator":["Wang, Yuyao"],"dc:date":["2019-11-26T20:33:50Z","2019-07-03","2019-08"],"dc:description":["This thesis presents and evaluates a new method for estimating the angular position for an outer rotor permanent magnet synchronous machine (PMSM). PMSMs are increasingly used as prime movers in electric vehicles such as cars and bicycles, and the precise control of these machines requires reliable feedback of the rotational position of the rotor. Conventional methods of achieving this feedback signal rely on either physically connected sensors or the implementation of sensorless methods, each of which has certain drawbacks. The proposed method uses an array of linear Hall-effect sensors located in the leakage magnetic field of the rotor. These sensors detect the rotation-dependent changing field, which is fed into a machine-learning based neural network algorithm to interpret the signals. Due to the use of machine-learning, the algorithm will first need to be trained to properly correlate the sensor signals to the rotor angle. Data sets of training signals are acquired with commercial sensors and an outer rotor PMSM, and offline training steps and results are discussed. The main objective is to design a cost-effective position estimation system that is comparable to encoders and resolvers in functionality and performance, without the limitations of sensorless position estimation methods.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-11-26 without embargo terms","The student, Yuyao Wang, accepted the attached license on 2019-07-03 at 12:50.","The student, Yuyao Wang, submitted this Thesis for approval on 2019-07-03 at 12:54.","This Thesis was approved for publication on 2019-07-03 at 16:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14153 on 2019-11-26 at 12:50:46","Made available in DSpace on 2019-11-26T20:33:50Z (GMT). No. of bitstreams: 2 WANG-THESIS-2019.pdf: 8660884 bytes, checksum: 1039a2d392d989132b17f40b58501b21 (MD5) LICENSE.txt: 4207 bytes, checksum: bed98d123d75476410e61591f59ef15d (MD5) Previous issue date: 2019-07-03"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/105643"],"dc:language":["en"],"dc:rights":["Copyright 2019 Yuyao Wang"],"dc:subject":["Permanent magnet synchronous machine, position estimation, Hall effect, neural networks"],"dc:title":["Position estimation of an outer rotor permanent magnet synchronous machine using linear hall-effect sensors and neural networks"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:44Z"}