Sensorless BLDC Motor Commutation Point Detection and Phase Deviation Correction Method
Phase-to-neutral voltage or neutral-to-virtual neutral voltage zero-crossing points (ZCPs) detection method is usually used for sensorless brushless dc motor commutation control. Unfortunately, neither of them can be realized in lower speed range. In this paper, a simple commutation point detection...
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Veröffentlicht in: | IEEE transactions on power electronics 2019-06, Vol.34 (6), p.5880-5892 |
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description | Phase-to-neutral voltage or neutral-to-virtual neutral voltage zero-crossing points (ZCPs) detection method is usually used for sensorless brushless dc motor commutation control. Unfortunately, neither of them can be realized in lower speed range. In this paper, a simple commutation point detection method is proposed based on detecting inactive phase terminal to dc-link midpoint voltage. It eliminates the requirement of neutral wire or virtual neutral voltage and provides an amplified version of back electromotive force at the ZCPs which makes the lower speed range detection possible. As the speed increases, commutation point error is enlarged due to the low-pass filter. Utilizing the symmetry of the terminal to midpoint voltage, the phase error can be corrected. However, due to the nonlinear relationship between the detected voltage difference and phase error, it is difficult to regulate the error fast and robustly. Therefore, a novel phase regulator based on fuzzy neural network is proposed in this paper with simple structure and learning ability. The validity of the proposed ZCPs detection method and commutation instant shift correction method are verified through experimental results. |
doi_str_mv | 10.1109/TPEL.2018.2867615 |
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Unfortunately, neither of them can be realized in lower speed range. In this paper, a simple commutation point detection method is proposed based on detecting inactive phase terminal to dc-link midpoint voltage. It eliminates the requirement of neutral wire or virtual neutral voltage and provides an amplified version of back electromotive force at the ZCPs which makes the lower speed range detection possible. As the speed increases, commutation point error is enlarged due to the low-pass filter. Utilizing the symmetry of the terminal to midpoint voltage, the phase error can be corrected. However, due to the nonlinear relationship between the detected voltage difference and phase error, it is difficult to regulate the error fast and robustly. Therefore, a novel phase regulator based on fuzzy neural network is proposed in this paper with simple structure and learning ability. The validity of the proposed ZCPs detection method and commutation instant shift correction method are verified through experimental results.</description><identifier>ISSN: 0885-8993</identifier><identifier>EISSN: 1941-0107</identifier><identifier>DOI: 10.1109/TPEL.2018.2867615</identifier><identifier>CODEN: ITPEE8</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial neural networks ; Back electromotive force ; Brushless dc (BLdc) motor ; Brushless motors ; Buck converters ; Commutation ; commutation signal ; D C motors ; Electric potential ; Electromotive forces ; Error correction ; Error detection ; Fuzzy logic ; fuzzy neural network (FNN) ; Fuzzy neural networks ; Harmonic analysis ; Inverters ; Low pass filters ; Neural networks ; Phase deviation ; Phase error ; Product design ; sensorless motor ; Voltage control ; zero-crossing points (ZCPs) detection</subject><ispartof>IEEE transactions on power electronics, 2019-06, Vol.34 (6), p.5880-5892</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-ec4ed26a4efcbba93adf754f7d743e0a1d7eabc5d477ffaa3091a14a5238fa1d3</citedby><cites>FETCH-LOGICAL-c293t-ec4ed26a4efcbba93adf754f7d743e0a1d7eabc5d477ffaa3091a14a5238fa1d3</cites><orcidid>0000-0003-2837-7646 ; 0000-0001-9380-1410 ; 0000-0001-7808-9402</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8450029$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8450029$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhou, Xinxiu</creatorcontrib><creatorcontrib>Zhou, Yongping</creatorcontrib><creatorcontrib>Peng, Cong</creatorcontrib><creatorcontrib>Zeng, Fanquan</creatorcontrib><creatorcontrib>Song, Xinda</creatorcontrib><title>Sensorless BLDC Motor Commutation Point Detection and Phase Deviation Correction Method</title><title>IEEE transactions on power electronics</title><addtitle>TPEL</addtitle><description>Phase-to-neutral voltage or neutral-to-virtual neutral voltage zero-crossing points (ZCPs) detection method is usually used for sensorless brushless dc motor commutation control. Unfortunately, neither of them can be realized in lower speed range. In this paper, a simple commutation point detection method is proposed based on detecting inactive phase terminal to dc-link midpoint voltage. It eliminates the requirement of neutral wire or virtual neutral voltage and provides an amplified version of back electromotive force at the ZCPs which makes the lower speed range detection possible. As the speed increases, commutation point error is enlarged due to the low-pass filter. Utilizing the symmetry of the terminal to midpoint voltage, the phase error can be corrected. However, due to the nonlinear relationship between the detected voltage difference and phase error, it is difficult to regulate the error fast and robustly. Therefore, a novel phase regulator based on fuzzy neural network is proposed in this paper with simple structure and learning ability. The validity of the proposed ZCPs detection method and commutation instant shift correction method are verified through experimental results.</description><subject>Artificial neural networks</subject><subject>Back electromotive force</subject><subject>Brushless dc (BLdc) motor</subject><subject>Brushless motors</subject><subject>Buck converters</subject><subject>Commutation</subject><subject>commutation signal</subject><subject>D C motors</subject><subject>Electric potential</subject><subject>Electromotive forces</subject><subject>Error correction</subject><subject>Error detection</subject><subject>Fuzzy logic</subject><subject>fuzzy neural network (FNN)</subject><subject>Fuzzy neural networks</subject><subject>Harmonic analysis</subject><subject>Inverters</subject><subject>Low pass filters</subject><subject>Neural networks</subject><subject>Phase deviation</subject><subject>Phase error</subject><subject>Product design</subject><subject>sensorless motor</subject><subject>Voltage control</subject><subject>zero-crossing points (ZCPs) detection</subject><issn>0885-8993</issn><issn>1941-0107</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE9LAzEQxYMoWKsfQLwseN6aSbJN9qjb-ge2WLDiMaS7E7ql3dQkLfjt3brF0zDz3psZfoTcAh0B0PxhMZ-WI0ZBjZgayzFkZ2QAuYCUApXnZECVylKV5_ySXIWwphRERmFAvj6wDc5vMITkqZwUycxF55PCbbf7aGLj2mTumjYmE4xY_fWmrZP5ygTsZoem9xTO-5M8w7hy9TW5sGYT8OZUh-TzebooXtPy_eWteCzTiuU8plgJrNnYCLTVcmlybmorM2FlLQVHaqCWaJZVVgsprTWG0xwMCJMxrmyn8iG57_fuvPveY4h67fa-7U5qxiCjSnAGnQt6V-VdCB6t3vlma_yPBqqP_PSRnz7y0yd-XeauzzSI-O9XHTbavf4LSyxtTQ</recordid><startdate>20190601</startdate><enddate>20190601</enddate><creator>Zhou, Xinxiu</creator><creator>Zhou, Yongping</creator><creator>Peng, Cong</creator><creator>Zeng, Fanquan</creator><creator>Song, Xinda</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-2837-7646</orcidid><orcidid>https://orcid.org/0000-0001-9380-1410</orcidid><orcidid>https://orcid.org/0000-0001-7808-9402</orcidid></search><sort><creationdate>20190601</creationdate><title>Sensorless BLDC Motor Commutation Point Detection and Phase Deviation Correction Method</title><author>Zhou, Xinxiu ; Zhou, Yongping ; Peng, Cong ; Zeng, Fanquan ; Song, Xinda</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-ec4ed26a4efcbba93adf754f7d743e0a1d7eabc5d477ffaa3091a14a5238fa1d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Artificial neural networks</topic><topic>Back electromotive force</topic><topic>Brushless dc (BLdc) motor</topic><topic>Brushless motors</topic><topic>Buck converters</topic><topic>Commutation</topic><topic>commutation signal</topic><topic>D C motors</topic><topic>Electric potential</topic><topic>Electromotive forces</topic><topic>Error correction</topic><topic>Error detection</topic><topic>Fuzzy logic</topic><topic>fuzzy neural network (FNN)</topic><topic>Fuzzy neural networks</topic><topic>Harmonic analysis</topic><topic>Inverters</topic><topic>Low pass filters</topic><topic>Neural networks</topic><topic>Phase deviation</topic><topic>Phase error</topic><topic>Product design</topic><topic>sensorless motor</topic><topic>Voltage control</topic><topic>zero-crossing points (ZCPs) detection</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Xinxiu</creatorcontrib><creatorcontrib>Zhou, Yongping</creatorcontrib><creatorcontrib>Peng, Cong</creatorcontrib><creatorcontrib>Zeng, Fanquan</creatorcontrib><creatorcontrib>Song, Xinda</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on power electronics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhou, Xinxiu</au><au>Zhou, Yongping</au><au>Peng, Cong</au><au>Zeng, Fanquan</au><au>Song, Xinda</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sensorless BLDC Motor Commutation Point Detection and Phase Deviation Correction Method</atitle><jtitle>IEEE transactions on power electronics</jtitle><stitle>TPEL</stitle><date>2019-06-01</date><risdate>2019</risdate><volume>34</volume><issue>6</issue><spage>5880</spage><epage>5892</epage><pages>5880-5892</pages><issn>0885-8993</issn><eissn>1941-0107</eissn><coden>ITPEE8</coden><abstract>Phase-to-neutral voltage or neutral-to-virtual neutral voltage zero-crossing points (ZCPs) detection method is usually used for sensorless brushless dc motor commutation control. Unfortunately, neither of them can be realized in lower speed range. In this paper, a simple commutation point detection method is proposed based on detecting inactive phase terminal to dc-link midpoint voltage. It eliminates the requirement of neutral wire or virtual neutral voltage and provides an amplified version of back electromotive force at the ZCPs which makes the lower speed range detection possible. As the speed increases, commutation point error is enlarged due to the low-pass filter. Utilizing the symmetry of the terminal to midpoint voltage, the phase error can be corrected. However, due to the nonlinear relationship between the detected voltage difference and phase error, it is difficult to regulate the error fast and robustly. Therefore, a novel phase regulator based on fuzzy neural network is proposed in this paper with simple structure and learning ability. The validity of the proposed ZCPs detection method and commutation instant shift correction method are verified through experimental results.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPEL.2018.2867615</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-2837-7646</orcidid><orcidid>https://orcid.org/0000-0001-9380-1410</orcidid><orcidid>https://orcid.org/0000-0001-7808-9402</orcidid></addata></record> |
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subjects | Artificial neural networks Back electromotive force Brushless dc (BLdc) motor Brushless motors Buck converters Commutation commutation signal D C motors Electric potential Electromotive forces Error correction Error detection Fuzzy logic fuzzy neural network (FNN) Fuzzy neural networks Harmonic analysis Inverters Low pass filters Neural networks Phase deviation Phase error Product design sensorless motor Voltage control zero-crossing points (ZCPs) detection |
title | Sensorless BLDC Motor Commutation Point Detection and Phase Deviation Correction Method |
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