Model Predictive Current Control With Online Parameter Estimation for Synchronous Reluctance Machine Controlled by High-Frequency Signal Injection Position-Sensorless
Accurate machine parameters and rotor position information are essential in vector-controlled motor drive systems. However, machine parameter variations by various factors such as the current and the temperature degrade the performance of vector control. Also, a position sensor such as an encoder an...
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| Veröffentlicht in: | IEEE access 2022, Vol.10, p.25267-25277 |
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| creator | Kim, Hyeon-Seong Lee, Kibok |
| description | Accurate machine parameters and rotor position information are essential in vector-controlled motor drive systems. However, machine parameter variations by various factors such as the current and the temperature degrade the performance of vector control. Also, a position sensor such as an encoder and a resolver increases the drive system cost. This paper proposes model predictive current control (MPCC) with the online parameter estimation for synchronous reluctance machines controlled by a high-frequency signal injection position-sensorless method. This approach removes the need for accurate knowledge about the system and eliminates the need for the position sensor. The proposed method adopts a recursive least-square (RLS) to estimate the electrical machine parameters in real-time. The estimated parameters are used for the deadbeat continuous control set (CCS) MPCC and the position-sensorless control. The high-frequency signal injection method is modified to be suitable for the proposed CCS-MPCC method, ensuring stable operation in the low-speed regions. Simulation and experimental results are provided to verify the performance of the proposed control method. |
| doi_str_mv | 10.1109/ACCESS.2022.3156694 |
| format | Article |
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However, machine parameter variations by various factors such as the current and the temperature degrade the performance of vector control. Also, a position sensor such as an encoder and a resolver increases the drive system cost. This paper proposes model predictive current control (MPCC) with the online parameter estimation for synchronous reluctance machines controlled by a high-frequency signal injection position-sensorless method. This approach removes the need for accurate knowledge about the system and eliminates the need for the position sensor. The proposed method adopts a recursive least-square (RLS) to estimate the electrical machine parameters in real-time. The estimated parameters are used for the deadbeat continuous control set (CCS) MPCC and the position-sensorless control. The high-frequency signal injection method is modified to be suitable for the proposed CCS-MPCC method, ensuring stable operation in the low-speed regions. Simulation and experimental results are provided to verify the performance of the proposed control method.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2022.3156694</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Coders ; Control methods ; Covariance matrices ; Directional control ; high-frequency signal injection ; Low speed ; Mathematical models ; Model predictive current control (MPCC) ; Parameter estimation ; Performance degradation ; Position sensing ; position-sensorless ; Predictive control ; Predictive models ; recursive-least square (RLS) ; Reluctance machinery ; Resolvers ; Rotors ; Signal injection ; Stators ; Switches ; synchronous reluctance machines (SynRM) ; Voltage control ; Voltage measurement</subject><ispartof>IEEE access, 2022, Vol.10, p.25267-25277</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-38f627e626fa2cd5c7e794a084082f67b44c2a7be83bab88b02ef33bf7ad0b0f3</citedby><cites>FETCH-LOGICAL-c408t-38f627e626fa2cd5c7e794a084082f67b44c2a7be83bab88b02ef33bf7ad0b0f3</cites><orcidid>0000-0002-9336-1408 ; 0000-0002-9160-8876</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9727167$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Kim, Hyeon-Seong</creatorcontrib><creatorcontrib>Lee, Kibok</creatorcontrib><title>Model Predictive Current Control With Online Parameter Estimation for Synchronous Reluctance Machine Controlled by High-Frequency Signal Injection Position-Sensorless</title><title>IEEE access</title><addtitle>Access</addtitle><description>Accurate machine parameters and rotor position information are essential in vector-controlled motor drive systems. However, machine parameter variations by various factors such as the current and the temperature degrade the performance of vector control. Also, a position sensor such as an encoder and a resolver increases the drive system cost. This paper proposes model predictive current control (MPCC) with the online parameter estimation for synchronous reluctance machines controlled by a high-frequency signal injection position-sensorless method. This approach removes the need for accurate knowledge about the system and eliminates the need for the position sensor. The proposed method adopts a recursive least-square (RLS) to estimate the electrical machine parameters in real-time. The estimated parameters are used for the deadbeat continuous control set (CCS) MPCC and the position-sensorless control. The high-frequency signal injection method is modified to be suitable for the proposed CCS-MPCC method, ensuring stable operation in the low-speed regions. Simulation and experimental results are provided to verify the performance of the proposed control method.</description><subject>Coders</subject><subject>Control methods</subject><subject>Covariance matrices</subject><subject>Directional control</subject><subject>high-frequency signal injection</subject><subject>Low speed</subject><subject>Mathematical models</subject><subject>Model predictive current control (MPCC)</subject><subject>Parameter estimation</subject><subject>Performance degradation</subject><subject>Position sensing</subject><subject>position-sensorless</subject><subject>Predictive control</subject><subject>Predictive models</subject><subject>recursive-least square (RLS)</subject><subject>Reluctance machinery</subject><subject>Resolvers</subject><subject>Rotors</subject><subject>Signal injection</subject><subject>Stators</subject><subject>Switches</subject><subject>synchronous reluctance machines (SynRM)</subject><subject>Voltage control</subject><subject>Voltage measurement</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkVFr2zAUhc1YYaXrL-iLYM_OZMm25Mdi0jXQ0jB37FFI8lWi4EqdJA_yh_o7p9ShTC-6XJ3zicMpipsKr6oKd99v-349DCuCCVnRqmnbrv5UXJKq7Ura0Pbzf_OX4jrGA86H51XDLou3Rz_ChLYBRquT_Quon0MAl1DvXQp-Qr9t2qMnN1kHaCuDfIEEAa1jsi8yWe-Q8QENR6f3wTs_R_QTplkn6TSgR6n3J9-ZNcGI1BHd292-vAvwZwanj2iwOycntHEH0O_ArY_2NJQDuOjDBDF-LS6MnCJcn--r4tfd-rm_Lx-efmz624dS15inknLTEgYtaY0kemw0A9bVEvP8SkzLVF1rIpkCTpVUnCtMwFCqDJMjVtjQq2KzcEcvD-I15IzhKLy04n3hw07IkKyeQLCaG8yZorU0dUVHqWiT6ZnCK0koyaxvC-s1-Bw1JnHwc8hRoyAt5VXWMJZVdFHp4GMMYD5-rbA49SuWfsWpX3HuN7tuFpcFgA9HxwirWkb_AZl6pQA</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Kim, Hyeon-Seong</creator><creator>Lee, Kibok</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-9336-1408</orcidid><orcidid>https://orcid.org/0000-0002-9160-8876</orcidid></search><sort><creationdate>2022</creationdate><title>Model Predictive Current Control With Online Parameter Estimation for Synchronous Reluctance Machine Controlled by High-Frequency Signal Injection Position-Sensorless</title><author>Kim, Hyeon-Seong ; Lee, Kibok</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-38f627e626fa2cd5c7e794a084082f67b44c2a7be83bab88b02ef33bf7ad0b0f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Coders</topic><topic>Control methods</topic><topic>Covariance matrices</topic><topic>Directional control</topic><topic>high-frequency signal injection</topic><topic>Low speed</topic><topic>Mathematical models</topic><topic>Model predictive current control (MPCC)</topic><topic>Parameter estimation</topic><topic>Performance degradation</topic><topic>Position sensing</topic><topic>position-sensorless</topic><topic>Predictive control</topic><topic>Predictive models</topic><topic>recursive-least square (RLS)</topic><topic>Reluctance machinery</topic><topic>Resolvers</topic><topic>Rotors</topic><topic>Signal injection</topic><topic>Stators</topic><topic>Switches</topic><topic>synchronous reluctance machines (SynRM)</topic><topic>Voltage control</topic><topic>Voltage measurement</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Hyeon-Seong</creatorcontrib><creatorcontrib>Lee, Kibok</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE Xplore Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Hyeon-Seong</au><au>Lee, Kibok</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Model Predictive Current Control With Online Parameter Estimation for Synchronous Reluctance Machine Controlled by High-Frequency Signal Injection Position-Sensorless</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2022</date><risdate>2022</risdate><volume>10</volume><spage>25267</spage><epage>25277</epage><pages>25267-25277</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>Accurate machine parameters and rotor position information are essential in vector-controlled motor drive systems. However, machine parameter variations by various factors such as the current and the temperature degrade the performance of vector control. Also, a position sensor such as an encoder and a resolver increases the drive system cost. This paper proposes model predictive current control (MPCC) with the online parameter estimation for synchronous reluctance machines controlled by a high-frequency signal injection position-sensorless method. This approach removes the need for accurate knowledge about the system and eliminates the need for the position sensor. The proposed method adopts a recursive least-square (RLS) to estimate the electrical machine parameters in real-time. The estimated parameters are used for the deadbeat continuous control set (CCS) MPCC and the position-sensorless control. The high-frequency signal injection method is modified to be suitable for the proposed CCS-MPCC method, ensuring stable operation in the low-speed regions. 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| subjects | Coders Control methods Covariance matrices Directional control high-frequency signal injection Low speed Mathematical models Model predictive current control (MPCC) Parameter estimation Performance degradation Position sensing position-sensorless Predictive control Predictive models recursive-least square (RLS) Reluctance machinery Resolvers Rotors Signal injection Stators Switches synchronous reluctance machines (SynRM) Voltage control Voltage measurement |
| title | Model Predictive Current Control With Online Parameter Estimation for Synchronous Reluctance Machine Controlled by High-Frequency Signal Injection Position-Sensorless |
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