Improved Iterative Learning Direct Torque Control for Torque Ripple Minimization of Surface-Mounted Permanent Magnet Synchronous Motor Drives
This article presents an improved iterative learning direct torque control (IL-DTC) to remarkably minimize the torque ripples for a surface-mounted permanent magnet synchronous motor (SPMSM) drive. Unlike the conventional IL-DTC, the proposed IL-DTC significantly attenuates the torque ripples by eff...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2021-11, Vol.17 (11), p.7291-7303 |
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| creator | Mohammed, Sadeq Ali Qasem Choi, Han Ho Jung, Jin-Woo |
| description | This article presents an improved iterative learning direct torque control (IL-DTC) to remarkably minimize the torque ripples for a surface-mounted permanent magnet synchronous motor (SPMSM) drive. Unlike the conventional IL-DTC, the proposed IL-DTC significantly attenuates the torque ripples by effectively suppressing the repetitive disturbances using the speed and load torque compensating terms in the improved error dynamics via the improved feedback control terms and iterative learning control terms. Further, it has a simple structure and fast dynamic response due to the direct control of the torque and flux. The stability is verified through the convergence of speed errors to zero as the iteration index goes to infinity. The comparative results via MATLAB/Simulink and a prototype SPMSM test-bed with TI-TMS320F28335-DSP demonstrate the improved control performance (e.g., less torque ripples, faster transient response, smaller overshoot/undershoot, and smaller steady-state error) over the conventional IL-DTC under critical load/speed conditions with severe model parameter uncertainties. |
| doi_str_mv | 10.1109/TII.2021.3053700 |
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Unlike the conventional IL-DTC, the proposed IL-DTC significantly attenuates the torque ripples by effectively suppressing the repetitive disturbances using the speed and load torque compensating terms in the improved error dynamics via the improved feedback control terms and iterative learning control terms. Further, it has a simple structure and fast dynamic response due to the direct control of the torque and flux. The stability is verified through the convergence of speed errors to zero as the iteration index goes to infinity. The comparative results via MATLAB/Simulink and a prototype SPMSM test-bed with TI-TMS320F28335-DSP demonstrate the improved control performance (e.g., less torque ripples, faster transient response, smaller overshoot/undershoot, and smaller steady-state error) over the conventional IL-DTC under critical load/speed conditions with severe model parameter uncertainties.</description><identifier>ISSN: 1551-3203</identifier><identifier>EISSN: 1941-0050</identifier><identifier>DOI: 10.1109/TII.2021.3053700</identifier><identifier>CODEN: ITIICH</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Control stability ; Direct torque control (DTC) ; Dynamic response ; Feedback control ; Harmonic analysis ; iterative learning control (ILC) ; Iterative methods ; Learning ; Mathematical model ; Parameter uncertainty ; Permanent magnet motors ; Permanent magnets ; repetitive disturbances ; Ripples ; Stators ; surface-mounted permanent magnet synchronous motor (SPMSM) ; Synchronous motors ; Torque ; Torque control ; torque ripple minimization (TRM) ; Traction motors ; Transient response</subject><ispartof>IEEE transactions on industrial informatics, 2021-11, Vol.17 (11), p.7291-7303</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c287t-2f8b9537ee80686d87faff15a8615edf5f1165b5e751424f27d16cae4bd8c1e93</citedby><cites>FETCH-LOGICAL-c287t-2f8b9537ee80686d87faff15a8615edf5f1165b5e751424f27d16cae4bd8c1e93</cites><orcidid>0000-0002-1820-6096 ; 0000-0003-3429-5049 ; 0000-0003-0940-9876</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9334416$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9334416$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Mohammed, Sadeq Ali Qasem</creatorcontrib><creatorcontrib>Choi, Han Ho</creatorcontrib><creatorcontrib>Jung, Jin-Woo</creatorcontrib><title>Improved Iterative Learning Direct Torque Control for Torque Ripple Minimization of Surface-Mounted Permanent Magnet Synchronous Motor Drives</title><title>IEEE transactions on industrial informatics</title><addtitle>TII</addtitle><description>This article presents an improved iterative learning direct torque control (IL-DTC) to remarkably minimize the torque ripples for a surface-mounted permanent magnet synchronous motor (SPMSM) drive. Unlike the conventional IL-DTC, the proposed IL-DTC significantly attenuates the torque ripples by effectively suppressing the repetitive disturbances using the speed and load torque compensating terms in the improved error dynamics via the improved feedback control terms and iterative learning control terms. Further, it has a simple structure and fast dynamic response due to the direct control of the torque and flux. The stability is verified through the convergence of speed errors to zero as the iteration index goes to infinity. The comparative results via MATLAB/Simulink and a prototype SPMSM test-bed with TI-TMS320F28335-DSP demonstrate the improved control performance (e.g., less torque ripples, faster transient response, smaller overshoot/undershoot, and smaller steady-state error) over the conventional IL-DTC under critical load/speed conditions with severe model parameter uncertainties.</description><subject>Control stability</subject><subject>Direct torque control (DTC)</subject><subject>Dynamic response</subject><subject>Feedback control</subject><subject>Harmonic analysis</subject><subject>iterative learning control (ILC)</subject><subject>Iterative methods</subject><subject>Learning</subject><subject>Mathematical model</subject><subject>Parameter uncertainty</subject><subject>Permanent magnet motors</subject><subject>Permanent magnets</subject><subject>repetitive disturbances</subject><subject>Ripples</subject><subject>Stators</subject><subject>surface-mounted permanent magnet synchronous motor (SPMSM)</subject><subject>Synchronous motors</subject><subject>Torque</subject><subject>Torque control</subject><subject>torque ripple minimization (TRM)</subject><subject>Traction motors</subject><subject>Transient response</subject><issn>1551-3203</issn><issn>1941-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kN1LwzAUxYsoOKfvgi8Bnzvz0aTto8yvwori5nPJ2puZsSY1TQfzf_B_NmPTp3u5_M65nBNF1wRPCMH53aIoJhRTMmGYsxTjk2hE8oTEGHN8GnbOScwoZufRRd-vMQ4My0fRT9F2zm6hQYUHJ73eApqBdEabFXrQDmqPFtZ9DYCm1nhnN0hZ93d61123AVRqo1v9HdTWIKvQfHBK1hCXdjA-WL-Ba6UB41EpVwY8mu9M_emssUOPSuuD4YMLn_vL6EzJTQ9XxzmOPp4eF9OXePb6XEzvZ3FNs9THVGXLPKQEyLDIRJOlSipFuMwE4dAorggRfMkh5SShiaJpQ0QtIVk2WU0gZ-Po9uAbsoccva_WdnAmvKwo5yLJRMr2FD5QtbN970BVndOtdLuK4GpfehVKr_alV8fSg-TmINEA8I_njCUJEewXYZGAPg</recordid><startdate>20211101</startdate><enddate>20211101</enddate><creator>Mohammed, Sadeq Ali Qasem</creator><creator>Choi, Han Ho</creator><creator>Jung, Jin-Woo</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>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-1820-6096</orcidid><orcidid>https://orcid.org/0000-0003-3429-5049</orcidid><orcidid>https://orcid.org/0000-0003-0940-9876</orcidid></search><sort><creationdate>20211101</creationdate><title>Improved Iterative Learning Direct Torque Control for Torque Ripple Minimization of Surface-Mounted Permanent Magnet Synchronous Motor Drives</title><author>Mohammed, Sadeq Ali Qasem ; Choi, Han Ho ; Jung, Jin-Woo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c287t-2f8b9537ee80686d87faff15a8615edf5f1165b5e751424f27d16cae4bd8c1e93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Control stability</topic><topic>Direct torque control (DTC)</topic><topic>Dynamic response</topic><topic>Feedback control</topic><topic>Harmonic analysis</topic><topic>iterative learning control (ILC)</topic><topic>Iterative methods</topic><topic>Learning</topic><topic>Mathematical model</topic><topic>Parameter uncertainty</topic><topic>Permanent magnet motors</topic><topic>Permanent magnets</topic><topic>repetitive disturbances</topic><topic>Ripples</topic><topic>Stators</topic><topic>surface-mounted permanent magnet synchronous motor (SPMSM)</topic><topic>Synchronous motors</topic><topic>Torque</topic><topic>Torque control</topic><topic>torque ripple minimization (TRM)</topic><topic>Traction motors</topic><topic>Transient response</topic><toplevel>online_resources</toplevel><creatorcontrib>Mohammed, Sadeq Ali Qasem</creatorcontrib><creatorcontrib>Choi, Han Ho</creatorcontrib><creatorcontrib>Jung, Jin-Woo</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>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology 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><jtitle>IEEE transactions on industrial informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mohammed, Sadeq Ali Qasem</au><au>Choi, Han Ho</au><au>Jung, Jin-Woo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved Iterative Learning Direct Torque Control for Torque Ripple Minimization of Surface-Mounted Permanent Magnet Synchronous Motor Drives</atitle><jtitle>IEEE transactions on industrial informatics</jtitle><stitle>TII</stitle><date>2021-11-01</date><risdate>2021</risdate><volume>17</volume><issue>11</issue><spage>7291</spage><epage>7303</epage><pages>7291-7303</pages><issn>1551-3203</issn><eissn>1941-0050</eissn><coden>ITIICH</coden><abstract>This article presents an improved iterative learning direct torque control (IL-DTC) to remarkably minimize the torque ripples for a surface-mounted permanent magnet synchronous motor (SPMSM) drive. Unlike the conventional IL-DTC, the proposed IL-DTC significantly attenuates the torque ripples by effectively suppressing the repetitive disturbances using the speed and load torque compensating terms in the improved error dynamics via the improved feedback control terms and iterative learning control terms. Further, it has a simple structure and fast dynamic response due to the direct control of the torque and flux. The stability is verified through the convergence of speed errors to zero as the iteration index goes to infinity. The comparative results via MATLAB/Simulink and a prototype SPMSM test-bed with TI-TMS320F28335-DSP demonstrate the improved control performance (e.g., less torque ripples, faster transient response, smaller overshoot/undershoot, and smaller steady-state error) over the conventional IL-DTC under critical load/speed conditions with severe model parameter uncertainties.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TII.2021.3053700</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-1820-6096</orcidid><orcidid>https://orcid.org/0000-0003-3429-5049</orcidid><orcidid>https://orcid.org/0000-0003-0940-9876</orcidid></addata></record> |
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| ispartof | IEEE transactions on industrial informatics, 2021-11, Vol.17 (11), p.7291-7303 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Control stability Direct torque control (DTC) Dynamic response Feedback control Harmonic analysis iterative learning control (ILC) Iterative methods Learning Mathematical model Parameter uncertainty Permanent magnet motors Permanent magnets repetitive disturbances Ripples Stators surface-mounted permanent magnet synchronous motor (SPMSM) Synchronous motors Torque Torque control torque ripple minimization (TRM) Traction motors Transient response |
| title | Improved Iterative Learning Direct Torque Control for Torque Ripple Minimization of Surface-Mounted Permanent Magnet Synchronous Motor Drives |
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