Sliding Mode Direct Torque Control of SPMSMs Based on a Hybrid Wolf Optimization Algorithm

Direct torque control has been widely used to control surface-mounted permanent magnet synchronous motors (SPMSMs). To reduce the torque ripple and improve the flux tracking accuracy of SPMSM drives, sliding mode direct torque control (SMDTC) was developed. However, its optimal performance is hardly...

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Vydáno v:	IEEE transactions on industrial electronics (1982) Ročník 69; číslo 5; s. 4534 - 4544
Hlavní autoři:	Jin, Zhijia, Sun, Xiaodong, Lei, Gang, Guo, Youguang, Zhu, Jianguo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.05.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Control methods Control surfaces Convergence Coyote optimization algorithm (COA) grey wolf optimization algorithm (GWOA) Indexes Optimization Optimization algorithms Parameters permanent magnet synchronous motors Permanent magnets Ripples Sliding mode control sliding mode direct torque control (SMDTC) Sun Synchronous motors Torque Torque control Torque measurement Tracking
ISSN:	0278-0046, 1557-9948
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Abstract	Direct torque control has been widely used to control surface-mounted permanent magnet synchronous motors (SPMSMs). To reduce the torque ripple and improve the flux tracking accuracy of SPMSM drives, sliding mode direct torque control (SMDTC) was developed. However, its optimal performance is hardly obtained by trial and error tuning of the control parameters. Hence, a hybrid wolf optimization algorithm (HWOA) is proposed to automatically adjust the controller's parameters of SMDTC for SPMSMs in this article. This algorithm combines the grey wolf optimization algorithm and coyote optimization algorithm. A conversion probability is designed to use them simultaneously. The proposed HWOA holds the advantages of the two algorithms. It converges very fast and can avoid local optimums effectively. Furthermore, a special fitness index with penalty terms is designed to enhance flux tracking accuracy and reduce the torque ripple of SPMSM drives. The superiority of the proposed control method is verified by an experiment.
AbstractList	Direct torque control has been widely used to control surface-mounted permanent magnet synchronous motors (SPMSMs). To reduce the torque ripple and improve the flux tracking accuracy of SPMSM drives, sliding mode direct torque control (SMDTC) was developed. However, its optimal performance is hardly obtained by trial and error tuning of the control parameters. Hence, a hybrid wolf optimization algorithm (HWOA) is proposed to automatically adjust the controller's parameters of SMDTC for SPMSMs in this article. This algorithm combines the grey wolf optimization algorithm and coyote optimization algorithm. A conversion probability is designed to use them simultaneously. The proposed HWOA holds the advantages of the two algorithms. It converges very fast and can avoid local optimums effectively. Furthermore, a special fitness index with penalty terms is designed to enhance flux tracking accuracy and reduce the torque ripple of SPMSM drives. The superiority of the proposed control method is verified by an experiment.
Author	Zhu, Jianguo Lei, Gang Guo, Youguang Jin, Zhijia Sun, Xiaodong
Author_xml	– sequence: 1 givenname: Zhijia surname: Jin fullname: Jin, Zhijia email: 2111904009@stmail.ujs.edu.cn organization: Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China – sequence: 2 givenname: Xiaodong orcidid: 0000-0002-9451-3311 surname: Sun fullname: Sun, Xiaodong email: xdsun@ujs.edu.cn organization: Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China – sequence: 3 givenname: Gang orcidid: 0000-0002-8369-6802 surname: Lei fullname: Lei, Gang email: gang.lei@uts.edu.au organization: School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW, Australia – sequence: 4 givenname: Youguang orcidid: 0000-0001-6182-0684 surname: Guo fullname: Guo, Youguang email: youguang.guo-1@uts.edu.au organization: School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW, Australia – sequence: 5 givenname: Jianguo orcidid: 0000-0002-9763-4047 surname: Zhu fullname: Zhu, Jianguo email: jianguo.zhu@sydney.edu.au organization: School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, Australia
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Cites_doi	10.1109/TTE.2020.3031338 10.1109/41.937422 10.1109/TPEL.2020.2994254 10.1109/TPEL.2019.2923726 10.1109/TIE.2017.2677346 10.1109/TIE.2020.2984433 10.1080/15325008.2015.1041625 10.1109/TIA.2016.2599902 10.1109/ACCESS.2020.3000770 10.1109/PEDES.2016.7914260 10.1109/TVT.2020.2981422 10.1109/CEC.2018.8477769 10.1109/TIE.2016.2607698 10.1109/ACCESS.2018.2864303 10.1109/ICETEESES.2016.7581394 10.1016/j.asoc.2015.09.045 10.1007/s00500-018-3310-y 10.1016/j.advengsoft.2013.12.007 10.1109/TPEL.2013.2262953 10.1109/TEC.2020.2989438 10.1109/TIE.2016.2569067 10.1109/TIE.2015.2426678 10.1049/iet-epa.2019.0383 10.1109/JSEN.2018.2822712 10.1109/TIE.2019.2947875 10.1109/TEC.2009.2036249 10.1109/COMPUMAG45669.2019.9032768 10.1109/TPEL.2021.3063226 10.1109/TPEL.2017.2694450 10.1109/TTE.2021.3052986
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Snippet	Direct torque control has been widely used to control surface-mounted permanent magnet synchronous motors (SPMSMs). To reduce the torque ripple and improve the...
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SubjectTerms	Algorithms Control methods Control surfaces Convergence Coyote optimization algorithm (COA) grey wolf optimization algorithm (GWOA) Indexes Optimization Optimization algorithms Parameters permanent magnet synchronous motors Permanent magnets Ripples Sliding mode control sliding mode direct torque control (SMDTC) Sun Synchronous motors Torque Torque control Torque measurement Tracking
Title	Sliding Mode Direct Torque Control of SPMSMs Based on a Hybrid Wolf Optimization Algorithm
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