Hybrid optimal control feedforward neural network-super twisting sliding mode algorithm applied of two series-connected multi-phase PMSM using neural SVPWM algorithm

Hybrid optimal control feedforward neural network-super twisting sliding mode algorithm applied of two series-connected multi-phase PMSM using neural SVPWM algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hybrid optimal control feedforward neural network-super twisting sliding mode algorithm applied of two series-connected multi-phase PMSM using neural SVPWM algorithm Fayçal Mehedi, Ismail Bouyakoub, Abdelkader Yousfi, Zakaria Reguieg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7049092/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Multiphase motors enable independent control of several motors connected in series using single inverter. Effective control of these multi-machine systems (MMS) is crucial, considering factors like torque and speed. While vector control is a common method, its reliance on conventional proportional-integral (PI) controllers makes it vulnerable to performance degradation when system parameters fluctuate, highlighting the need for accurate modeling, robust drives, and advanced control. In this context, this study proposes an advanced super-twisting sliding mode control with neural network algorithm (NSTSMC) approach for controlling two series-connected five-phase permanent magnet synchronous motors (5Ph-PMSMs) powered by a single inverter. While conventional sliding mode control (SMC) is effective for nonlinear systems, it suffers from steady-state inaccuracies and chattering. The NSTSMC design enhances system robustness and significantly reduces chattering. Additionally, a constant switching frequency is achieved using a modified neural-space vector pulse width modulation (NSVPWM) for the inverter. Digital simulations in MATLAB confirmed the proposed technique's efficacy, demonstrating its superior ability to improve system features compared to conventional and SMC-SVPWM techniques. The NSTSMC-NSVPWM technique significantly improved the performance of two 5Ph-PMSMs. For 5Ph-PMSM1, it cut response time by 45.45% compared to SMC-SVPWM technique and 89.09% over conventional methods. 5Ph-PMSM2 saw similar gains, with response time reductions of 47.27% (SMC-SVPWM technique) and 93.09% (MMS-PI Method). Additionally, the proposed technique substantially reduced torque ripple in both machines: 23.52% and 77.58% for 5Ph-PMSM1, and 21.87% and 78.26% for 5Ph-PMSM2, against conventional and SMC-SVPWM methods, respectively. This proposed control also offers robust performance against parameter variations. Five-phase PMSM series-connected sliding mode control feedforward neural network super-twisting sliding mode control Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 20 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 18 Oct, 2025 Reviewers invited by journal 13 Jul, 2025 Editor assigned by journal 06 Jul, 2025 Submission checks completed at journal 06 Jul, 2025 First submitted to journal 04 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7049092","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486061283,"identity":"b83a0ffe-4a70-443d-940a-ae58447a3299","order_by":0,"name":"Fayçal Mehedi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIie3RsUrDQBzH8V84SAb/Z9eTCr7CQcCl0L5KQoYusQouHRwCQpzENUJfQgp1vXDg1AfoWOjaoeCoFP/BbsKfjA735W64gw9/uANCoX9YUgGKFw344Hhn54i2IIGQO5GL6kRiKNuLwLrfix4EZ/nubj66TDc3rSeMZ3HyCOy/JaKXabOe0vVmljEp7mPyiBbCmAn0aqhrz6S07evR5bW5dYqMOOX9Sx89pQ2TBkyudvwaViQrpStP1pTWHTpiFJNMIEovh/QxJbPed6TIayrQLpxAkue3T3oYTQZPZXrIMM5fEh9tpRfrPuVvwoxQKBQK9ekHtJ5HfFmTJJIAAAAASUVORK5CYII=","orcid":"","institution":"Hassiba Benbouali University of Chlef","correspondingAuthor":true,"prefix":"","firstName":"Fayçal","middleName":"","lastName":"Mehedi","suffix":""},{"id":486061284,"identity":"d2d231ef-31d5-45bc-85cc-43ec4223b250","order_by":1,"name":"Ismail Bouyakoub","email":"","orcid":"","institution":"Hassiba Benbouali University of Chlef","correspondingAuthor":false,"prefix":"","firstName":"Ismail","middleName":"","lastName":"Bouyakoub","suffix":""},{"id":486061285,"identity":"27a249d5-2d43-4401-912e-8dbf6ba1da35","order_by":2,"name":"Abdelkader Yousfi","email":"","orcid":"","institution":"Djilali Bounaama University of Khamis Miliana","correspondingAuthor":false,"prefix":"","firstName":"Abdelkader","middleName":"","lastName":"Yousfi","suffix":""},{"id":486061286,"identity":"87497217-481f-49d3-9337-db0c75cc84aa","order_by":3,"name":"Zakaria Reguieg","email":"","orcid":"","institution":"Hassiba Benbouali University of Chlef","correspondingAuthor":false,"prefix":"","firstName":"Zakaria","middleName":"","lastName":"Reguieg","suffix":""}],"badges":[],"createdAt":"2025-07-04 18:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7049092/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7049092/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87054921,"identity":"370e7747-c5df-4373-a856-4992b3497a79","added_by":"auto","created_at":"2025-07-18 15:32:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1935588,"visible":true,"origin":"","legend":"","description":"","filename":"PaperofFayalMehediIJSTTEE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7049092/v1_covered_0760d97d-683e-4e55-a35a-092e16949620.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid optimal control feedforward neural network-super twisting sliding mode algorithm applied of two series-connected multi-phase PMSM using neural SVPWM algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"iranian-journal-of-science-and-technology-transactions-of-electrical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iste","sideBox":"Learn more about [Iranian Journal of Science and Technology, Transactions of Electrical Engineering](https://link.springer.com/journal/40998)","snPcode":"40998","submissionUrl":"https://submission.springernature.com/new-submission/40998/3","title":"Iranian Journal of Science and Technology, Transactions of Electrical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Five-phase PMSM, series-connected, sliding mode control, feedforward neural network, super-twisting sliding mode control","lastPublishedDoi":"10.21203/rs.3.rs-7049092/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7049092/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMultiphase motors enable independent control of several motors connected in series using single inverter. Effective control of these multi-machine systems (MMS) is crucial, considering factors like torque and speed. While vector control is a common method, its reliance on conventional proportional-integral (PI) controllers makes it vulnerable to performance degradation when system parameters fluctuate, highlighting the need for accurate modeling, robust drives, and advanced control. In this context, this study proposes an advanced super-twisting sliding mode control with neural network algorithm (NSTSMC) approach for controlling two series-connected five-phase permanent magnet synchronous motors (5Ph-PMSMs) powered by a single inverter. While conventional sliding mode control (SMC) is effective for nonlinear systems, it suffers from steady-state inaccuracies and chattering. The NSTSMC design enhances system robustness and significantly reduces chattering. Additionally, a constant switching frequency is achieved using a modified neural-space vector pulse width modulation (NSVPWM) for the inverter. Digital simulations in MATLAB confirmed the proposed technique's efficacy, demonstrating its superior ability to improve system features compared to conventional and SMC-SVPWM techniques. The NSTSMC-NSVPWM technique significantly improved the performance of two 5Ph-PMSMs. For 5Ph-PMSM1, it cut response time by 45.45% compared to SMC-SVPWM technique and 89.09% over conventional methods. 5Ph-PMSM2 saw similar gains, with response time reductions of 47.27% (SMC-SVPWM technique) and 93.09% (MMS-PI Method). Additionally, the proposed technique substantially reduced torque ripple in both machines: 23.52% and 77.58% for 5Ph-PMSM1, and 21.87% and 78.26% for 5Ph-PMSM2, against conventional and SMC-SVPWM methods, respectively. This proposed control also offers robust performance against parameter variations.\u003c/p\u003e","manuscriptTitle":"Hybrid optimal control feedforward neural network-super twisting sliding mode algorithm applied of two series-connected multi-phase PMSM using neural SVPWM algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 15:08:08","doi":"10.21203/rs.3.rs-7049092/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-20T08:33:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T15:29:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332464628527227342902861119449841850530","date":"2026-05-09T12:17:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263798093717205884711954857020456942593","date":"2026-04-08T03:17:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17743913825532981200080281423776089905","date":"2026-04-06T09:58:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-02T18:23:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31526870293948003153086127292452023706","date":"2026-04-02T14:40:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188475371465766058044132790328054891818","date":"2025-10-18T21:21:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-13T16:03:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-06T06:50:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-06T06:49:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Iranian Journal of Science and Technology, Transactions of Electrical Engineering","date":"2025-07-04T18:45:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"iranian-journal-of-science-and-technology-transactions-of-electrical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iste","sideBox":"Learn more about [Iranian Journal of Science and Technology, Transactions of Electrical Engineering](https://link.springer.com/journal/40998)","snPcode":"40998","submissionUrl":"https://submission.springernature.com/new-submission/40998/3","title":"Iranian Journal of Science and Technology, Transactions of Electrical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"71ca01e9-640e-419e-ac53-0db1df16e599","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-20T08:33:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T15:29:23+00:00","index":81,"fulltext":""},{"type":"reviewerAgreed","content":"332464628527227342902861119449841850530","date":"2026-05-09T12:17:38+00:00","index":80,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T08:40:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 15:08:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7049092","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7049092","identity":"rs-7049092","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}