Sensorless Vector Control Of Three-Phase Induction Motor to Crush Sugar Cane

Sensorless Vector Control Of Three-Phase Induction Motor to Crush Sugar Cane | 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 Sensorless Vector Control Of Three-Phase Induction Motor to Crush Sugar Cane Sime Gonfa Heyi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6725108/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper presents an adaptive neuro-fuzzy inference system sensorless speed estimation for IM drives is designed and simulated, together with fuzzy-PI optimized by GA to crush sugarcane. In previous years, the identification and monitoring of this highly nonlinear dynamic plant was the focus of research efforts in the field of IM control. Speed sensors are needed by existing vector control methods for field orientation and control, but their installation makes the drive system bulky, unreliable, and expensive, and installing them might not be possible in some applications, like high-speed drives or motor drives in hostile environments. In these scenarios, speed is determined using stator quantities that are simple to measure. To address the speed sensing issue, numerous speed sensorless approaches have been developed. Developed speed estimation algorithms require a lot of computing time and/or depend on various parameters. The proposed estimation method in this thesis uses ANFIS to get the speed signal. A fuzzy PI controller is used in place of the traditional PI controller. A hybrid learning technique is utilized to train the ANFIS, which is used as an estimator. When the motor drive is operating in a closed loop at different values of speeds and loads for speed observation, the data used for training is obtained using conventional FOC simulations. The use of MATLAB®2020a is used to model the entire drive system. Last but not least, the drive results have been examined for both steady-state and dynamic settings, including sensitivity to motor parameter uncertainty, tracking the drive's speed set points, quick torque response, low-speed behavior, and step response with speed reversal. For the transient response, speed tracking, and low-speed operation, the simulation result error between actual and estimated speed has been less than 0.1%, 0.5%, and 0.2%, respectively. According to simulation data, the overshot and settling time are reduced by 0.0504% and 0.14 seconds at full, respectively, when employing fuzzy-PI compared to PI controllers for the reference speed of 60 rad/sec. The fuzzy-PI controller also produces a robust response. Electrical Engineering Adaptive Neural-Fuzzy Inference Systems (ANFIS) Fuzzy-PI Controller (FPI) Genetic Algorithms (GA) Induction Motor (IM) Full Text Additional Declarations The authors declare no competing interests. Supplementary Files ESTimateddata.xlsxSheet1.pdf Estimated simulated data Cite Share Download PDF Status: Posted Version 1 posted 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-6725108","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":460445217,"identity":"669f4c2f-fcb4-4c87-a6ef-849c1c0850b2","order_by":0,"name":"Sime Gonfa Heyi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBAC+WYog72B+RgzmHWAgBaDw1AGzwG2NIQWfNoMYAyeAzxmRGph5058wPDLJo+H/cy3x4VtDHJ8NxIYH3/Ao0W+mXezAWNfWjEPT+5245ltDMaSNxKYDfD65zDvNgnGnsOJ+xlyt0nztjEkbriRwCZBQMv2HyAtPfxvnoG01AO1sP8gZAsDww+gFokcNpCWBAOgLfi9f5h3s0RiQxpQyzNzY55zEoYzzzxsljiDR4t8/9mNHz78sQE6LPnZY54yG3m+48kHP1TgcxgIJLbBmRJAzNhASAMQ/CFCzSgYBaNgFIxcAAB/0FD6vLzw1gAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0004-8187-4064","institution":"Bule Hora University","correspondingAuthor":true,"prefix":"","firstName":"Sime","middleName":"Gonfa","lastName":"Heyi","suffix":""}],"badges":[],"createdAt":"2025-05-22 12:44:44","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6725108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6725108/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83354084,"identity":"3abc796c-89ec-47ab-9b66-397bf43b4d96","added_by":"auto","created_at":"2025-05-23 14:39:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1336646,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchArticle2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6725108/v1_covered_37d96e1c-85d1-4f54-8150-c4794f0b27fd.pdf"},{"id":83352363,"identity":"1efda241-c42c-4c78-b32b-592d6ecb412c","added_by":"auto","created_at":"2025-05-23 14:23:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1749948,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated simulated data\u003c/p\u003e","description":"","filename":"ESTimateddata.xlsxSheet1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6725108/v1/0e412335834c35d432557455.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSensorless Vector Control Of Three-Phase Induction Motor to Crush Sugar Cane\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Bule Hora University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adaptive Neural-Fuzzy Inference Systems (ANFIS), Fuzzy-PI Controller (FPI), Genetic Algorithms (GA), Induction Motor (IM)","lastPublishedDoi":"10.21203/rs.3.rs-6725108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6725108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents an adaptive neuro-fuzzy inference system sensorless speed estimation for IM drives is designed and simulated, together with fuzzy-PI optimized by GA to crush sugarcane. In previous years, the identification and monitoring of this highly nonlinear dynamic plant was the focus of research efforts in the field of IM control. Speed sensors are needed by existing vector control methods for field orientation and control, but their installation makes the drive system bulky, unreliable, and expensive, and installing them might not be possible in some applications, like high-speed drives or motor drives in hostile environments. In these scenarios, speed is determined using stator quantities that are simple to measure. To address the speed sensing issue, numerous speed sensorless approaches have been developed. Developed speed estimation algorithms require a lot of computing time and/or depend on various parameters. The proposed estimation method in this thesis uses ANFIS to get the speed signal. A fuzzy PI controller is used in place of the traditional PI controller. A hybrid learning technique is utilized to train the ANFIS, which is used as an estimator. When the motor drive is operating in a closed loop at different values of speeds and loads for speed observation, the data used for training is obtained using conventional FOC simulations. The use of MATLAB\u0026reg;2020a is used to model the entire drive system.\u003c/p\u003e \u003cp\u003eLast but not least, the drive results have been examined for both steady-state and dynamic settings, including sensitivity to motor parameter uncertainty, tracking the drive's speed set points, quick torque response, low-speed behavior, and step response with speed reversal. For the transient response, speed tracking, and low-speed operation, the simulation result error between actual and estimated speed has been less than 0.1%, 0.5%, and 0.2%, respectively. According to simulation data, the overshot and settling time are reduced by 0.0504% and 0.14 seconds at full, respectively, when employing fuzzy-PI compared to PI controllers for the reference speed of 60 rad/sec. 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