Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3161
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dc.contributor.authorÜnlütürk, Ali-
dc.contributor.authorAydoğdu, Ömer-
dc.date.accessioned2022-11-28T16:54:43Z-
dc.date.available2022-11-28T16:54:43Z-
dc.date.issued2022-
dc.identifier.issn2169-3536-
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2022.3210540-
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2022.3210540-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/3161-
dc.description.abstractIn this paper, a novel Machine Learning (ML) based Adaptive Fuzzy Logic-Proportional Integral (AFL-PI) controller was developed for the self-balancing and precision motion control of a two wheeled Underactuated-Mobile Inverted Pendulum (U-MIP) under variable payloads. One of the external disturbances in balance and motion control of the U-MIP is the amount of payload it carries on. To investigate the effectiveness of the proposed controller, a load bar was mounted on top of the U-MIP. The weights of 55gr each can be attached to this bar for variable payloads. The weights on the bar were labeled as three different classes: Low Load (LL), Normal Load (NL) and Heavy Load (HL). Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) models were tested to obtain the highest payload class estimation. The highest load classification accuracy was achieved with ANN. Therefore, the ANN model was applied on the U-MIP. The balance performance of the U-MIP was compared by applying the classical FL-PI and ANN based AFL-PI controller on the robot. In order to compare the body tilt angle performance of the U-MIP, the optimal FL-PI parameter in LL was applied for NL and HL conditions without changing. Then, the proposed ANN based AFL-PI controller was implemented on U-MIP. With the proposed novel controller, the body tilt angle variation of the U-MIP was improved by %29.42 for NL and %55.62 for HL compared to the classical FL-PI controller. The validity of the proposed controller was proved by real experiments.en_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Accessen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFuzzy logicen_US
dc.subjectPayloadsen_US
dc.subjectRobot sensing systemsen_US
dc.subjectMobile robotsen_US
dc.subjectArtificial neural networksen_US
dc.subjectMotion controlen_US
dc.subjectMachine learningen_US
dc.subjectMachine learningen_US
dc.subjectadaptive fuzzy logic controlen_US
dc.subjectbalance roboten_US
dc.subjectsensor fusionen_US
dc.subjectDesignen_US
dc.titleMachine Learning Based Self-Balancing and Motion Control of the Underactuated Mobile Inverted Pendulum With Variable Loaden_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ACCESS.2022.3210540-
dc.identifier.scopus2-s2.0-85139449411en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.volume10en_US
dc.identifier.startpage104706en_US
dc.identifier.endpage104718en_US
dc.identifier.wosWOS:000866433400001en_US
dc.institutionauthorAydoğdu, Ömer-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid55972949100-
dc.authorscopusid14833966800-
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextembargo_20300101-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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