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https://hdl.handle.net/20.500.13091/3161
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ünlütürk, Ali | - |
dc.contributor.author | Aydoğdu, Ömer | - |
dc.date.accessioned | 2022-11-28T16:54:43Z | - |
dc.date.available | 2022-11-28T16:54:43Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://doi.org/10.1109/ACCESS.2022.3210540 | - |
dc.identifier.uri | https://doi.org/10.1109/ACCESS.2022.3210540 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/3161 | - |
dc.description.abstract | In 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.iso | en | en_US |
dc.publisher | Ieee-Inst Electrical Electronics Engineers Inc | en_US |
dc.relation.ispartof | IEEE Access | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Fuzzy logic | en_US |
dc.subject | Payloads | en_US |
dc.subject | Robot sensing systems | en_US |
dc.subject | Mobile robots | en_US |
dc.subject | Artificial neural networks | en_US |
dc.subject | Motion control | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Machine learning | en_US |
dc.subject | adaptive fuzzy logic control | en_US |
dc.subject | balance robot | en_US |
dc.subject | sensor fusion | en_US |
dc.subject | Design | en_US |
dc.title | Machine Learning Based Self-Balancing and Motion Control of the Underactuated Mobile Inverted Pendulum With Variable Load | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/ACCESS.2022.3210540 | - |
dc.identifier.scopus | 2-s2.0-85139449411 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.identifier.volume | 10 | en_US |
dc.identifier.startpage | 104706 | en_US |
dc.identifier.endpage | 104718 | en_US |
dc.identifier.wos | WOS:000866433400001 | en_US |
dc.institutionauthor | Aydoğdu, Ömer | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 55972949100 | - |
dc.authorscopusid | 14833966800 | - |
dc.identifier.scopusquality | Q1 | - |
item.grantfulltext | embargo_20300101 | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.openairetype | Article | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.04. 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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Machine_Learning_Based_Self-Balancing_and_Motion_Control_of_the_Underactuated_Mobile_Inverted_Pendulum_With_Variable_Load.pdf Until 2030-01-01 | 2.15 MB | Adobe PDF | View/Open Request a copy |
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