Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3773
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dc.contributor.authorLevent, Mehmet Latif-
dc.contributor.authorAydoğdu, Ömer-
dc.contributor.authorYücelbaş, Cüneyt-
dc.date.accessioned2023-03-03T13:35:01Z-
dc.date.available2023-03-03T13:35:01Z-
dc.date.issued2020-
dc.identifier.issn2148-2683-
dc.identifier.urihttps://doi.org/10.31590/ejosat.804741-
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1135938-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/3773-
dc.description.abstractIn this study, a new adaptive controller design was created that compensates for variable load effects and provides high control performance. In the proposed control method, Discrete Time Kalman Filter method (DKF), which estimates system output states, and Discrete Time Linear Quadratic Regulator (DLQR) method, one of the optimal control methods, were used. Although the DLQR control method produces good results when applied to unvarying systems, it cannot provide the desired response in time varying systems because it has no adaptation mechanism. In order to solve this problem, an adaptation mechanism based lyapunov method which has been developed that adapts to different environmental conditions, constantly updating a new state feedback gain matrix value (newK ) and optimal lyapunov adaptation gain values (1 ,2 ,3 ,4 ,5 and6 ) used for system control block such as position (1x ) control, speed (2x ) control and current (3x ) control. In this mechanism, lyapunov adaptation gain initial values were calculated using the Artificial Neural Network (ANN) method as a new approach. Thus, it was aimed to eliminate the variable load effects and to increase the stability of the system. In order to demonstrate the effectiveness of the proposed method, a variable loaded VsimLabs (Virtual Simulation laboratories) servo system was modelled as a time-varying linear system and used in practical implementation and simulation in Matlab-Simulink environment. Based on the experimental results and performance measurements such as Integral Square Error (ISE), Integral Absolute Error (IAE) and Integral time absolute error (ITAE), it was observed that the proposed method increases the system performance and stability by minimizing variable load effect and steady state error.en_US
dc.language.isoenen_US
dc.relation.ispartofAvrupa Bilim ve Teknoloji Dergisien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAdaptation mechanismen_US
dc.subjectArtificial neural networken_US
dc.subjectLyapunov methoden_US
dc.subjectTime varying linear system Adaptasyon Mekanizmasıen_US
dc.subjectYapay Sinir Ağıen_US
dc.subjectLyapunov Yöntemien_US
dc.subjectZamanla Değişen Doğrusal Sistemen_US
dc.titleDiscrete Time State Estimation with Kalman Filter and Adaptive LQR Control of a Time Varying Linear Systemen_US
dc.typeArticleen_US
dc.identifier.doi10.31590/ejosat.804741-
dc.departmentKATÜNen_US
dc.identifier.volume0en_US
dc.identifier.issueEjosat Özel Sayı 2020 (ICCEES)en_US
dc.identifier.startpage322en_US
dc.identifier.endpage331en_US
dc.institutionauthor-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanen_US
dc.identifier.trdizinid1135938en_US
item.grantfulltextopen-
item.openairetypeArticle-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections
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