Data-Driven TD3 Control of IM Considering Magnetic Saturation and Temperature Effect

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dc.contributor.authorKorpe, Ugur Ufuk
dc.contributor.authorGokdag, Mustafa
dc.contributor.authorGulbudak, Ozan
dc.date.accessioned2024-09-29T16:04:28Z
dc.date.available2024-09-29T16:04:28Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description6th Global Power, Energy and Communication Conference (GPECOM) -- JUN 04-07, 2024 -- Budapest, HUNGARYen_US
dc.description.abstractInduction machines (IM) are still widely used in the industry due to their advantages, such as low maintenance requirements and improved robustness. The field-oriented control (FOC), direct torque control (DTC), and model predictive control (MPC) techniques are used to control IM in high-performance control applications. The common disadvantage of these control techniques is that the control performances are negatively affected by changes in machine parameters, and machine parameters vary non-linearly depending on the magnetic saturation and temperature. To solve this negative affect, the control technique can be optimized by using a parameter estimation methods. Another solution to eliminate these negative effects is to design a reinforcement learning (RL)-based controller that regulates the control variables without the knowledge of machine parameters. In this study, IM speed control is performed using a twin-delayed deep deterministic policy gradient (TD3) agent. The dynamic and steady-state performance of the designed controller are compared with the traditional control techniques. Extensive simulation results have shown that the dynamic and steady-state performance of the designed controller is better than other control techniques.en_US
dc.description.sponsorshipIEEEen_US
dc.identifier.doi10.1109/GPECOM61896.2024.10582723
dc.identifier.endpage184en_US
dc.identifier.isbn979-8-3503-5108-8
dc.identifier.isbn979-8-3503-5109-5
dc.identifier.issn2832-7667
dc.identifier.scopus2-s2.0-85199041185en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage179en_US
dc.identifier.urihttps://doi.org/10.1109/GPECOM61896.2024.10582723
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6129
dc.identifier.wosWOS:001268516300112en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartofProceedings 2024 Ieee 6th Global Power, Energy and Communication Conference, Ieee Gpecom 2024en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInduction motoren_US
dc.subjectparameter estimationen_US
dc.subjectreinforcement learningen_US
dc.subjectTD3 agenten_US
dc.titleData-Driven TD3 Control of IM Considering Magnetic Saturation and Temperature Effecten_US
dc.typeConference Objecten_US

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