| Yazarlar (3) |
Arş. Gör. Uğur Ufuk KÖRPE
Kırşehir Ahi Evran Üniversitesi, Türkiye |
Mustafa Gökdağ
Karabük Üniversitesi, Türkiye |
|
Ozan Gülbudak
Karabük Üniversitesi, Türkiye |
| Özet |
| Induction 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. |
| Anahtar Kelimeler |
| Induction motor | parameter estimation | reinforcement learning | TD3 agent |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayımlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Web of Science Kapsamındaki Kongre/Sempozyum |
| Bildiri Dili | İngilizce |
| Kongre Adı | IEEE 6th Global Power, Energy and Communication Conference (IEEE GPECOM2024) |
| Kongre Tarihi | 04-06-2024 / 07-06-2024 |
| Basıldığı Ülke | Macaristan |
| Basıldığı Şehir | Budapest |
| Atıf Sayıları |
