| 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) have been used daily since the 19th century. The induction machine should be effectively controlled to achieve high performance. Since the late 20th century and 21st century, field-oriented control (FOC), direct torque control (DTC), and model predictive control (MPC) techniques have been used in high-performance control applications. However, these techniques are dependent on the motor parameters and inverter model. These parameters change non-linearly depending on magnetic saturation, temperature, and operating point which negatively affects the performance of the system. In order to eliminate the negative effects of parameter changes, reinforcement learning (RL)based methods have become increasingly popular in the literature in recent years. In this study, for the first time, the speed control of IM is performed using TD3 agent, which is one of the RL-based methods. The dynamic and steady-state performance of the control system designed with the TD3 agent is compared with the traditional FOC technique. Extensive simulation results have shown the robustness of the proposed drive system. |
| Anahtar Kelimeler |
| Induction motor | model predictive control | 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 |
