Data-Driven TD3 Control of IM Considering Magnetic Saturation and Temperature Effect
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
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.
Açıklama
6th Global Power, Energy and Communication Conference (GPECOM) -- JUN 04-07, 2024 -- Budapest, HUNGARY
Anahtar Kelimeler
Induction motor, parameter estimation, reinforcement learning, TD3 agent
Kaynak
Proceedings 2024 Ieee 6th Global Power, Energy and Communication Conference, Ieee Gpecom 2024
WoS Q Değeri
N/A
Scopus Q Değeri
N/A