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

Cilt

Sayı

Künye