AI-BASED FIELD-ORIENTED CONTROL FOR INDUCTION MOTORS

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DOI

Elmehdi Benmalek

elmehdi.benmalek@um5s.net.ma

https://orcid.org/0000-0003-1078-1421
Marouane Rayyam

03malaho06@gmail.com

Ayoub Gege

khokhada@gmail.com

Omar Ennasiri

khobzkarm13@gmail.com

Adil Ezzaidi

khobzkarm@gmail.com

Abstract

The current article deals with the implementation of Reinforcement Learning based Field Oriented Control (FOC) for the induction motors (IM). It is pertinent to mention that although conventional controllers like PID are widely used in FOC induction, they are model-based and face problems such as parameter adjustment. PID controllers need to be tuned because of the approximations of the model, variations of the parameters during operation, and the external disturbances that are uncertain and unpredictable. RL is a machine learning approach that is model-free which can adapt to the variations and disturbances. Therefore, these controllers can be an excellent alternative to the conventional controllers. In this study, an RL-based controller was used to control the speed of the induction motor using the FOC and space vector modulation (SVM). Computational simulations were done using the MATLAB/SIMULINK to test the controllers’ performance under different operating conditions. This study highlights the effectiveness of RL in optimizing IM control, offering potential benefits in various industrial and automation applications.

Keywords:

induction motor, field-oriented control, NPC inverter, reinforcement learning, TD3 agent

References

Article Details

Benmalek, E., Rayyam, M., Gege, A., Ennasiri, O., & Ezzaidi, A. (2024). AI-BASED FIELD-ORIENTED CONTROL FOR INDUCTION MOTORS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(4), 75–81. https://doi.org/10.35784/iapgos.6253