AI-BASED FIELD-ORIENTED CONTROL FOR INDUCTION MOTORS

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.