Improvised FOC and Speed Estimation of PMSM using Reinforcement Learning and Neural Networks

In this study, field-oriented control of a sensor less Interior PMSM with the help of Artificial Neural Network (ANN) is carried out by utilizing both Proportional-Integral (PI) controllers and Reinforcement learning (RL). Making a high-speed response controller, which improves the motor by its performance, requires the appropriate control strategy for the motor and the results from each method are compared. A Deep Neural Network is built and trained to estimate both rotor's position and speed. Thus, removing the need for sensors, which increases drive system complexity, and costs. A Twin Delayed Deep Deterministic Policy Gradients (TD3) method is used for Reinforcement Learning. It is a combination of both actor and critic networks that finds the best policy to implement in order to maximize the estimated cumulative long-term reward. Due to the above-mentioned benefits, PMSMs are frequently utilized in modern variable speed AC drives, especially in applications involving electric vehicles.