Machine learning techniques for vector control of permanent magnet synchronous motor drives

In the conventional vector control technique for motor drive, Proportional-Integral (PI) controllers are being used, which are sensitive to parameter variations of the drive system. This article presents Machine Learning (ML)-based controllers for a surface permanent magnet synchronous motor (PMSM) drive system. In this work, ML-based regression algorithms such as linear regression, support vector machine regression and feedforward neural network are investigated for speed control application. The entire vector control scheme implementing the ML-based control algorithms is investigated theoretically and simulated under various dynamic operating conditions. Simulation results and performance metrics are compared with those of the conventional PI controller, and they validate the effectiveness of the proposed control algorithms for speed control applications. The proposed ML-based controllers have the ability to meet the speed tracking requirements in the closed-loop system, with performance metrics superior to those of the PI controller, by an average value of 20% for different test scenarios. The transient levels of the motor drive reduce by 0.02% while using the proposed controllers.