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

Abstract

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.

Keywords:

• Controllers
• machine learning (ML)
• neural network
• permanent magnet synchronous motor (PMSM)
• regression algorithms
• vector control

REVIEWING EDITOR:

• Ai Qingsong, Wuhan University of Technolog, Wuhan, China

SUBJECTS:

• Technology
• Computer Science
• Algorithms & Complexity
• Artificial Intelligence
• Engineering & Technology
• Electrical & Electronic Engineering
• Systems & Control Engineering
• Transport & Vehicle Engineering

Disclosure statement

The authors declare that they have no conflict of interest.

Data availability statement

The data that support the findings of this study are available from the corresponding author, J. L. Febin Daya, upon reasonable request.

Additional information

Notes on contributors

Ashly Mary Tom

Ashly Mary Tom is a doctoral student at the School of Electrical Engineering, Vellore Institute of Technology, Chennai Campus, India. Her research focuses on motor drives and machine learning techniques.

J. L. Febin Daya

J.L. Febin Daya is presently serving as Professor in Electric Vehicles Incubation, Testing, and Research Center at Vellore Institute of Technology, Chennai Campus, India. His current research interests include electrical drives and control, wireless charging, intelligent systems, electric vehicles and precision agriculture.