Design of machine learning-based controllers for speed control of PMSM drive

This study presents machine learning (ML)-based controllers for a surface permanent magnet synchronous motor (PMSM) drive system. The ML-based regression techniques like linear regression (LR), support vector machine regression (SVM), feedforward neural network (NN) and advanced NN like Long Short-T...

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Vydáno v:Scientific reports Ročník 15; číslo 1; s. 17826 - 24
Hlavní autoři: Tom, Ashly Mary, Daya, J. L. Febin
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 22.05.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/166
639/166/987
639/705/794
Algorithms
Control algorithms
Controllers
Humanities and Social Sciences
Learning algorithms
Long short-term memory
Long short-term memory network
Machine learning
Machine learning (ML)
multidisciplinary
Neural network
Neural networks
Permanent magnet synchronous motor (PMSM)
Science
Science (multidisciplinary)
Vector control
Controllers
Long short-term memory network
Machine learning (ML)
Neural network
Vector control
Permanent magnet synchronous motor (PMSM)
ISSN:2045-2322, 2045-2322
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Popis
Shrnutí:This study presents machine learning (ML)-based controllers for a surface permanent magnet synchronous motor (PMSM) drive system. The ML-based regression techniques like linear regression (LR), support vector machine regression (SVM), feedforward neural network (NN) and advanced NN like Long Short-Term Memory network (LSTM) are explored here in detail. This paper aims to develop an improved vector controller based on machine learning, and to investigate ML algorithms which are not yet been explored for the current control of a PMSM drive. The proposed machine learning-based control approach, which explores the influence of decoupling terms on vector control, is theoretically investigated and simulated in the vector control environment of the PMSM drive. The performance is also evaluated in real-time using the Opal-RT setup. The proposed control approach demonstrates the ability to fulfill the speed tracking requirements in the closed-loop drive system. A comparison of the simulation results between the PI controller and the suggested control algorithms validates the effectiveness of the proposed control algorithms for speed control applications. The performances of the proposed ML-based controllers improved in terms of evaluation metrics, transient peak levels and current responses, when compared to the conventional PI controller.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-02396-y