Publication result detail

A Dead-Time Compensation Strategy Based on an Online Learned Artificial Neural Network

BUCHTA, L.; KOZOVSKÝ, M.; BLAHA, P.

Original Title

A Dead-Time Compensation Strategy Based on an Online Learned Artificial Neural Network

English Title

A Dead-Time Compensation Strategy Based on an Online Learned Artificial Neural Network

Type

WoS Article

Original Abstract

This article presents an innovative approach to mitigate the harmonic distortion of the phase currents of a permanent magnet synchronous motor (PMSM) controlled by a field-oriented control (FOC) algorithm. The issue of phase current harmonic distortion is often a consequence of the output voltage deformation caused by the non-linearities of the voltage source inverter (VSI). The relationship between the disturbance voltages of the inverter and the phase currents of the motor is non-linear. Therefore, we used an artificial neural network (ANN) to identify the compensation voltages. The topology is designed to allow the neural network to solve complex problems with the limited computing resources available on the AURIX TC397 microcontroller. The input vector is assembled from quantities available in the PMSM FOC algorithm. The online learning process based on the back-propagation algorithm is adapted to operate directly on the microcontroller. The proposed strategy with ANN is verified on a real PMSM. The results show the excellent ability of the proposed ANN to suppress the harmonic distortion of the PMSM phase currents without knowledge of the VSI parameters.

English abstract

This article presents an innovative approach to mitigate the harmonic distortion of the phase currents of a permanent magnet synchronous motor (PMSM) controlled by a field-oriented control (FOC) algorithm. The issue of phase current harmonic distortion is often a consequence of the output voltage deformation caused by the non-linearities of the voltage source inverter (VSI). The relationship between the disturbance voltages of the inverter and the phase currents of the motor is non-linear. Therefore, we used an artificial neural network (ANN) to identify the compensation voltages. The topology is designed to allow the neural network to solve complex problems with the limited computing resources available on the AURIX TC397 microcontroller. The input vector is assembled from quantities available in the PMSM FOC algorithm. The online learning process based on the back-propagation algorithm is adapted to operate directly on the microcontroller. The proposed strategy with ANN is verified on a real PMSM. The results show the excellent ability of the proposed ANN to suppress the harmonic distortion of the PMSM phase currents without knowledge of the VSI parameters.

Keywords

Inverter non-linearities compensation, dead-time effect, artificial neural network (ANN), permanent magnet synchronous motor (PMSM), voltage source inverter (VSI)

Key words in English

Inverter non-linearities compensation, dead-time effect, artificial neural network (ANN), permanent magnet synchronous motor (PMSM), voltage source inverter (VSI)

Authors

BUCHTA, L.; KOZOVSKÝ, M.; BLAHA, P.

Released

03.04.2025

Periodical

IEEE Transactions on Industrial Electronics

Volume

72

Number

10

State

United States of America

Pages from

10574

Pages to

10584

Pages count

11

URL

https://ieeexplore.ieee.org/document/10948334

Full text in the Digital Library

http://hdl.handle.net/11012/255539

BibTex

@article{BUT197508,
  author="Luděk {Buchta} and Matúš {Kozovský} and Petr {Blaha}",
  title="A Dead-Time Compensation Strategy Based on an Online Learned Artificial Neural Network",
  journal="IEEE Transactions on Industrial Electronics",
  year="2025",
  volume="72",
  number="10",
  pages="10574--10584",
  doi="10.1109/TIE.2025.3544207",
  issn="0278-0046",
  url="https://ieeexplore.ieee.org/document/10948334"
}

Documents

A_Dead-Time_Compensation_Strategy_Based_on_an_Online_Learned_Artificial_Neural_Network