Adaptive One Sample Ahead Preview Control with Multilevel Validation for Non-Sinusoidal PMSMs in <i>dq</i> Coordinates

This study shows the design and implementation of an adaptive one sample ahead preview (AOSAP) control structure tailored for non-sinusoidal permanent magnet synchronous motors (PMSMs), implemented in both d and q coordinates. The control strategy employs a first-order reference model and is systematically compared against conventional Proportional-Integral (PI) controllers. The simulation results in Matlab and the experimental evaluations conducted on a Typhoon hardware-in-the-loop (HIL) 604 platform and a physical prototype demonstrate the superior performance of the proposed AOSAP controller. It exhibits lower tracking errors and faster regulation compared to classical PI controllers. The control algorithms are developed on Texas Instruments Delfino C2000 microcontrollers, showcasing the feasibility of the proposed approach in real world applications.

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Publication Details

Journal
IEEJ Journal of Industry Applications
Published
2025-07-24
DOI
https://doi.org/10.1541/ieejjia.25000120
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Adaptive One Sample Ahead Preview Control with Multilevel Validation for Non-Sinusoidal PMSMs in dq Coordinates

Wencong Su, Van‐Hai Bui, Lucas Rossato Rocha, Rodrigo Padilha Vieira et al.
IEEJ Journal of Industry Applications
Neural Networks and Applications
article

Adaptive One Sample Ahead Preview Control with Multilevel Validation for Non-Sinusoidal PMSMs in dq Coordinates

Wencong Su, Van‐Hai Bui, Lucas Rossato Rocha, Rodrigo Padilha Vieira, Guilherme Vieira Hollweg, Mengqi Wang
article en

Abstract

This study shows the design and implementation of an adaptive one sample ahead preview (AOSAP) control structure tailored for non-sinusoidal permanent magnet synchronous motors (PMSMs), implemented in both d and q coordinates. The control strategy employs a first-order reference model and is systematically compared against conventional Proportional-Integral (PI) controllers. The simulation results in Matlab and the experimental evaluations conducted on a Typhoon hardware-in-the-loop (HIL) 604 platform and a physical prototype demonstrate the superior performance of the proposed AOSAP controller. It exhibits lower tracking errors and faster regulation compared to classical PI controllers. The control algorithms are developed on Texas Instruments Delfino C2000 microcontrollers, showcasing the feasibility of the proposed approach in real world applications.

IEEJ Journal of Industry ApplicationsVol. 14(6)
Universidade Federal de Santa Maria (BR), University of Michigan–Dearborn (US)
Openalex Percentile: Top 91%
Neural Networks and Applications
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