Comparative Analysis of Weighting-Factor-Free Predictive Control Strategies for Direct Torque Control in Permanent Magnet Synchronous Machines

Direct torque control (DTC) based on the finite control set model predictive control (FCS-MPC) provides a straightforward and intuitive solution for controlling permanent magnet synchronous motors (PMSMs). However, conventional FCS-MPC relies on appropriately tuned weighting factors in the cost function, which have a significant impact on the control performance and increase design complexity. This paper presents a comprehensive experimental comparison of emerging FCS-MPC strategies for DTC of PMSMs that eliminate the need for weighting factors. Specifically, a sequential FCS-MPC approach is benchmarked against decision-making-based FCS-MPC methods that employ Euclidean distance normalisation. Extensive experimental results, obtained across a wide range of operating conditions, are used to assess current total harmonic distortion (THD), torque and flux ripple, and transient performance. Results indicate that while all methods yield comparable current THD, decision-making-based strategies achieve superior torque and flux regulation with reduced ripple compared to the sequential approach. These findings demonstrate that decision-making-based FCS-MPC methods provide additional flexibility in defining control objectives, eliminating the need to design weighting factors, such as those used in the sequential method while offering superior performance.

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Publication Details

Journal
Processes
Published
2025-08-18
DOI
https://doi.org/10.3390/pr13082614
Citations
2
Primary Topic
Electric Motor Design and Analysis
Type
article
Field-Weighted Citation Impact
1.46
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article

Comparative Analysis of Weighting-Factor-Free Predictive Control Strategies for Direct Torque Control in Permanent Magnet Synchronous Machines

Patrick Wheeler, Jacopo Riccio, Emrah Zerdali, Jakson Paulo Bonaldo et al.
2 citations
Processes
Electric Motor Design and Analysis
1.46
article

Comparative Analysis of Weighting-Factor-Free Predictive Control Strategies for Direct Torque Control in Permanent Magnet Synchronous Machines

Patrick Wheeler, Jacopo Riccio, Emrah Zerdali, Jakson Paulo Bonaldo, Raul Vitor Arantes Monteiro, Marco Rivera
article en
2 citations

Abstract

Direct torque control (DTC) based on the finite control set model predictive control (FCS-MPC) provides a straightforward and intuitive solution for controlling permanent magnet synchronous motors (PMSMs). However, conventional FCS-MPC relies on appropriately tuned weighting factors in the cost function, which have a significant impact on the control performance and increase design complexity. This paper presents a comprehensive experimental comparison of emerging FCS-MPC strategies for DTC of PMSMs that eliminate the need for weighting factors. Specifically, a sequential FCS-MPC approach is benchmarked against decision-making-based FCS-MPC methods that employ Euclidean distance normalisation. Extensive experimental results, obtained across a wide range of operating conditions, are used to assess current total harmonic distortion (THD), torque and flux ripple, and transient performance. Results indicate that while all methods yield comparable current THD, decision-making-based strategies achieve superior torque and flux regulation with reduced ripple compared to the sequential approach. These findings demonstrate that decision-making-based FCS-MPC methods provide additional flexibility in defining control objectives, eliminating the need to design weighting factors, such as those used in the sequential method while offering superior performance.

ProcessesVol. 13(8)
University of Talca (CL), University of Nottingham (GB), Universidade Federal de Mato Grosso (BR), Ege University (TR)
Openalex Percentile: Top 16%
Electric Motor Design and Analysis
1.46
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Comparative Analysis of Weighting-Factor-Free Predictive Control Strategies for Direct Torque Control in Permanent Magnet Synchronous Machines — Patrick Wheeler, Jacopo Riccio, et al. · Processes (2025) | TGRS Research Map | TGRS