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ANN-Based Online Parameter Correction for PMSM Control Using Sphere Decoding Algorithm

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Název: ANN-Based Online Parameter Correction for PMSM Control Using Sphere Decoding Algorithm
Autoři: Joseph O. Akinwumi, Yuan Gao, Xin Yuan, Sergio Vazquez, Harold S. Ruiz
Zdroj: Sensors ; Volume 26 ; Issue 2 ; Pages: 553
Informace o vydavateli: Multidisciplinary Digital Publishing Institute
Rok vydání: 2026
Sbírka: MDPI Open Access Publishing
Témata: sphere decoding algorithm, permanent magnet synchronous motor, artificial neural network, model predictive control
Popis: This work addresses parameter mismatch in Permanent Magnet Synchronous Motor (PMSM) drives, focusing on performance degradation caused by variations in flux linkage and inductance arising under realistic operating uncertainties. An artificial neural network (ANN) is trained to estimate these parameter shifts and update the controller model online. The procedure comprises three steps: (i) data generation using Sphere Decoding Algorithm-based Model Predictive Control (SDA-MPC) across a mismatch range of ±50%; (ii) offline ANN training to map measured features to parameter estimates; and (iii) online ANN deployment to update model parameters within the SDA-MPC loop. MATLAB /Simulink simulations show that ANN-based compensation can improve current tracking and THD under many mismatch conditions, although in some cases—particularly when inductance is overestimated—THD may increase relative to nominal operation. When parameters return to nominal values the ANN adapts accordingly, steering the controller back toward baseline performance. The data-driven adaptation enhances robustness with modest computational overhead. Future work includes hardware-in-the-loop (HIL) testing and explicit experimental study of temperature-dependent effects.
Druh dokumentu: text
Popis souboru: application/pdf
Jazyk: English
Relation: Intelligent Sensors; https://dx.doi.org/10.3390/s26020553
DOI: 10.3390/s26020553
Dostupnost: https://doi.org/10.3390/s26020553
Rights: https://creativecommons.org/licenses/by/4.0/
Přístupové číslo: edsbas.4BFE3319
Databáze: BASE
Popis
Abstrakt:This work addresses parameter mismatch in Permanent Magnet Synchronous Motor (PMSM) drives, focusing on performance degradation caused by variations in flux linkage and inductance arising under realistic operating uncertainties. An artificial neural network (ANN) is trained to estimate these parameter shifts and update the controller model online. The procedure comprises three steps: (i) data generation using Sphere Decoding Algorithm-based Model Predictive Control (SDA-MPC) across a mismatch range of ±50%; (ii) offline ANN training to map measured features to parameter estimates; and (iii) online ANN deployment to update model parameters within the SDA-MPC loop. MATLAB /Simulink simulations show that ANN-based compensation can improve current tracking and THD under many mismatch conditions, although in some cases—particularly when inductance is overestimated—THD may increase relative to nominal operation. When parameters return to nominal values the ANN adapts accordingly, steering the controller back toward baseline performance. The data-driven adaptation enhances robustness with modest computational overhead. Future work includes hardware-in-the-loop (HIL) testing and explicit experimental study of temperature-dependent effects.
DOI:10.3390/s26020553