Adaptive-Confidence-Window-Modulated Predictive Control for Induction Motor Drives: Real-Time HIL Validation on DS1202.
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| Title: | Adaptive-Confidence-Window-Modulated Predictive Control for Induction Motor Drives: Real-Time HIL Validation on DS1202. |
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| Authors: | Khaldi, Belgacem Said1 (AUTHOR), Charrak, Naas1,2 (AUTHOR), Kouzou, Abdellah1,3 (AUTHOR) jose.rodriguezp@uss.cl, Rodriguez, Jose2,4 (AUTHOR), Abdelrahem, Mohamed1,3,4 (AUTHOR) mohamed.abdelrahem@tum.de |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 11, p2711. 30p. |
| Subject Terms: | *Predictive control systems, *Induction motors, *Adaptive control systems, *Hardware-in-the-loop simulation, *Model validation, *Torque control, *Real-time computing |
| Abstract: | This paper proposes an adaptive-confidence-window-modulated model predictive controller (ACW-M2PC) for induction motor drives. The method combines angle-guided local sector selection with a confidence-triggered bounded expansion toward adjacent sectors, so that the online search remains local whenever the local solution is reliable and expands only when necessary. This decision structure reduces unnecessary candidate evaluations while preserving low computational burden and improving the quality of the selected voltage action. The proposed controller was implemented and validated through real-time hardware-in-the-loop experiments on a dSPACE DS1202 platform. Compared with a baseline full-search-modulated model predictive controller (M2PC), ACW-M2PC reduced the average number of evaluated sectors by 79.7% while maintaining zero-overrun real-time execution. At the same time, it improved torque quality, reducing torque ripple peak-to-peak by 70.2% and torque ripple RMS by 62.0%, with a slight reduction in speed integral absolute error. An ablation study further showed that angle-guided local reduction already captures a large part of the computational benefit, whereas the confidence-triggered bounded expansion provides the additional corrective action required when the local solution becomes insufficient. Overall, these results show that ACW-M2PC improves the performance–complexity trade-off while remaining suitable for real-time induction motor drive control. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | This paper proposes an adaptive-confidence-window-modulated model predictive controller (ACW-M2PC) for induction motor drives. The method combines angle-guided local sector selection with a confidence-triggered bounded expansion toward adjacent sectors, so that the online search remains local whenever the local solution is reliable and expands only when necessary. This decision structure reduces unnecessary candidate evaluations while preserving low computational burden and improving the quality of the selected voltage action. The proposed controller was implemented and validated through real-time hardware-in-the-loop experiments on a dSPACE DS1202 platform. Compared with a baseline full-search-modulated model predictive controller (M2PC), ACW-M2PC reduced the average number of evaluated sectors by 79.7% while maintaining zero-overrun real-time execution. At the same time, it improved torque quality, reducing torque ripple peak-to-peak by 70.2% and torque ripple RMS by 62.0%, with a slight reduction in speed integral absolute error. An ablation study further showed that angle-guided local reduction already captures a large part of the computational benefit, whereas the confidence-triggered bounded expansion provides the additional corrective action required when the local solution becomes insufficient. Overall, these results show that ACW-M2PC improves the performance–complexity trade-off while remaining suitable for real-time induction motor drive control. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19112711 |
