Real-Time Validation of Enhanced Permanent Magnet Synchronous Motor Drive Using Dense-Neural-Network-Based Control
Bibliographic record
Abstract
High-performance current and speed control are required to obtain smooth output torque, current tracking, and speed tracking in permanent-magnet synchronous motor (PMSM) drives. The motor speed and stator current control rely on multiple nonlinear motor parameters, which play a crucial role in shaping the performance of PMSM. Moreover, tuning the speed and current controller parameters using the conventional control technique depends on these PMSM parameters, also variation of these parameters will have a decisive influence on the dynamic performance of PMSM. To enhance the robustness of vector control and tracking methodology against PMSM parameter uncertainties and load disturbances, a novel artificial intelligence (AI)-based advanced speed and current control technique for PMSM is proposed in this article. Subsequently, the methodology for designing and training the suggested Dense Neural Network (DNN) controllers are elicited. The proposed controllers can handle the inevitable fluctuation and non-linearity in motor parameters at different load points and drive conditions. The proposed DNN scheme is validated in terms of settling time, dynamic responsiveness, tolerance to parameter fluctuations, and overall robustness. A comparative analysis is conducted against adaptive proportional-integral (API) control applied to the same PMSM within the OPAL-RT real-time simulator (RTS). The viability of the proposed control scheme is substantiated through simulation, Software-In-the-Loop (SIL) and Hardware-In-the-Loop (HIL) testing with an RTS and an automotive-grade controller board across diverse conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".