Comparative Study Between Finite Control Set Model Predictive Control and Digital Sliding-Mode Control for the Reduction of Current Harmonics in Six-Phase PMSM Drives
Bibliographic record
Abstract
Asymmetrical six-phase permanent magnet synchronous machines (PMSMs) drives are a promising candidate for electric transportation systems, offering inherent fault tolerance. However, one of the key challenges in developing high-performance control strategies for these systems is suppressing undesirable circulating currents that increase the machine losses. Hence, this paper compares two non-linear control strategies, namely finite control set model predictive control (FCS-MPC) and digital sliding-mode control (DSMC), to address this issue. Besides providing an excellent dynamic performance and robustness to parameter mismatch errors, both control strategies aim to minimize the circulating current harmonics, which are mainly due to deadtime effects and back-EMF harmonics in six-phase PMSM drives. Simulation results obtained with MATLAB/Simulink® are presented in this paper to compare and validate the performance of both control strategies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".