Machine Learning-based Trajectory Planning for Single-loop Flatness-based Control of PMSMs
Bibliographic record
Abstract
Permanent magnet synchronous motors (PMSMs) are among the most widely used motors in modern industry. Over the past few decades, extensive research has been conducted on various control methods, while field-oriented control (FOC) being one of the most well-known approaches. Additionally, flatness-based (FB) control has been introduced in the literature as a solution for addressing the nonlinear characteristics inherent in PMSM drive systems. Traditional FB control methods typically has a cascaded structure and employ second-order functions as trajectory functions to maintain the flatness property of the drive system. However, this cascaded structure presents certain limitations, particularly in applications requiring high-dynamic performance. To overcome these drawbacks, the concept of single-loop FB control has been proposed in recent studies. One significant challenge in single-loop FB control systems is ignoring controller limits, such as overcurrent protection. To address this issue, this paper proposes a novel trajectory planning method for single-loop FB control of PMSMs, using machine learning. The proposed method effectively tackles the challenge of current protection while maintaining the system’s flatness property. The effectiveness of the proposed approach has been validated through simulation studies, demonstrating its potential for enhancing the performance of PMSM drives.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".