Analysis of Predictive Current Regulator for High-Speed IPMSM Operating at Low Sampling to Fundamental Frequency Ratios
Bibliographic record
Abstract
At low sampling to fundamental frequency ratios (SFRs), the time delay and current sampling error deteriorate the performances of the current regulator. Therefore, this paper proposes a comprehensive analysis and a tuning method of the PI controller and Internal Model Controller (IMC) with one-step current prediction (OCP) operating under low SFRs. The tuning method analyzes the migration of the closed-loop poles with the electrical speed change and selects the optimal control parameters to improve the system's performance. To enhance the disturbance rejection of the IMC approach, a novel active resistance is also proposed. Simulation and experimental results performed on an interior permanent magnet synchronous motor (IPMSM) have successfully validated the proposed methods. The results show that, the conventional PI design with OCP results in acceptable decoupling and dynamic performances at a minimum SFR of 7.5. While the proposed IMC approach still retains its stability and dynamic performances both at SFR of 7.5 and 5. For disturbance rejection, only the proposed IMC design can achieve enhanced performance at low SFRs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".