A Multi-level DC-Link Extremum-seeking PI Controller for a PMSM with Low Inductance
Bibliographic record
Abstract
In this paper, an adaptive PI controller using a multi-level DC link (MLDCL) inverter is proposed and applied for angular position trajectory tracking in a permanent magnet synchronous motor (PMSM) with relatively low stator inductance and resistance. Modern lightweight motors have smaller resistance and inductance values when compared to conventional motors, which give rise to higher levels of current ripples when using large PWM frequencies. To alleviate the problem, the proposed controller combines the merits of a multi-level DC link inverter with a multivariable sliding-mode extremum seeking tuned PI controller to achieve fast and precise trajectory tracking. The extremum seeking tuning method adjusts the PI gains in realtime which makes the system tracking performance more precise. The MLDCL inverter used in this paper reduces the PMSM current ripples of the three-phase inverter through utilizing multiple half-bridge cells instead of an individual DC source used in traditional pulse-width modulation (PWM) inverters. The tracking performance of the proposed control scheme is investigated through simulations by comparing the results with a recently proposed fixed-gain PI controller using a traditional PWM inverter. The results demonstrate the effectiveness of the proposed controller in achieving lower torque ripples and more precise tracking response.
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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 | 0.000 |
| 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 | 0.001 |
| 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".