Multivariable sliding‐mode extremum seeking PI tuning for current control of a PMSM
Bibliographic record
Abstract
High‐performance current control is critical for obtaining smooth output torque in permanent‐magnet synchronous motors (PMSMs). To this end, a new proportional–integral (PI) tuning method based on multivariable sliding‐mode extremum seeking is proposed in this study and applied for current control of a PMSM. In the proposed method, a sliding‐mode extremum seeking optimiser varies the PI gains by minimising a cost function based on the feedback error term. The resulting PI controller can achieve fast and accurate tracking response, high disturbance rejection, and low sensitivity to PMSM parameter variations. The stability of the proposed control strategy is investigated through a Lyapunov analysis and its performance is evaluated through experimental studies. The results indicate that the proposed controller can offer improved performance in terms of accuracy, parametric variations, and load torque disturbances when compared with a conventional PI and a recently proposed PI controller using the gradient‐based extremum seeking tuning method.
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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 | 0.000 | 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".