Torque Ripple Minimization and Control of a Permanent Magnet Synchronous Motor Using Multiobjective Extremum Seeking
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Bibliographic record
Abstract
In this paper, a multiobjective extremum-seeking (MOES) approach is proposed for torque control of a permanent magnet synchronous motor and minimization of its torque ripple. The latter aspect is important in human-machine interface applications such as haptic interfaces requiring smooth torque profiles at slow speeds. The proposed MOES scheme combines an adaptive iterative learning control method with an adaptive proportional-integral (PI) controller, which makes the system less sensitive to load disturbances and improves the control performance for torque regulation during transient events. Experiments are performed on a proof-of-concept exercise machine that generates desired torque profiles and mechanical impedance based on user's preference. The performance of the proposed controller is further compared with a recently proposed adaptive PI controller. The experimental results validate the effectiveness of the proposed controller in terms of torque ripple suppression, steady-state and transient performance, and load disturbance rejection.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it