A Current Control Scheme With an Adaptive Internal Model for Torque Ripple Minimization and Robust Current Regulation in PMSM Drive Systems
Bibliographic record
Abstract
This paper addresses the problem of uncertainties in practical permanent magnet synchronous motors (PMSMs), and proposes a simple adaptive internal model within the current feedback and reference current generation structure as a solution. Due to the time varying nature and the high-bandwidth property of uncertainties in a practical PMSM drive system, the internal model is simply chosen as the estimated uncertainty function. To provide a high bandwidth estimate of the uncertainty function with high-noise immunity, a simple adaptation law is derived, in the sense of Lyapunov functions, using the nominal current dynamics. The inclusion of the frequency modes of the disturbances to be eliminated (the flux harmonics and voltage disturbances caused by parameter variation) in the stable closed-loop system introduces very high-attenuation at different frequency modes corresponding to uncertainty modes. Therefore, a robust torque ripple minimization and current regulation performances are yielded. To properly tune the proposed scheme, a stability analysis based on a discrete-time Lyapunov function has been used to determine the stability limits of the adaptation gain. Comparative evaluation results are presented to demonstrate the effectiveness of the proposed control scheme under different operating conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".