A Current Control Scheme with an Adaptive Internal Model for Robust Current Regulation and Torque Ripple Minimization in PMSM Vector Drive
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 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, which adaptively varies with different operating conditions. Subsequently, the frequency modes of the uncertainty function are embedded in the control effort, and a robust current control performance is yielded. Furthermore, the inclusion of the estimated uncertainty function provides an efficient solution for torque ripple minimization in PMSM drives. This is because the frequency modes of the disturbances to be eliminated i.e., the flux harmonics, are included in the stable closed loop system. As a result, the controller can introduce a very high attenuation gains at different frequency modes corresponding to the flux harmonics. To provide a high bandwidth estimate of the uncertainty function, a simple adaptation law is derived, in the sense of Lyapunov functions, using the nominal current dynamics. 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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".