Normal-Operation-Undisturbed Magnet Flux Linkage Monitoring in PMSM Drives via a Mechanical-Model-Based Dual Time-Scale Approach
Bibliographic record
Abstract
Accurate identification of magnet flux linkage is of great importance for the condition monitoring and control optimization of permanent magnet synchronous motor (PMSM) drives. In this work, a normal-operation-undisturbed magnet flux linkage monitoring technique using a mechanical-model-based dual time-scale approach is presented. The proposed technique is composed of two Adaline-type asymptotic observers, which operate at different time scales and provide updates to each other to guarantee accuracy and cope with rank deficiency during the identification. With the aid of Lyapunov theory, a dynamic learning factor is ingeniously designed for each of the two Adaline-type asymptotic observers, which yields powerful noise immunity and helps to guarantee observer stability. In comparison to existing magnet flux linkage identification solutions, the proposed method is impervious to inverter nonlinearity and magnetic saturation, and meanwhile, it circumvents the need for harmonic signal injection and control structure alteration so as to eliminate the resulting possibility of affecting normal motor operations. Simulations, along with real-time experiments under different temperatures, loads, and speeds, are presented to demonstrate the feasibility of the proposed technique and its capability to accurately monitor the magnet flux linkage.
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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 | 0.001 |
| 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 | 0.001 |
| 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".