Exact analytic technique for designing controller architecture in multi-axis active magnetic bearings with rotor eccentricities
Bibliographic record
Abstract
Multi-coil active magnetic bearing systems require an efficient controller design to maintain stability, maximize performance, and improve flexibility in high-speed transport systems, particularly those used in electric cars, military, and aerospace. By employing Fourier-based frameworks rather than conventional Laplace-based methods, the work reported here simplifies the AMBs controllers, offering more flexibility to complex dynamics and adaptability to non-linearity. For computational and mathematical brevity, the entire formulation has been linearized around a specific range of uncertainty in this work. The magnetic field pattern with rotor eccentricities is characterized by using a subdomain technique and a perturbation methodology. Complex magnetic field formulations are obtained by the estimation of zeroth and first-order formulae in polar coordinates. For every coil, predictive model-based interpolation functions are constructed and optimal controller settings for stability are found by eigenvalue evaluation. The study enhances accuracy and effectiveness by validating these analytical methods against numerical results, offering deeper insights into rotor dynamics and controller adjustments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".