Application of Second-Order Sliding-Mode Concepts to Active Magnetic Bearings
Bibliographic record
Abstract
Rotor mass imbalance is a common problem to rotating machines due to the unavoidable imperfections in manufacturing. These imbalance forces can be viewed as harmonic disturbances which lead to a periodic rotor runout during rotation. Furthermore, the runout length increases with the rotational speed squared. Moreover, for variable rotational speed applications, these harmonic disturbances are also time-varying. Active magnetic bearings (AMB) provide a means of actively attenuating these disturbances. Although various imbalance compensation schemes have been proposed in the literature to handle this problem, they are often more suitable for constant rotational speed applications where disturbances can be handled at a predetermined rotational speed. This study proposes the application of second-order sliding-mode control (2-SMC) to regulate AMB systems throughout a wide operating speed range. The proposed controllers are composed of two components. The first component is a linear controller for the sake of stabilizing the inherently unstable system, while the second component is a 2-SMC to handle the model uncertainties of the system as well as the exogenous harmonic disturbances. Simulation and experimental results are provided to demonstrate the effectiveness and superiority of the proposed techniques compared to the conventional linear controller.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".