Modeling and analysis of a class of linear reluctance actuators for advanced precision motion systems
Bibliographic record
Abstract
Reluctance actuators (RA) are a type of electromagnetic actuator that offer high forces for short range motions. The RA takes advantage of the electromagnetic reluctance force property in air gaps between the stator core and mover parts. The mover accelerates because the stator generates the magnetic flux that produces an attractive magnetic attraction between the stator and mover. Hysteresis and other non-linearities in the magnetic flux have an impact on the force and have a nonlinear gap dependency. It is demonstrated that the RA has the capacity to produce a force that is effective and suitable for millimeter-range high-acceleration applications. One application for the RA is the short-stroke stage of photolithography machines for example. The RA is available in a wide variety of configurations, such as CCore, E-Core, Maxwell, and Plunger-type designs. The RA requires precise dynamic models and control algorithms to help linearize the RA for better control and optimization. Some nonlinear dynamics include magnetic hysteresis, flux fringing, and eddy currents. The RA is shown to have a much higher force density than any other traditional actuator, with the main disadvantage being the nonlinear and hysteretic behaviour which makes it hard to control without proper dynamic and control models in place. It is important to model the RA accurately for better control. The output force can be significantly impacted by unequal offsets or asymmetries between the mover and stator. In the thesis that follows, a review of RA systems is performed, an investigation that shows the importance of including the mean path length (MPL) term for higher accuracy, a technique for calculating the force of various asymmetrical instances for the C-core RA is demonstrated. This thesis documents currently available knowledge of the RA such as available applications, configurations, dynamic models, measurement systems, and control systems for the RA. The findings presented can allow for future control systems to be designed to counteract multi-axial asymmetric issues of the RA.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".