Mixed-Sensitivity $H_\infty$ Control of Magnetic-Fluid-Deformable Mirrors
Bibliographic record
Abstract
This paper presents an H∞controller designed to cancel dynamic wavefront aberrations in an adaptive optics (AO) system based on a magnetic-fluid-deformable mirror (MFDM). MFDMs are a recently proposed novel type of active optical elements called wavefront correctors, which constitute the central part of AO systems. They offer cost and performance advantages over existing wavefront correctors. MFDMs have been found particularly suitable for ophthalmic imaging systems where they can be used to compensate for the complex optical aberrations in the eye that blur the images of the internal parts of the eye. However, their practical implementation in clinical devices is hampered by the lack of effective methods to control the shape of their deformable surface. Specifically, control algorithms that can be used to cancel dynamically varying wavefront aberrations need to be developed. This paper presents one such control algorithm that can be used to compensate for high-order time-varying optical aberrations using an MFDM. The control algorithm is developed using the mixed-sensitivity H∞design method that enables the tracking of the desired MFDM surface shape and also limits the magnitude of the control currents applied to the MFDM. Experimental results showing the performance of a closed-loop system comprising the developed controller and a 19-channel prototype MFDM are presented.
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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 | 0.000 |
| 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 | 0.001 | 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".