Control System Design for Retinal Imaging Adaptive Optics Systems Using Orthonormal Basis Functions
Bibliographic record
Abstract
This paper presents a solution to control system design issues for membrane mirrors used in retinal imaging adaptive optics systems. Such systems allow for the early diagnosis of eye diseases through high resolution imaging of the retina. Optical defects in the eye, known as aberrations, distort the retinal images, hence reducing their resolution. A retinal imaging adaptive optics system makes use of a deformable mirror whose shape is adjusted in real time to cancel the aberration effects. Due to the unknown and time-varying nature of the aberrations in the eye, the main control problem addressed in this paper is the tracking of an unknown and time-varying shape for the membrane mirror. Since the desired shape of the mirror is unknown and time-varying, it is proposed in this paper to design a multivariable controller that is tuned online to converge to the controller needed to achieve regulation. This is done iteratively, by taking advantage of the Q-parameterization of stabilizing controllers, so that the controller will converge to the ideal controller. Most often, finite impulse response (FIR) filters are used to represent the Q-parameter. It is proposed in this paper to represent the Q-parameter using orthonormal infinite impulse response filter basis functions. Such basis functions yield faster convergence rates during parameter estimation, and a Q-parameter representation that is less sensitive to parameter variations from the desired parameters. This is particularly crucial for the proposed application, where small errors in a typical FIR representation for the Q-parameter can lead to significant performance degradation. Simulation results are presented to illustrate the performance of the proposed adaptive controller design approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".