Implementation of type-II tip-tilt control in NFIRAOS with woofer-tweeter and vibration cancellation
Bibliographic record
Abstract
In a previous paper, we have proposed to implement a type-II controller in NFIRAOS, the Narrow Field Infra Red Adaptive Optics System for the Thirty Meter Telescope. Type-II control enables increased tip-tilt rejection, which, for a given error budget, translates into increased sky-coverage. Our proposed type-II controller is a cascade of two integrators, a gain and a lead filter. The correction is then split between the tweeter (the deformable mirror surface) and the woofer (a tip-tilt stage that holds the deformable mirror) using high and low pass filters. So far, we had only characterized this controller in the continuous domain, where the discrete nature of the real-time computer part is approximated by continuous functions (Laplace analysis). In this paper, we discuss the discrete implementation, with particular focus on a) anti-windup, to robustly deal with temporary saturations, and b) low sampling rates, where frequency warping and aliasing may occur in the discretization process. The implementation is tested in a hybrid Simulink model, where continuous and discrete processes are properly implemented using continuous or discrete blocks, respectively, and the performance is compared with the performance predicted by the continuous domain analysis. An effective saturation handling strategy is also proposed. Finally, we analyze the implementation of dedicated algorithm to further attenuate narrow band vibrations. These techniques include a traditional notch filter, whose performance is compared to a more advanced adaptive vibration cancelation algorithm (AVCA). We find that the AVCA can correctly reject large amplitude vibrations, even when the AO sampling frequency is low.
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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) | 0.003 | 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".