Distributed and Centralized H∞ Control of Large Segmented Telescopes
Bibliographic record
Abstract
Next generation telescopes are to employ segmented mirrors to realize extremely large primary mirror surfaces. Most of the current ground-based telescopes has monolithic mirrors with radius upto 8 metres. Due to limitations segmentation is preferred for larger size mirrors. Segmentation of mirrors brings a challenging task of controlling the vast number of individual units. In this paper, the H∞ control of the primary mirror of the next generation telescopes are investigated. Both spatially-invariant distributed and centralized controllers are designed for simplified dynamic model of a 37 segment test unit. Firstly, the 37 segment system is modelled by adopting a nodal model. Secondly, an analytic calculation of a H∞ controller is presented. A centralized H∞ controller is, then, designed and simulated in MatLab-Simulink environment. Next, the simulation results are presented and the performance of the controller is evaluated. Thirdly, spatially-invariant distributed controller synthesis is described and a spatially-invariant distributed controller is designed for 37 segment system by controller truncation. The spatially-invariant distributed controller is simulated for the 37 segment system. The simulation results of the controller is presented and compared with the results from centralized scheme. It is shown that both centralized and spatially-invariant distributed controllers satisfy the imaging performance requirements.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".