Servo design and analysis for the Thirty Meter Telescope primary mirror actuators
Bibliographic record
Abstract
The Thirty Meter Telescope has 492 primary mirror segments, each incorporated into a Primary Segment Assembly (PSA), each of which in turn has three actuators that control piston, tip, and tilt, for a total of 1476 actuators. Each actuator has a servo loop that controls small motions (nanometers) and large motions (millimeters). Candidate actuators were designed and tested that fall into the categories of "hard" and "soft," depending on the offload spring stiffness relative to the PSA structural stiffness. Dynamics models for each type of actuator are presented, which respectively use piezo-electric transducers and voice coils. Servo design and analysis are presented that include assessments of stability, performance, robustness, and control structure interaction. The analysis is presented for a single PSA on a rigid base, and then using Zernike approximations the analysis is repeated for 492 mirror segments on a flexible mirror cell. Servo requirements include low-frequency stiffness, needed for wind rejection; reduced control structure interaction, specified by a bound on the sensitivity function; and mid-frequency damping, needed to reduce vibration transmission. The last of these requirements, vibration reduction, was found to be an important distinguishing characteristic for actuator selection. Hard actuators have little inherent damping, which is improved using PZT shunt circuits and force feedback, but still these improvements were found to result in less damping than is provided by the soft actuator. Results of the servo analysis were used for an actuator down-select study.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".