Bibliographic record
Abstract
P RACTICAL spacecraft attitude control systems must operate in the presence of disturbances, modeling errors, and actuator limitations. These issues have been the subject of much research interest. Adaptive control, where the unknown system parameters are estimated adaptively, is one of the proposed approaches for dealing with modeling uncertainty (see, for example, [1–4]). Both [1,2] deal with the attitude tracking problem, but they do not treat disturbances or actuator saturation. Reference [3] also deals with the tracking problem; it includes actuator saturation but not disturbances. Reference [4] includes bounded disturbances, but it does not treat actuator saturation, and it only deals with the attitude regulator problem. Recently, new control laws have been obtained that treat both disturbances and actuator limitations simultaneously [5–8]. References [5,6] deal with the attitude regulation problem only. References [7,8] treat the attitude tracking problem, and both present globally convergent control laws, given bounds on the spacecraft inertia matrix and the disturbances. The advantage of these approaches is that the form of the disturbance need not be known, only the bound. On the other hand, these approaches have no ability to learn the system model, which could be a useful feature if the attitude motion is to be optimized. This Note shows that, when an adaptive attitude control law based on the form given in [2] is appropriately designed, any linearly parameterizable disturbances can be accommodated; the closed-loop system is stable, with asymptotic tracking in the presence of actuator saturation. The unknown system parameters are learned adaptively.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".