Robust control of large flexible space structures using a coprime factor plant description
Bibliographic record
Abstract
Dynamics of large flexible space structures (LFSS) characterized by their high order and their significant number of closely-spaced, lightly-damped, clustered low-frequency modes. Finite-element (FE) models of LFSS are known to be accurate only for the first few modes of the structure. Moreover, these models do not provide the modal damping ratios. Model identification of LFSS is often impractical because such structures are assembled in space. Thus it would be desirable to have a design procedure that would directly use an uncertain FE model to produce a controller that could be implemented on real LFSS with good confidence. This paper presents a simple description of uncertainties in LFSS as stable perturbations in the factors of a nominal left-coprime factorization (LCF) of LFSS dynamics. This leads to a better, less conservative description of the uncertainty set and hence improves achievable closed-loop performance.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".