Mode Localization in Flexible Spacecraft: A Control Challenge
Bibliographic record
Abstract
Mode localization is often an unexpected phenomenon in e exible space structures because it arises from very small manufacturing errors. It will occur, however, in structures comprising lightly damped, weakly coupled repeating members that have tightly clustered modes of vibration. If not properly taken into account, mode localization can potentially pose problems for high-performance e exible spacecraft controllers relying on the assumption of perfect symmetry. In cases where actuation and sensing are limited, local decentralized control design for each structural member relies on the propagation of energy from one member to the next through channels of weak coupling. Mode localization can severely inhibit this propagation leading to control performance degradation. A solution to this problem is to design controllers based on models consisting of multiple members of the structure or to centralize control design. Because mode shape errors can be large when modes localize, however, any uncertainty model that attempts to capture this information will lead to reduced control authority, overconservatism, and performance losses. It is shown that eigenvalue perturbation models are an effective means by which to design controllers fore exible space structures subject to mode localization. The successful control of a structurewith modelocalization isdemonstrated through experimentsconducted ona e exiblespacecraftemulator that exhibits the phenomenon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".