Dynamics-Informed hierarchical sliding mode control for libration of partial space elevators in cargo transportation
Bibliographic record
Abstract
This study presents an original analysis of out-of-plane libration dynamics and its control in partial space elevator cargo transportation. A comprehensive three-dimensional dynamic model of the partial space elevator has been derived to examine the effects of out-of-plane libration. To ensure a stable in-plane configuration, a coupling-free analytical speed function is developed that cancels out the coupling effects stemming from out-of-plane libration. A novel approach is developed to introduce a thrust control strategy for the elimination of out-of-plane libration using a virtual dynamic-based hierarchical sliding mode control law. By employing a new combined channel implementation mode, it is demonstrated that in and out-of-plane libration can be controlled with a single thruster, resulting in reduced fuel consumption. The stability of the proposed control strategy is proven within the Lyapunov framework. Numerical simulations validate the effectiveness of the control strategy and highlight its significance. Results show that out-of-plane libration increases the magnitude of in-plane libration, while climber movement amplifies out-of-plane libration. The control strategy successfully eliminates out-of-plane libration using limited control input, ensuring stable cargo transportation in the partial space elevator.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".