Optimal Control of Nanosatellite Fast Deorbit Using Electrodynamic Tether
Bibliographic record
Abstract
This paper proposes a piecewise two-phased optimal control scheme for fast nanosatellite deorbit by a short electrodynamic tether. The first phase concerns the open-loop control trajectory optimization, where the optimal control problem is formulated only for the tether libration motion by assuming the slow-varying orbital elements of the electrodynamic tether system as constant within a discretized interval. The second phase deals with the closed-loop optimal control for tracking the derived optimal reference trajectory subject to multiple major orbital perturbations. The finite receding horizon control method is used in the optimal trajectory tracking. Both optimal control problems are solved by a direct collocation method based on the Hermite–Simpson method using discretization schemes with coincident nodes. The resulting nonlinear programming problem significantly reduces the problem size and improves the computational efficiency. Numerical results for fast nanosatellite deorbit by an electrodynamic tether in both equatorial and highly inclined orbits show the proposed method achieves high control accuracy and efficiency.
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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) | 0.000 | 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 | 0.001 |
| 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".