Bibliographic record
Abstract
Electrodynamic tethers (EDTs) provide a compact, propellant-free method for space debris removal but face significant challenges due to unstable libration motion induced by interactions with Earth's magnetic and gravitational fields. Current stabilization methods, which assume a straight tether, become ineffective when the tether curves during large librations. Additionally, the tether's thin nature precludes the placement of sensors along its length to directly measure its geometric profile for control purposes. This paper proposes a novel solution for these challenges by employing model predictive control (MPC) to regulate the geometric profile of librating EDTs, using the induced electric current as the sole control input. A high-fidelity multiphysics model, developed using the nodal position finite element method and orbital-motion-limited theory, estimates the tether's geometric profile based on measurable positions and velocities at its ends. An extended Kalman filter (EKF) is applied to reconstruct the non-measurable tether profile between the ends, addressing potential singularities and ambiguities in estimation. The control problem is reformulated as a trajectory-tracking task for the underactuated EDT system, where MPC optimizes the current within physical and control constraints to maintain tether straightness and minimize libration angles. Numerical simulations confirm the proposed approach effectively aligns the tether with the reference trajectory, significantly enhancing stability and mitigating libration. This framework provides a robust solution for stabilizing EDTs in orbital debris removal missions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".