Passivity-Based Model Predictive Control for Tethered Despin of Massive Space Objects by Small Space Tug
Bibliographic record
Abstract
This paper studies the stable control of tethered despin of a massive rotating space object in a central gravity field by a small space tug. A control scheme under the passivity-based Model Predictive Control framework is designed to ensure the constraints of state and input (bounded libration angle, positive tether tension and bounded thrust) are satisfied. Furthermore, an additional passivity constraint is introduced into the passivity-based Model Predictive Control to guarantee the asymptotic stability of the closed-loop control system. The attainable equilibrium configuration of the tethered system is first analyzed. Then, a framework of the storage energy function is constructed by the potential energy shaping methodology to establish the passivity mapping of the tethered system from input to output. Finally, the strictly asymptotic stability of despinning control of the tethered system under the proposed control law is theoretically proved by the Invariance theorem and Lyapunov stability theory. The effectiveness of the proposed control scheme is verified by numerical simulation.
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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) | 0.001 | 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".