Computational control framework for tethered space systems using finite element method
Bibliographic record
Abstract
This paper develops a computational feedback control framework for flexible tethered space systems by integrating dynamic models, state estimation, and optimal control with a moving horizon into a cohesive procedure using the finite element method. A high-fidelity dynamic model of the tethered space system is developed using the nodal position finite element method. The positions and velocities of the entire tether are estimated by a robust finite element Kalman state estimator. Then, optimal control in the finite element form is proposed to control the geometrical profile of flexible space tethers. The optimal control problem is recast into canonical equations by Hamilton's variational principle as a two-point boundary value problem with a moving terminal boundary condition. The control inputs are generated numerically from the canonical equations for closed-loop feedback control using a moving horizon. The effectiveness of the proposed computational optimal feedback control framework is verified through numerical simulations of an initially bent tethered space system. Analysis results show the proposed framework can be extended to other flexible elastic systems, indicating its potential for broader applications. The key advantages of the framework are algorithmic controller synthesis within a variational framework, preservation of high-fidelity of dynamic model in controller synthesis, and numerical algorithms in place of closed-form control commands for more adaptive and flexible control solutions. • Proposed computational optimal feedback control framework with state estimator • Derived optimal control by NPFEM using Hamilton's variational principle • Adopted FE-Kalman state estimator for full tether state estimation • Verified computational control framework through numerical simulations
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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".