Formulation of Torque-Optimal Guidance Trajectories for a CubeSat with Degraded Reaction Wheels
Bibliographic record
Abstract
This paper presents the theory and design of a torque-optimal guidance algorithm for CubeSat applications. CubeSats and nano-satellites provide mission-exible low-cost platforms for the academic and scientic communities to conduct cutting-edge research in the harsh environment of space. The mission life of nano-satellites may be limited by the attitude actuators, and it is therefore benecial to reduce torque and angular momentum usage during reorientation maneuvers. The algorithm focuses on being computationally lightweight and robust, while including the eects of gyroscopic moments, environmental torques, and degraded reaction wheels. Results indicate that this torque-optimal guidance algorithm demonstrates substantial improvements in performance and pointing accuracy over an Eigenaxis controller for similar maneuvers, with low to moderate computational overhead. In doing so, it presents a signicant advancement towards the development of intelligent GN&C systems for small satellites.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".