Design of Attitude Control Systems for CubeSat-Class Nanosatellite
Bibliographic record
Abstract
We present a satellite attitude control system design using low-cost hardware and software for a 1U CubeSat. The attitude control system architecture is a crucial subsystem for any satellite mission since precise pointing is often required to meet mission objectives. The accuracy and precision requirements are even more challenging for small satellites where limited volume, mass, and power are available for the attitude control system hardware. In this proposed embedded attitude control system design for a 1U CubeSat, pointing is obtained through a two-stage approach involving coarse and fine control modes. Fine control is achieved through the use of three reaction wheels or three magnetorquers and one reaction wheel along the pitch axis. Significant design work has been conducted to realize the proposed architecture. In this paper, we present an overview of the embedded attitude control system design; the verification results from numerical simulation studies to demonstrate the performance of a CubeSat-class nanosatellite; and a series of air-bearing verification tests on nanosatellite attitude control system hardware that compares the performance of the proposed nonlinear controller with a proportional-integral-derivative controller.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".