Parameter estimation and control of an automatic balancing system for CubeSat research and applications
Bibliographic record
Abstract
Deployed for purposes of GPS, defense, atmospheric and space research, environmental monitoring, broadcasting, and communication, Earth observation satellites are complex systems that require the design of highly reliable control and estimation algorithms. A satellite’s Attitude Determination and Control System (ADCS) must be able to operate accurately, in a robust manner against unexpected conditions, especially in missions that demand more intricate tasks. The desire for optimal and robust performance in satellites has been the driving factor behind decades of attitude control research. With computers, the performance of spacecraft subject to some mission can be simulated to test new control methods, but the availability of real satellites to researchers for testing these algorithms is very limited. To solve this issue, attitude control simulators have been developed, such that algorithms and hardware can be tested inexpensively in a lab environment, while maintaining a high level of accuracy to the environment it emulates. The Nanosatellite Attitude Control Simulator (NACS) has been developed at McMaster University for this purpose. Consisting of a mock 1U CubeSat, an air-bearing configuration, and an Automatic Balancing System (ABS), rotational attitude control experiments are conducted in-lab without deployment, simulating the zero-gravity of space. The mechanism responsible for environment simulation is the ABS, which minimizes residual torque due to gravity by influencing the center of mass (CoM) of the system, thereby improving control performance and efficiency. The performance of the ABS in a balancing task is presented, where system parameters of inertia and CoM are estimated from response data. Three filtering strategies are investigated for this purpose, providing varying degrees of accuracy and computational cost.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".