Bibliographic record
Abstract
Autonomous multiple spacecraft formation flying missions demand the development of reliable control systems to ensure rapid, accurate, and effective response to various attitude and formation reconfiguration commands. Keeping in mind the complexities involved in the technology development to enable spacecraft formation flying, this thesis presents the development and validation of a fault tolerant control algorithm that augments the AOCS on-board a spacecraft to ensure that these challenging formation flying missions will fly successfully. Taking inspiration from the existing theory on nonlinear control, a fault-tolerant control system for the RyePicoSat missions is designed to cope with actuator faults whilst maintaining the desirable degree of overall stability and performance. Autonomous fault tolerant adaptive control scheme for spacecraft equipped with redundant actuators and robust control of spacecraft in underactuated configuration, represent the two central themes of this thesis. The developed algorithms are validated using a hardware-in-the-loop simulation. A reaction wheel testbed is used to validate the proposed fault tolerant attitude control scheme. A spacecraft formation flying experimental testbed is used to verify the performance of the proposed robust control scheme for underactuated spacecraft configurations. The proposed underactuated formation flying concept leads to more than 60% savings in fuel consumption when compared to a fully actuated spacecraft formation configuration. We also developed a novel attitude control methodology that requires only a single thruster to stabilize three axis attitude and angular velocity components of a spacecraft. Numerical simulations and hardware-in-the-loop experimental results along with rigorous analytical stability analysis shows that the proposed methodology will greatly enhance the reliability of the spacecraft, while allowing for potentially significant overall mission cost reduction.
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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) | 0.000 | 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) | 0.001 | 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".