Simple Adaptive Control for Spacecraft Trajectory Tracking Under Uncertainties and Perturbations
Bibliographic record
Abstract
Orbital debris is a growing problem that poses a collision risk to spacecraft and is expected to worsen exponentially according to Kessler syndrome as each collision between two bodies fragments them into three or more.Active removal of debris by a chaser spacecraft is necessary to prevent runaway growth in the number of objects but removal is complicated by the presence of perturbation forces and uncertainties about debris parameters such as mass.Capturing and towing debris and performing similar proximity operations will require a control strategy that can perform trajectory tracking that is robust to these uncertainties.This thesis presents the use of Simple Adaptive Control (SAC), a direct adaptive control method, to perform trajectory tracking in the presence of mass and perturbation uncertainties.A new laboratory facility was developed for this work which included an air bearing testbed and robotic platforms representing spacecraft.The author contributed significantly to the development of this facility, notably creating the software environment that enabled simulations and experiments.The main experiment in this work involves a chaser platform performing a circular inspection trajectory around a target, representing an important phase in debris removal or other proximity operations.Four different scenarios were tested with different conditions: nominal conditions which involved no perturbations or mass uncertainties, increasing the mass by 50% without informing the system or retuning control gains, subjecting the system to perturbation forces, and finally combining the mass change with perturbations for the worst case scenario.The trajectory tracking performance of SAC and PD control were compared in all four scenarios.Both simulation and experimental results agree that SAC is a viable control strategy for this purpose that significantly outperforms a PD controller in all the conditions considered, notably providing more consistent performance across several trials in the presence of randomly fluctuating perturbations.It was also discovered that perturbations had a more significant impact on trajectory tracking performance than mass changes in this context, though the combined effect had the largest impact.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".