Design Methodologies for Simple Adaptive Controllers with Applications to Spacecraft Proximity Operations
Bibliographic record
Abstract
Space debris in low-Earth Orbit is increasing year-on-year, with inaction threatening Kessler Syndrome, a point where debris collisions are self-sustaining and space launches are impossible. Conservative estimates suggest removing at least 10 pieces of large debris per year Conventional control techniques make it infeasible to manage the rendezvous, docking, and deorbit multiple pieces of debris every year. Advanced controllers, such as adaptive controllers which can sense and correct for deviations in systems with unknown or time-varying characteristics, are able to manage debris uncertainty without requiring costly or time-intensive design reformulations upon contact with each target. Simple adaptive control offers the ability to manage unknown or time-varying systems with guaranteed performance, and without intervention. Simple adaptive control varies linear control gains until adequate system performance is achieved. The current work improves implementation of simple adaptive control through novel design heuristics, application of optimization, and disturbance accommodation. Techniques are experimentally verified, and tested on a multipleinput-multiple-output simulation of a spacecraft's attitude and position during spacecraft rendezvous, docking, and post-docking control. Experimental results show that optimization is able to decrease the convergence time of a simple adaptive controller, and that disturbance compensation increases the model tracking of a simple adaptive controller. Design heuristics are able to provide a tangible method for determining simple adaptive control parameters. Furthermore, simulations show that simple adaptive control can uncouple unknown system dynamics, while improving the response. The provided work provides several methods and techniques to help designers implement simple adaptive control in physical systems, and improving those designs once they are implemented. Finally, several avenues for further research are proposed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".