Immersion and Invariance Adaptive Control for Proximity Operations under Uncertainties and Modeling Errors
Bibliographic record
Abstract
Two Immersion and Invariance (I&I) adaptive controllers are designed for translational trajectory tracking in spacecraft proximity operations under unknown mass properties and unmodeled dynamics. In one case, the controller is designed utilizing the Clohessy-Wiltshire dynamics, requiring known mass bounds and I&I extension methods such as filtered-states and dynamic scaling factors. For the second case, the I&I controller is developed by approximating the exact nonlinear relative dynamics as double-integrator dynamics through assumptions such as short time scales relative to one orbital period, and small relative distances compared to an orbital radius. These simplified dynamics allow for an analytical I&I controller without assumed bounds on mass, or any extension methods. Simulations are presented which show that the designed I&I estimators converge nearly precisely to the actual mass value, regardless of higher-order dynamical modeling errors. In addition, simulation comparisons to standard adaptive techniques such as Indirect Regressor-Matrices and Direct Simple Adaptive Control show improved transient performance and reduced control effort. The I&I controller developed under double-integrator dynamics is computationally light requiring only one numerically solved state and two analytical equations, making itwell-suited for on-board real-time applications.
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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 | 0.000 | 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".