Spacecraft Proximity Operations Under Motion and Input Constraints: A Learning-Based Robust Optimal Control Approach
Bibliographic record
Abstract
This article proposes a learning-based robust control scheme for spacecraft proximity operations under motion constraints (i.e., approaching path and sensor field-of-view constraints), input saturation, and external disturbances. To enhance the robustness of the learning algorithm, a disturbance observer with finite-time convergence is first designed to provide accurate model information for online learning. By virtue of the barrier functions and hyperbolic tangent functions, a performance index is developed, which incorporates both motion and input constraints into the framework of adaptive dynamic programming. Then, following the actor–critic structure, an approximate optimal saturated control policy is obtained using two neural networks (NNs), wherein the weights of the NNs are updated online. It is shown that the derived controller can guarantee the boundedness of system states and network weight estimation errors, while ensuring the satisfaction of motion and input constraints despite the presence of external disturbances. Finally, numerical simulations are carried out for spacecraft proximity operations with a tumbling target to verify the effectiveness of our proposed method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".