Model-based controllers for CubeSat ORU installation: A comparative study
Bibliographic record
Abstract
The increasing space debris population in critical orbits due to spacecraft failure dictates the need for action. On-Orbit Servicing (OOS) has been proposed as a method for mitigating this trend by repairing existing space assets. The development of large servicing spacecraft has been given significant attention. In this paper, we are approaching the problem with a new paradigm for a servicing spacecraft by proposing a CubeSat class servicer equipped with a one-degree-of-freedom (DoF) robotic arm. Considering the OOS context, we choose the Orbital Replacement Unit (ORU) installation on a target spacecraft as a challenging benchmark task for the proposed servicer configuration. To carry out this task, we formulate and compare performance of four controllers to achieve coordinated control of both the CubeSat and the robotic arm for the installation task. We start with a basic PD controller and progress to the more advanced impedance controller, Model Predictive Control (MPC) and Model Predictive Impedance Control (MPIC) designs. The controllers are evaluated and compared across several relevant metrics for the ORU installation and their merits for practical implementation are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".