Application of Predictive Control for Autonomous Satellite Capture Using a Deployable Manipulator System
Bibliographic record
Abstract
Two prototypes of a robot known as the Multi-module Deployable Manipulator System (MDMS) have been developed in our laboratory. This manipulator is intended for carrying for research in space platform-based robotics. In the present paper, this manipulator is used in laboratory-simulated capture of an orbiting satellite. A model-based predictive controller is developed and implemented in the MDMS. Using the controlled manipulator, laboratory experiments are carried out to study autonomous capture of a free floating and spinning target. The objective is to evaluate the performance of the predictive controller for the capturing task. A planar prototype MDMS is used as the chaser robot in experiments, and the target statellite is simulated within the controller. The model of the target statelite assumes that it is free of any external torques and involves rotations within the plane of operation of the MDMS prototype. The predictions for the chaser robot are based on a model of the MDMS, linearized about the starting configuration of the task. An unconstrained predictive controller is used in the present experiments. The resluts from tracking various moving target are presented, with and without predictions of the target movement. The performance is shown to be quite satisfactory.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".