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Record W4255975277 · doi:10.32920/ryerson.14649819.v1

Detumbling a Non-Cooperative Space Target With Unknown Inertial Parameters Using a Space Manipulator Subjected to End-Effector Force/Torque Limits

2021· preprint· en· W4255975277 on OpenAlexaff
Rabindra A. Gangapersaud

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsInertial frame of referenceTorqueControl theory (sociology)InertiaRobot end effectorFictitious forceEngineeringSpacecraftRobotComputer scienceControl (management)Aerospace engineeringPhysicsArtificial intelligenceClassical mechanics

Abstract

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This study addresses the problem of detumbling a non-cooperative space target, such as a malfunctioning satellite, using a space robot for the purpose of performing on-orbit servicing. The space robot is denoted as the servicer and consists of a satellite base equipped with a robotic manipulator. The formulation of a detumbling control strategy must respect limits on the grasping force and torque at the servicer’s end-effector without knowledge of the target’s inertial parameters (mass, inertia tensor, location of center of mass). In the literature, prior studies have formulated detumbling strategies under the assumption of accurate knowledge of the target’s inertial parameters. However, obtaining accurate estimates of the target’s inertial parameters is difficult, and parameter uncertainty may lead to instability and violation of the end-effector force/torque limits. This study will address the problem of detumbling a noncooperative target with unknown but bounded inertial parameters subjected to force/torque limits at the servicer’s end-effector. In this study, two detumbling control strategies are presented. The first detumbling strategy is presented under the assumption that force/torque measurements at the end-effector are available. Detumbling of the target is achieved by applying a reference force/torque to the target that is designed to bring the target’s tumbling motion to rest subjected to force/torque limits. To ensure stable detumbling of the target, a robust compensator is designed based on bounds of the target’s unknown inertial parameters. Furthermore, once the detumbling process starts, in order to reduce the robust control gains, bounds on the target’s unknown inertial parameters are estimated in real-time. The resultant detumbling controller enables the servicer to detumble the target while complying with the target’s unknown residual tumbling motion. The second detumbling control strategy is developed without the need of end-effector’s force/torque measurements and takes into account magnitude constraints on servicer’s control inputs in the detumbling controller’s design. Detumbling is achieved by tracking a desired detumbling trajectory that is delineated subjected to end-effector force/torque limits and requires bounds on the target’s inertial parameters. The hyperbolic tangent function is utilized to model the magnitude constraints on the servicer’s control inputs, resulting in a system that is non-affine in its control inputs. As a result, an augmented model of the servicer is presented to allow the formulation of the detumbling controller. Using bounds on the target’s inertial parameters, robust adaptive control approach is utilized to design the detumbling controller with the backstepping technique in order to track the desired detumbling trajectory and to reject the gained target’s momentum. Numerical simulation studies were conducted for both detumbling control strategies utilizing a servicer equipped with a 7-degree-of-freedom (DOF) manipulator. The results demonstrate that both control strategies are capable of detumbling a non-cooperative target with unknown inertial parameters subjected to force/torque limits. Experiments conducted with a 3-DOF manipulator demonstrate that the design procedure utilized to delineate the desired detumbling trajectory in the second detumbling strategy respects force/torque limits at the end effector. The study is concluded with a discussion comparing the two proposed detumbling strategies by highlighting their advantages and disadvantages.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.228
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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