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Record W2073752657 · doi:10.1177/02783640022066842

On the Dynamics and Control of Flexible Multibody Systems with Closed Loops

2000· article· en· W2073752657 on OpenAlexaff
Christopher J. Damaren

Bibliographic record

VenueThe International Journal of Robotics Research · 2000
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPayload (computing)Control theory (sociology)PassivityFeed forwardController (irrigation)Property (philosophy)Control engineeringPosition (finance)Multibody systemComputer scienceRobotTorqueTracking (education)Robotic armKinematicsTrajectoryEngineeringControl (management)Artificial intelligencePhysics

Abstract

fetched live from OpenAlex

The motion control problem for cooperating flexible robot arms manipulating a large rigid payload is considered. An output that depends on the payload position and contributions from the joint motion of each arm is constructed whose rate yields the passivity property with respect to a special input. The input is a combination of the torques from each arm and contains a free load-sharing parameter. The passivity property is shown to depend on the payload mass properties, and in cases where the payload is large, a passivity-based controller combining feedforward and feedback as elements is devised, which yields tracking. An experimental facility consisting of two planar 3-DoF arms is used to implement the strategies. Good tracking is observed and compared with simulation predictions for closed-loop flexible multibody systems.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.282
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations44
Published2000
Admission routes1
Has abstractyes

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