MétaCan
Menu
Back to cohort
Record W2117131289 · doi:10.1115/1.4025755

Position Domain Synchronization Control of Multi-Degrees of Freedom Robotic Manipulator

2013· article· en· W2117131289 on OpenAlexafffund
P. R. Ouyang, V. Pano

Bibliographic record

VenueJournal of Dynamic Systems Measurement and Control · 2013
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPosition (finance)Synchronization (alternating current)Control theory (sociology)Domain (mathematical analysis)Computer scienceEquidistantMotion (physics)Nonlinear systemLyapunov functionControl engineeringMotion controlTracking (education)Time domainArtificial intelligenceControl (management)Computer visionRobotEngineeringMathematics

Abstract

fetched live from OpenAlex

In this paper, a new position domain synchronization control (PDSC) law is proposed for contour control of multi-DOF nonlinear robotic manipulators with the main goal of improving contour tracking performance. The robotic manipulator is treated as a master-slave motion system, where the position of the master motion is used as an independent reference via equidistant sampling, and the slave motions are described as functions of the master motion. To build this relationship, the dynamics of the original system is transformed from time domain to position domain. The new control introduces synchronization and coupled errors in the control law to further coordinate the master and slave motions. Stability analysis is performed based on the Lyapunov method for the proposed PDSC, and simulations are conducted to verify the effectiveness of the developed control system.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.189
Teacher spread0.180 · 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 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

Citations21
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Dynamic Systems Measurement and ControlSame topicIterative Learning Control SystemsFrench-language works237,207