MétaCan
Menu
Back to cohort
Record W2028777387 · doi:10.1115/detc2007-34291

Hybrid Force/Velocity Control of a Pneumatic Gantry Robot for Contour Tracking: Tuning and Model Validation

2007· article· en· W2028777387 on OpenAlexaff
Mohammed Abu Mallouh, Brian Surgenor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)Tracking (education)Compensation (psychology)Focus (optics)RobotComputer sciencePID controllerWork (physics)Contact forceControl engineeringSimulationEngineeringControl (management)Artificial intelligenceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

The paper examines hybrid force/velocity control of a pneumatic gantry robot for contour tracking. Both experimental and simulation results are presented. The control system is structured to control the contact force and the tangential velocity simultaneously. Controller tuning and model validation results are given for a fixed gain PI-based hybrid force/velocity controller. A simple yet effective model is presented in sufficient detail such that other researchers can perform their own simulations to investigate the utility of their own controller designs. The model is used to demonstrate the negative effects of Coulomb friction. Future work will focus on friction compensation techniques to improve performance.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
Research integrity0.0010.000
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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

Explore more

Same topicIterative Learning Control SystemsFrench-language works237,207