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Record W1967028238 · doi:10.1115/dscc2009-2665

Hybrid Control of a Pneumatic Gantry Robot for Contour Tracking: Proportional Pressure Versus Proportional Flow Control

2009· article· en· W1967028238 on OpenAlexaff
Mohammed Abu Mallouh, Brian Surgenor, Sasan Taghizadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsControl theory (sociology)Proportional controlTracking (education)Compensation (psychology)PID controllerRobotOffset (computer science)Pneumatic flow controlController (irrigation)Artificial neural networkControl engineeringControl systemComputer scienceControl valvesEngineeringControl (management)Artificial intelligenceTemperature controlMechanical engineering

Abstract

fetched live from OpenAlex

The application of a pneumatic gantry robot to contour tracking is examined. A hybrid controller is structured to control the contact force and the tangential velocity, simultaneously. In a previous study, experimental contour tracking results for the robot were obtained with electronic proportional pressure control (PPC) valves. The results demonstrated the potential of pneumatic actuation for contour tracking applications. In another study it was found that improvement in performance was limited by system lag and Coulomb friction. A neural network (NN) compensator was developed to counter both effects. Simulation results demonstrated the effectiveness of the NN compensator. Although improvement in performance with NN compensation was significant, this was offset by the requirement for substantive design effort. This paper shows experimentally that equally significant improvement can be achieved by switching from PPC valves to proportional flow control (PFC) valves. The PFC approach requires less design effort.

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 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 score0.979
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.232
Teacher spread0.218 · 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.

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

Citations0
Published2009
Admission routes1
Has abstractyes

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