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Record W2128884975 · doi:10.1109/robot.2002.1013660

High steady-state accuracy pneumatic servo positioning system with PVA/PV control and friction compensation

2003· article· en· W2128884975 on OpenAlexaff
Shu Ning, Gary M. Bone

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsMcMaster University
Fundersnot available
KeywordsControl theory (sociology)Compensation (psychology)ActuatorSteady state (chemistry)ServomechanismServoServo controlPneumatic actuatorElectrohydraulic servo valveEngineeringNonlinear systemController (irrigation)ServomotorControl valvesPower (physics)Control engineeringComputer scienceControl (management)Mechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Pneumatic servo actuators have the benefits of low-cost, cleanliness and a high power-to-weight ratio. However, their relatively poor accuracy prevents them from competing with electro-mechanical systems when higher accuracy is needed. The cause of the steady-state error for a pneumatic servo system with an open-center servo valve is investigated. Full nonlinear and linearized plant models are presented. An effective friction compensation method is introduced which can be added to any control strategy. When combined with a novel PVA/PV control approach, a steady-state accuracy of /spl plusmn/0.01mm was verified in experiments. This is a tenfold improvement over previously reported experimental results for such systems. This performance is achieved for both vertical and horizontal movements with payloads ranging from 0.3 to 11.3kg, without re-tuning the controller.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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

Citations50
Published2003
Admission routes1
Has abstractyes

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