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Record W2100123450 · doi:10.1109/acc.2006.1657203

QFT synthesis of a position controller for a pneumatic actuator in the presence of worst-case persistent disturbances

2006· article· en· W2100123450 on OpenAlexaff
Mark Karpenko, Nariman Sepehri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsControl theory (sociology)ActuatorQuantitative feedback theoryPosition (finance)Controller (irrigation)Pneumatic actuatorControl engineeringPosition trackingComputer scienceUpper and lower boundsStability (learning theory)Robust controlEngineeringControl (management)Control systemMathematics

Abstract

fetched live from OpenAlex

Quantitative feedback theory (QFT) is employed in this paper to design a simple and effective position controller for a typical industrial pneumatic actuator. An emphasis is placed on minimizing the effects of load disturbances. The QFT control law is designed to give the best attenuation of the prescribed worst-case persistent load disturbance in spite of practical limitations on the achievable closed-loop performance and in the presence of other design constraints including closed-loop tracking and stability. The efficacy of the control law is verified in simulations and the guaranteed upper bound on the position error due to the worst-case persistent disturbing load is determined. This paper also highlights some of the issues related to QFT design for pneumatic actuators in the presence of load disturbances including dry friction that, to the best of the authors' knowledge, have not been adequately detailed elsewhere

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: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.009
GPT teacher head0.202
Teacher spread0.194 · 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

Citations16
Published2006
Admission routes1
Has abstractyes

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