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Record W2148652377 · doi:10.1109/robot.1997.614360

Development of a hydraulic manipulator servoactuator model: simulation and experimental validation

2002· article· en· W2148652377 on OpenAlexafffund
G. Bilodeau, Evangelos Papadopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsControl theory (sociology)Nonlinear systemBody orificeHydraulic machineryControl engineeringComputer scienceController (irrigation)Manipulator (device)Joint (building)Robust controlParallel manipulatorSimulationEngineeringControl systemRobotMechanical engineeringControl (management)Structural engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, modelling and identification of a hydraulic servoactuator system is presented. The importance of such a model is evident in further understanding the system and in order to develop a robust force controller. The model accounts for line losses, nonlinear orifice areas, hysteresis, friction, leakage, and load dynamics. System parameters are identified based on a high-performance hydraulic manipulator joint, and in particular, the elbow of the SARCOS slave manipulator. Specialized hardware was designed and constructed in order to help identify parameters, to allow line pressure measurements, and to validate the model. The model is verified by comparing simulation and experimental results in two modes: static and dynamic. The results prove to be very good. The developed model sheds light onto the subsystems in a hydraulic manipulator joint and will prove useful in the development of a robust force control algorithm.

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)0.001
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.047
GPT teacher head0.251
Teacher spread0.204 · 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

Citations8
Published2002
Admission routes2
Has abstractyes

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