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Record W2057916931 · doi:10.1115/imece2007-41650

Investigation of a High Precision Hydrostatic Actuation System: How Nonlinearities Affect Its Performance

2007· article· en· W2057916931 on OpenAlexafffund
Wei Li, Richard Burton, Saeid Habibi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsMcMaster UniversityUniversity of Saskatchewan
FundersCanadian Water Network
KeywordsNonlinear systemControl theory (sociology)ActuatorHydrostatic equilibriumBond graphObservableComputer scienceAmplitudeMathematicsApplied mathematicsPhysicsArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

A prototype Electro-Hydraulic Actuator (EHA) system has demonstrated a positional accuracy in the order of 100 nanometer. Linearized models of the EHA have been formulated and have shown reasonable correlation to the performance of the physical EHA. However, these models predict zero steady state error (an impossible situation given the physical limitations of seals, friction etc.). Further, the prototype EHA indicates that the cut-off frequency decreases as the amplitude of the input signal decreases. This is not predicted by the linear models. In this paper the Bond-graph large scale modeling technique was used as the basis to formulate the describing equations of the EHA. The model was made increasingly more complex by introducing observable nonlinearities into the model. It was found that the introduction of nonlinear friction did show results whose trends were consistent with those observed experimentally. Assumed nonlinearities in the bulk modulus could not be substantiated. In addition, some of the observed experimental trends could not be predicted (such as order change) and pose additional challenges to be solved before a complete understanding of the true physics of the EHA can be realized.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.207
Teacher spread0.190 · 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

Citations1
Published2007
Admission routes2
Has abstractyes

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