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Record W4210943797 · doi:10.1139/tcsme-2020-0214

Research into the kinematic singularity problem of an all-attitude simulator and its control

2022· article· en· W4210943797 on OpenAlexvenueno aff
Peng Luo, Rong‐Hua Hu, Junlin Bai, Yang Shu

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsnot available
Fundersnot available
KeywordsSingularityKinematicsAttitude controlFlight simulatorControl theory (sociology)Gravitational singularityFidelityPosition (finance)Degrees of freedom (physics and chemistry)SimulationComputer scienceControl (management)EngineeringMathematicsClassical mechanicsPhysicsAerospace engineeringMathematical analysisArtificial intelligence

Abstract

fetched live from OpenAlex

All-attitude flight simulators can simulate flight attitude through three rotational degrees of freedom. In this paper, the inverse kinematics and singularity of the all-attitude simulator are analyzed. Because a singularity will reduce the fidelity of the flight attitude simulation, we investigated ways to resolve this problem. An approximate method for quickly quantifying singularities is proposed. Static damping and dynamic damping are designed to optimize the singularity of the all-attitude simulator and improve the fidelity of flight attitude simulation. Attitude data of typical flight movements, whose inverse kinematics has an obvious singular position, are used to test the two methods. The results of the simulation show that the two methods are feasible and effective, and that dynamic damping has better control over singular excesses than static damping.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.786

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 head0.256
Teacher spread0.239 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

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