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Record W2033442556 · doi:10.1109/robio.2014.7090519

Dynamics modeling and performance comparisons of two different rotor flying manipulators: Main-tail-rotor vs eight-rotor

2014· article· en· W2033442556 on OpenAlexaff
Bin Yang, Yuqing He, Jianda Han, Guangjun Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRotor (electric)Helicopter rotorCoupling (piping)Flight dynamicsControl theory (sociology)Controller (irrigation)Computer scienceControl engineeringSystem dynamicsEngineeringAerospace engineeringControl (management)AerodynamicsMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Rotor flying manipulator (RFM) is a system composed of a rotorcraft and a manipulator. RFM possesses great potential applications and thus has got much concern of the researchers all over the world. However, controller design of the RFM is challenging because of the dynamical coupling between the rotorcraft and the manipulator perplexes the whole system model structure, so the flight performance of the system is more sensitive to some uncertainty factors. Generally, the rotorcraft system of the RFM can be a main-tail-rotor helicopter or multi-rotor aircrafts, which are of completely different flight characteristics. Thus, it is valuable to conduct quantitative coupling analysis of the RFM with different flight platforms. This paper is aimed at this problem. We first construct the dynamics model of the RFM system with respect to different flight platforms, respectively. Then, coupling force and moment are computed quantitatively, and the comparative analysis on the influence of the motion state on them is conducted. Finally, some conclusions are given to show different performances of different kinds of RFM systems.

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 categoriesMeta-epidemiology (narrow)
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 score0.375
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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.

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
Published2014
Admission routes1
Has abstractyes

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