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Record W4312804004 · doi:10.1299/jsmermd.2022.2p1-e03

Vibration Control of Slung Load in Transportation Using Frequency-Domain Shaped Trajectory

2022· article· en· W4312804004 on OpenAlexaff
Tasuku HOSHINO, Daisuke Fujiwara, Hiroyuki KAMBE, Takayuki Kitamura

Bibliographic record

VenueThe Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2022
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsTrajectoryVibrationFrequency domainControl theory (sociology)Control (management)Computer sciencePhysicsAcoustics

Abstract

fetched live from OpenAlex

This paper describes the vibration suppression of a slung load which is carried by unmanned aerial vehicle (UAV), by tracking the vehicle to a vibration suppression trajectory. The trajectory is shaped in the frequency domain not to stimulate the normal mode of the UAV-load system. Since the frequency-domain design can explicitly handle constraints on acceleration and velocity of UAV, the resulting trajectory has time-suboptimality. The UAV-load system in a vertical plane is firstly modeled as DAEs and then the vibration suppression trajectory is designed using the natural frequency of load vibration and the vehicle constraints. Vibration suppression of the slung-load is verified in numerical simulations. The vibration control is finally implemented on ArduPilot, an open-source autopilot system.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.202
Teacher spread0.192 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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