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Record W4388264766 · doi:10.21203/rs.3.rs-3460128/v1

On Investigating Dynamic Coupling in Floating Platform and Overhead Crane Interactions: Modeling and Control

2023· preprint· en· W4388264766 on OpenAlexaff
Mohammad K. Al-Solihat, Mohammad Al Saaideh, Yazan M. Al-Rawashdeh, Mohammad Al Janaideh

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of GuelphMemorial University of Newfoundland
Fundersnot available
KeywordsOverhead craneControl theory (sociology)Payload (computing)BacksteppingSwingDynamic positioningControl engineeringController (irrigation)Observer (physics)Computer scienceCoupling (piping)Motion controlEngineeringOverhead (engineering)Dynamic simulationSimulationControl (management)Adaptive controlMarine engineeringRobotArtificial intelligence

Abstract

fetched live from OpenAlex

<title>Abstract</title> This study introduces a comprehensive model addressing the dynamic interaction between a crane and platform in the presence of realistic surge-roll-heave movements induced by ocean waves. While the motion of the payload is ascertainable, the surge-roll-heave motions of the floating platform are considered unknown. Consequently, we proposed a control design strategy integrating a backstepping controller and a high-gain observer. The primary objectives of this design are to accurately track the desired (linear) trajectories of the cart position and mitigate its swing (angular) motion. Moreover, the proposed controller is designed to handle challenges such as unknown dynamic friction, dynamic coupling, and external disturbances, all while ensuring dynamic. The extended high-gain observer is crucial in estimating dynamic states and external disturbances. Through simulation, we validate the effectiveness of our model-based control approach, demonstrating its robust performance in the face of unknown nonlinearities and disturbances caused by wave motion.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.063
GPT teacher head0.350
Teacher spread0.287 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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