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Decoupling and Tracking Control for Offshore Crane System Effect by Unknown Roll/Heave Wave Motions Disturbances

2024· article· en· W4402264257 on OpenAlexafffund
Mohammad Al Saaideh, Mohammad K. Al-Solihat, Yazan M. Al-Rawashdeh, Khaled F. Aljanaideh, Mohammad Al Janaideh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of GuelphMemorial University of Newfoundland
FundersOcean Frontier Institute
KeywordsDecoupling (probability)Submarine pipelineControl theory (sociology)Marine engineeringTracking (education)Control systemComputer scienceEngineeringGeologyControl engineeringControl (management)Geotechnical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper introduces an output feedback control method for an offshore crane system (OCS) with unknown wave disturbance caused by roll-heave motions. The method combines a backstepping controller and a high-gain observer to achieve the desired tracking trajectories of the cart position and the rope length. The dynamic model of the offshore crane system is initially formulated to account for unknown dynamic friction, dynamic coupling, and external disturbances while ensuring dynamic decoupling of the cart position and hoisting dynamics. The backstepping controller is proposed to stabilize the system and achieve trajectory tracking, where the extended high gain observer is used to estimate the dynamic states and external disturbances. The effectiveness of the proposed control approach is verified through simulations for both the system with disturbances generated by roll-heave motions. The simulation results demonstrate that the proposed approach is capable of achieving the desired trajectories, even under conditions of unknown nonlinearities and wave motion disturbances.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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
Published2024
Admission routes2
Has abstractyes

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