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Record W1920694679 · doi:10.1139/juvs-2013-0008

Adaptive controller for a biomimetic underwater vehicle

2013· article· en· W1920694679 on OpenAlexaffvenueabout
Nicolas Plamondon, Meyer Nahon

Bibliographic record

VenueJournal of Unmanned Vehicle Systems · 2013
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsController (irrigation)TrajectoryThrustTracking (education)UnderwaterControl theory (sociology)Track (disk drive)PropulsionComputer scienceAdaptive controlTracking errorVehicle dynamicsSimulationControl engineeringEngineeringControl (management)Aerospace engineeringGeologyArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Aqua is an underwater biomimetic vehicle designed and built at McGill University that uses six oscillating paddles to produce control and propulsion forces. These oscillating paddles provide a time-periodic thrust. Using an existing dynamics model of the vehicle and a numerical simulation, an adaptive controller was developed to provide trajectory tracking capabilities to the vehicle. The performance of the controller was first assessed on a dynamics simulation using different trajectories in roll and pitch. The same controller was then tested experimentally in the Caribbean Sea. We found that the adaptive controller was able to track the roll angle with good accuracy, while the tracking error in the pitch motion was more significant.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.217
Teacher spread0.197 · 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

Citations4
Published2013
Admission routes3
Has abstractyes

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