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Record W2528390804 · doi:10.1109/wac.2016.7583021

An integrated type-2 fuzzy sliding mode control for underactuated surface vessels

2016· article· en· W2528390804 on OpenAlexaff
Yu Tian, Simon X. Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsUnderactuationControl theory (sociology)Sliding mode controlController (irrigation)TrajectoryFuzzy logicKinematicsFuzzy control systemComputer scienceTracking (education)Control engineeringProcess (computing)EngineeringControl (management)Artificial intelligenceNonlinear system

Abstract

fetched live from OpenAlex

A new intelligent method is developed for tracking control of underactuated surface vessels (USV), which integrates interval type-2 fuzzy logic control (IT2FLC) with a sliding-mode control (SMC). Normally, USV trajectory tracking suffers from the poor performance due to uncertain model parameters and the dynamic disturbances from wind, wave and airflow. In the proposed control method, the IT2FLC is used to analyze the USV kinematic model for selecting the optimal velocity, as the IT2FLC is an advanced technique to minimize the high uncertainties of the control system. The integrated IT2FLC-SMC controller is developed with the USV dynamic model to process the applied control law for better tracking performance. The simulation results demonstrate that the proposed approach is able to handle the system uncertainties better in comparison to the Type-1 fuzzy sliding-mode control approach.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.266
Teacher spread0.245 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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