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Record W2106132706 · doi:10.1109/isic.2002.1157763

Path-tracking for tractor-trailers with hitching of both the on-axle and the off-axle kind

2003· article· en· W2106132706 on OpenAlexaff
R.M. DeSantis, Jean‐Matthieu Bourgeot, J.N. Todeschi, R. Hurteau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsAxleTractorOffset (computer science)Control theory (sociology)Robustness (evolution)LinearizationJacobian matrix and determinantComputer scienceAutomotive engineeringEngineeringMathematicsNonlinear systemStructural engineeringArtificial intelligenceControl (management)Physics

Abstract

fetched live from OpenAlex

Results relevant to path-tracking control for a tractor-2-trailers vehicle with one coupling joint 'on axle' and the other 'off axle', are developed. In the case of off axle hitchings with a negative offset, these results are based on input/output linearization and extend controller design procedures already available for vehicles of which the coupling joints are all 'on axle' or all 'off axle'. In the case of off axle hitchings with a positive offset, exact linearization is no longer applicable and the problem is solved using Jacobian linearization. Convergence and robustness properties of the ensuing controllers are illustrated by means of simulation. Extension of these results to more general tractor-n-trailers vehicles is discussed.

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)0.001
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.182
Teacher spread0.177 · 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

Citations19
Published2003
Admission routes1
Has abstractyes

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