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Record W2043213594 · doi:10.1109/cdc.2012.6425982

Position control for a class of vehicles in SE(3)

2012· article· en· W2043213594 on OpenAlexaff
Ashton Roza, Manfredi Maggiore

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBacksteppingControl theory (sociology)Position (finance)Controller (irrigation)Attitude controlModular designHeading (navigation)Computer scienceTorqueControl engineeringClass (philosophy)Control (management)EngineeringArtificial intelligenceAdaptive controlPhysics

Abstract

fetched live from OpenAlex

A hierarchical design framework is presented to control the position of a class of vehicles in SE(3) that are propelled by a thrust vector along a single body axis and incorporate some mechanism to induce torques about all body axes. A position control outer loop provides reference signals for an attitude control inner loop. The main result of this paper is a set of conditions under which a position controller designed for a point-mass system and an attitude controller can be combined to form a position controller that almost globally asymptotically stabilizes the vehicle to a desired position with desired heading. As opposed to the classical backstepping framework, the proposed approach is modular, in that position control and attitude control designs are completely separate. Thus, for instance, with the proposed technique one can employ any attitude controller from the vast literature on attitude stabilization, provided it enjoys a basic almost global stability property.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Citations10
Published2012
Admission routes1
Has abstractyes

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