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FORMATION CONTROLLERS FOR UNDERACTUATED SURFACE VESSELS AND ZERO DYNAMICS STABILITY

2008· article· en· W2396364797 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueControl and Intelligent Systems · 2008
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsnot available
Fundersnot available
KeywordsUnderactuationZero (linguistics)Control theory (sociology)Stability (learning theory)Dynamics (music)Surface (topology)Computer scienceMathematicsPhysicsControl (management)GeometryArtificial intelligence

Abstract

fetched live from OpenAlex

Nonlinear feedback control laws for controlling multiple robotic vessels in arbitrary formations are proposed. The presented leader-follower formation control approach uses only the inertial information obtained from the immediate neighbours of each vehicle via communication for control calculations. A three-degree-of-freedom (3DOF) surface vessel dynamic model and the method of Lyapunov has been used to derive the nonlinear control laws that stabilize the relative distance and orientation of neighboring vessels. It is shown that the internal dynamics of the 3DOF vessel as an underactuated system is also stable. The performance of these control laws is demonstrated in the presence of sea disturbances by computer simulations using a 6DOF dynamic model of the surface vessel. These controllers can be utilized to control an arbitrary number of robotic vessels moving in very general formations.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.944
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.035
GPT teacher head0.237
Teacher spread0.202 · 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