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Record W2562193524 · doi:10.1109/itsc.2016.7795806

Optimal design of formation tracking control for a tractor-trailer robotic system with omni-directional wheels

2016· article· en· W2562193524 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTractorControl theory (sociology)TrailerController (irrigation)Computer scienceControl engineeringTrajectoryHeuristicsOptimal controlSliding mode controlRobust controlVehicle dynamicsStability (learning theory)Control systemEngineeringControl (management)Automotive engineeringArtificial intelligenceMathematicsMathematical optimization

Abstract

fetched live from OpenAlex

This paper addresses the formation tracking control and optimal selection of control design parameters for a tractor-trailer omni-directional type robotic system with on-axle hitching. We design a sliding mode controller incorporating the dynamics of the system to drive the tractor robot to track a desired trajectory and the trailer robot to maintain a desired orientation with respect to the tractor. We also define an optimization problem to find the control design parameters that maximize the control performance and solve it using meta-heuristics optimization techniques. The stability of the developed controller is studied using Lyapunov theorem. We demonstrate the effectiveness of our proposed methodology using simulation results. It is concluded that the proposed control methodology can be adopted in real-time experiments and is applicable in real-world applications for good formation tracking performance under large disturbances.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.012
GPT teacher head0.191
Teacher spread0.180 · 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

Quick stats

Citations7
Published2016
Admission routes1
Has abstractyes

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