MétaCan
Menu
Back to cohort
Record W2043829279 · doi:10.2514/1.6376

Adaptive Control of a Flexible Robot Using Fuzzy Logic

2005· article· en· W2043829279 on OpenAlexaff
A. Green, Jurek Z. Sąsiadek

Bibliographic record

VenueJournal of Guidance Control and Dynamics · 2005
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsControl theory (sociology)Inverse dynamicsNonlinear systemJacobian matrix and determinantCantileverComputer scienceFuzzy logicRobotKinematicsMathematicsEngineeringPhysicsClassical mechanicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Operational problems with robot manipulators in space relate to several factors, one most importantly being structural flexibility and subsequently significant difficulties with the control systems, especially, for endpoint position control. Elastic vibrations of the links coupled with their large rotations and nonlinear dynamics is the primary cause. This paper presents a control scheme for tracking the endpoint of a two-link flexible robot. The dominant assumed modes of vibration for Euler-Bernoulli cantilever and pinned-pinned beam boundary conditions are coupled with the nonlinear dynamics for rigid links to form an Euler-Lagrange inverse flexible dynamics robot model. A Jacobian transpose control law actuating the robot joints is adapted by a fuzzy logic system (FLS) with link deformation inputs and a single variable output. Results obtained with an FLS adaptive control strategy show significantly diminished vibration amplitudes for both cantilever and pinned-pinned link dynamics and greatly improved control performance compared to the nonadaptive strategy.

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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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

Citations57
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Guidance Control and DynamicsSame topicDynamics and Control of Mechanical SystemsFrench-language works237,207