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Record W2097006611 · doi:10.1109/cdc.2004.1430212

Optimization-based robot impedance controller design

2004· article· en· W2097006611 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

Venue2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601) · 2004
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsQueen's University
Fundersnot available
KeywordsImpedance controlElectrical impedanceControl theory (sociology)Impedance parametersController (irrigation)Range (aeronautics)RobotPosition (finance)Computer scienceOptimal controlControl engineeringEngineeringControl (management)MathematicsMathematical optimizationArtificial intelligence

Abstract

fetched live from OpenAlex

Impedance control is a compliance control strategy capable of accommodating both unconstrained and constrained motion. The performance of impedance controllers depend heavily upon environment dynamics and the choice of target impedance. To maintain performance for a wide range of environment dynamics, target impedance needs to be adjusted accordingly. In this paper, a geometrical view on impedance control for target impedance selection is presented. Furthermore, analytical linear quadratic optimal control methodology is employed to tune the manipulator target impedance parameters for an optimum performance, requiring trade-off between position and force regulation. The performance of the proposed controllers is evaluated by numerical simulations.

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), Insufficient payload (model declined to judge)
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 score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.030
GPT teacher head0.243
Teacher spread0.213 · 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