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Record W1851548297 · doi:10.1109/mwscas.1991.252208

Simulation of adaptive controller for flexible joint manipulators during constrained motion task execution

2002· article· en· W1851548297 on OpenAlexaff
Yuexin Han, Ν.Κ. Sinha, M.A. Elbestawi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsMcMaster University
Fundersnot available
KeywordsJerkControl theory (sociology)Computer scienceParametric statisticsAdaptive controlRobustness (evolution)Robot manipulatorRobotAccelerationPassivityController (irrigation)Control engineeringArtificial intelligenceEngineeringMathematicsControl (management)

Abstract

fetched live from OpenAlex

An adaptive control scheme for flexible joint manipulators constrained by contact with the environment is presented. Based on a singular perturbation model of constrained manipulators, an estimate of the range of mu in which stability is guaranteed is given. Simulation results for a two-link flexible joint manipulator demonstrate the effectiveness of the proposed control algorithm. The proposed method has the following attractive features. First, fundamental passivity properties of rigid robot dynamics are used to design the adaptive controller for flexible joint manipulators. Second, the implementation of the full controller requires only joint and link position and velocity information. Robustness to parametric uncertainty is achieved without the need for acceleration and jerk measurements. Finally, this control algorithm is uncomplicated for practical applications.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 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: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.477

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 head0.049
GPT teacher head0.232
Teacher spread0.183 · 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 teacher head, 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

Citations0
Published2002
Admission routes1
Has abstractyes

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