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Record W2566539607 · doi:10.1109/ccsse.2016.7784370

Analysis and comparative study of reference based adaptive control system for serial mechanisms

2016· article· en· W2566539607 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsOntario Tech UniversityYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReference modelAdaptive controlComputer scienceSerial manipulatorPayload (computing)Control engineeringControl (management)Control theory (sociology)RobotArtificial intelligenceEngineeringParallel manipulator

Abstract

fetched live from OpenAlex

Motion control accuracy of robotic manipulators affects the overall robotic system performance. When the end-effector grabs different payloads, the joint motion of robotic manipulators will vary. Traditional controllers have the problem of not being able to compensate the payload variation effect. Model reference adaptive control has been proposed to address the above problem. Advances and issues of the model reference adaptive control for serial robotic mechanisms are presented in this paper. Some existing methods for the model reference adaptive control design issues are discussed and compared to further summarize and improve the methodologies in the model reference adaptive control of serial robotic mechanisms. Very few recent papers can be found in the area of model reference adaptive control of robotic manipulators, this paper will provide a guideline for future research in the direction of model reference adaptive control for robotic mechanisms.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.029
GPT teacher head0.241
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

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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