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Record W2338460402 · doi:10.1080/00207721.2016.1173742

Model-independent position domain sliding mode control for contour tracking of robotic manipulator

2016· article· en· W2338460402 on OpenAlexafffund
W. H. Yue, V. Pano, P. R. Ouyang, Yuqi Hu

Bibliographic record

VenueInternational Journal of Systems Science · 2016
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsControl theory (sociology)Feed forwardSliding mode controlRobustness (evolution)Lyapunov stabilityLyapunov functionNonlinear systemPosition (finance)Variable structure controlEngineeringComputer scienceControl engineeringArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

In this paper, a new position domain feedback type sliding mode control (PDC-SMC) law is proposed for contour tracking control of multi-DOF (degree of freedom) nonlinear robotic manipulators focusing on the improvement of contour tracking performances. One feature of the proposed control law is its model-independent control scheme that can avoid calculation of the feedforward part in a standard SMC. The new control law takes the advantages of the high contour tracking performance of PD type feedback position domain control (PDC) and the robustness of SMC. Stability analysis is performed using the Lyapunov stability theory, and simulation studies are conducted to verify the effectiveness of the developed PDC-SMC control system. In addition, the effects of control parameters of the SMC on system performances are studied.

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 score0.001
Threshold uncertainty score0.002

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 science0.0010.000
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 head0.019
GPT teacher head0.273
Teacher spread0.254 · 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

Citations25
Published2016
Admission routes2
Has abstractyes

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