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Record W2774799569 · doi:10.1109/access.2017.2782178

A Time-Delayed Multi-Master-Single-Slave Non-Linear Tele-Robotic System Through State Convergence

2017· article· en· W2774799569 on OpenAlexafffund
Umar Farooq, Jason Gu, M.E. El-Hawary, Muhammad Usman Asad, Ghulam Abbas, Jun Luo

Bibliographic record

VenueIEEE Access · 2017
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceMaster/slaveControl theory (sociology)Convergence (economics)Nonlinear systemRobotic armControl engineeringRobotLyapunov functionScheme (mathematics)State (computer science)Position (finance)Control (management)EngineeringArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

This paper presents the design of a multi-master-single-slave nonlinear tele-robotic system working in the presence of time varying delays. The structure of the proposed tele-robotic system is derived from the extended state convergence architecture and the control objective is defined as the position regulation of the slave manipulator. The desired reference value for the slave manipulator is set by the master systems according to their authority levels. To ensure that the tele-robotic system remains stable in the presence of time varying delays and the control objective is also achieved, Lyapunov-based stability analysis is carried out which results in certain guidelines to be followed for the selection of the control gains. In order to check the validity of the proposed scheme, MATLAB simulations are performed on a two degrees-of-freedom nonlinear tele-robotic system containing three master and single slave manipulators. Simulation results suggest that the proposed scheme is viable and can be deployed to control a class of multilateral nonlinear tele-robotic systems.

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 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score1.000

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.070
GPT teacher head0.297
Teacher spread0.228 · 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.

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

Citations4
Published2017
Admission routes2
Has abstractyes

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