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Record W2145945531 · doi:10.1109/tro.2013.2242377

A Framework for Unconditional Stability Analysis of Multimaster/Multislave Teleoperation Systems

2013· article· en· W2145945531 on OpenAlexaff
Behzad Khademian, Keyvan Hashtrudi-Zaad

Bibliographic record

VenueIEEE Transactions on Robotics · 2013
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsTeleoperationStability (learning theory)Control theory (sociology)Computer scienceTeleroboticsDual (grammatical number)Stability conditionsControl engineeringRobotControl (management)EngineeringMathematicsMobile robotArtificial intelligenceDiscrete time and continuous timeMachine learning

Abstract

fetched live from OpenAlex

A novel robust stability analysis framework is presented for unconditional stability analysis of multimaster/multislave teleoperation systems. Unlike the unconditional stability criterion for single-user systems, the newly proposed criteria for unconditional stability of multimaster/multislave teleoperation systems depend on the multiport network parameters and the port terminations. In addition to the analytical solution, the graphical demonstration of the unconditional stability region facilitates the analysis of coupled stability against variations in the dynamics of the environments and operators, even when they behave actively. The proposed robust stability analysis framework is examined on two multilateral shared control architectures that were previously developed for dual-user teleoperation 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 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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.032
GPT teacher head0.255
Teacher spread0.223 · 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

Citations30
Published2013
Admission routes1
Has abstractyes

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