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Record W2012202749 · doi:10.1155/2012/792057

A New Method for Bilateral Teleoperation Passivity under Varying Time Delays

2012· article· en· W2012202749 on OpenAlexaff
Farzad Hashemzadeh, Iraj Hassanzadeh, Mahdi Tavakoli, Ghasem Alizadeh

Bibliographic record

VenueMathematical Problems in Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTeleoperationPassivityControl theory (sociology)Channel (broadcasting)EstimatorFeed forwardConstant (computer programming)Block (permutation group theory)Computer scienceHaptic technologySimulationEngineeringControl engineeringControl (management)MathematicsTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

A new framework is proposed to mitigate the adverse effect of time‐varying time delays on the passivity of a teleoperation system. To this end, the communication channel with time‐varying delays is modeled as a constant‐delay channel along with additive output disturbances. Then, disturbance estimator blocks are added in each of the feedforward and feedback paths to estimate these disturbances and to compensate for them. In the disturbance estimator block, there is a need for a virtual time‐varying delay block such that the overall communication channel can be seen as one with a constant delay. We also propose a method for determining this virtual delay. Two PHANToM haptic devices connected through a communication channel with time‐varying delays are considered for a case study. Simulation and experimental results confirm the efficiency of the proposed method in terms of passivating the teleoperation system in the presence of time‐varying delays.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.001
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 head0.018
GPT teacher head0.253
Teacher spread0.235 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations6
Published2012
Admission routes1
Has abstractyes

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