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Record W2567753769 · doi:10.1109/cdc.2016.7798467

Switching time domain passivity control for multilateral teleoperation systems under time varying delays

2016· article· en· W2567753769 on OpenAlexaff
Usman Ahmad, Ya‐Jun Pan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTeleoperationPassivityControl theory (sociology)WeightingStability (learning theory)Division (mathematics)Scheme (mathematics)Computer scienceControl systemDomain (mathematical analysis)Time domainControl engineeringTransmission (telecommunications)EngineeringMaster/slaveControl (management)MathematicsTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Time domain passivity control, a well known control scheme widely used for teleoperation systems, normally works under the constraint of zero division. Small force or velocity signals can cause the occurrence of zero division which ultimately leads the system to be unstable. This paper presents a novel switching time domain passivity control scheme for multilateral teleoperation systems which not only ensures the stability of the system but also avoids zero division. In contrast to bilateral teleoperation systems, the multilateral teleoperation system is much more complex as it involves increased number of master and slave hardware, multiple operators and transmission of multiple signals over the communication network. A new framework for the communication channel has been proposed which incorporates the use of weighting coefficients to give masters and slaves authority depending upon the requirements of the operation. As the switching time domain passivity control keeps the system passive all the time, the stability is guaranteed. The proposed control scheme is valid for n masters and n slaves. Simulations with two masters and two slaves are carried out to verify the effectiveness of the proposed scheme.

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 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 score0.946
Threshold uncertainty score0.981

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 communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.203
Teacher spread0.194 · 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.

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

Citations10
Published2016
Admission routes1
Has abstractyes

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