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Record W2586489665 · doi:10.1109/smc.2016.7844897

Torque reflecting coordination control for bilateral shared autonomous system over open communication networks

2016· article· en· W2586489665 on OpenAlexaff
Shafiqul Islam, Jorge Dias, Lakmal Seneviratne

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsControl theory (sociology)TorqueComputer scienceControl engineeringController (irrigation)Position (finance)Reflection (computer programming)Lyapunov functionMaster/slaveControl systemTransparency (behavior)Control (management)EngineeringNonlinear systemArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, torque reflection based coordination control algorithm is designed for network-based bilateral shared autonomous system over open communication networks. The control algorithm for master and slave manipulator is designed by combining delayed position and velocity signal with the delayed reflected torques from the interaction between human and master and between slave and environment. Robust and adaptive control technique is used to deal with uncertainty associated with the gravity, unmodeled dynamic and other external input disturbance. The convergence of the closed loop system is shown by using Lyapunov method. In contrast with existing force reflection based design, the proposed controller can deal with uncertainty associated with the gravity, unmodeled dynamic and external input disturbance. Compared with other methods, the proposed design uses reflected torques from the interaction between master and human and between slave and environment so as to improve the transparency of the bilateral shared autonomous system. Finally, evaluation results are presented to demonstrate the validity of the proposed design for real-time applications.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.026
GPT teacher head0.277
Teacher spread0.252 · 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 designBench or experimental
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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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