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Record W1979884956 · doi:10.1109/chicc.2006.4347413

Wave Variable Sliding Mode Control Design for Bilateral Tele-Operation Systems using Haptic Interfaces

2006· article· en· W1979884956 on OpenAlexaff
Dong Lingfang, Khorasani Khashayar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsHaptic technologySliding mode controlComputer scienceControl theory (sociology)Tracking (education)Controller (irrigation)Interface (matter)Mode (computer interface)Position (finance)Transformation (genetics)Control systemControl engineeringPassivityControl (management)SimulationEngineeringArtificial intelligenceNonlinear system

Abstract

fetched live from OpenAlex

Wave transformation is an attractive method for tele-operation systems subject to significant time delays. The proposed method can maintain passivity of the communication channel regardless of the amount of delay. However, this method can potentially introduce position-tracking errors. The position tracking performance can become seriously unsatisfactory when the delay is time varying. Sliding mode control is an effective robust technique that is particularly useful when one considers tracking control problems with presence of uncertainties and disturbances. The control objective is to force the system dynamics to approach a sliding surface and to remain on it for all future time in order to eliminate or minimize tracking errors. In this paper, a randomly time varying delay is considered in the tele-operation system. A sliding mode controller is designed to ensure a precise position tracking control of the slave side to the master's command in a bilateral tele-operation system operating in a virtual environment using a haptic interface.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.619

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)0.0000.000

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 head0.033
GPT teacher head0.226
Teacher spread0.193 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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