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Record W2083177788 · doi:10.1115/1.4026967

Bilateral Control and Stabilization of Asymmetric Teleoperators With Bounded Time-Varying Delays

2014· article· en· W2083177788 on OpenAlexafffund
Trent Hilliard, Ya‐Jun Pan

Bibliographic record

VenueJournal of Dynamic Systems Measurement and Control · 2014
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTeleoperationControl theory (sociology)Linear matrix inequalityNonlinear systemComputer scienceLyapunov stabilityBounded functionRobotMathematicsControl (management)Mathematical optimizationArtificial intelligence

Abstract

fetched live from OpenAlex

A novel control scheme for asymmetric bilateral teleoperation systems is developed based on linear models of the hardware, with considerations in the existence of communication time delays. The master and slave manipulators were modeled as linear single degree of freedom systems. The human user force was modeled based on the band limited availability of human motion, and the environmental force was modeled as a spring and damper combination based on the slave position. The configuration of the whole system represents a relatively general framework for the teleoperation systems. The main contribution of the work can be concluded as follows. First to deal with asymmetric systems in teleoperation, an impedance matching approach was applied to the master side dynamics, while a static error feedback gain was used to stabilize the slave side dynamics. Second, in the existence of bounded random time-varying delays, approaches and techniques based on the Lyapunov method proposed for network controlled systems are now proposed for bilateral teleoperation systems. Specifically, a Lyapunov functional is proposed with consideration for the upper and lower bound of random delays. Linear matrix inequality (LMI) techniques are used with rigorous stability proof to design the slave side controller control gains. Furthermore, the cone complementarity algorithm is used to deal with nonlinear terms within the LMI under the new formulation. Finally, the applications of the proposed algorithm to haptic devices are described thoroughly, and experimental results with comparisons to simulation results are demonstrated to show the effectiveness of the proposed approach.

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)0.001
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: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.166
Teacher spread0.161 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

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