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Record W2561047556 · doi:10.1109/tcns.2016.2644261

Robust Sampled-Data Bilateral Teleoperation: Single-Rate and Multirate Stabilization

2016· article· en· W2561047556 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Control of Network Systems · 2016
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsTeleoperationControl theory (sociology)Sampling (signal processing)Stability (learning theory)Computer scienceController (irrigation)Bounded functionNonlinear systemDiscrete time and continuous timeNorm (philosophy)RobotChannel (broadcasting)Control engineeringMathematicsEngineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

This paper investigates robust sampled-data control design strategies for nonlinear bilateral teleoperators exposed to communication constraints, using discrete-time approximate models of the master and slave robots and assuming both single-rate and multirate sampling. The single-rate design guarantees input-to-state stability of the (unknown) exact discrete-time model in a semiglobal practical sense, in spite of norm-bounded channel uncertainties. Then imposing different sampling rates on positions/velocities or torque inputs in each side of the system, a multirate scheme is proposed and shown to maintain similar stability properties under a well-known Nyquist frequency assumption on control torques. Simulation results verify the advantages of our direct sampled-data design. The example also shows that the multirate controller successfully stabilizes the teleoperation system even when the single-rate bilateral setup is ineffective in the presence of different sampling rates.

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.

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 score0.975
Threshold uncertainty score0.684

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 head0.049
GPT teacher head0.212
Teacher spread0.163 · 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