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Admittance-Based Non-Singular Terminal Sliding Mode Control of Multiple Cooperative Manipulators

2023· article· en· W4385488293 on OpenAlexaff
Lucas Wan, Ya‐Jun Pan, Qiguang Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsDalhousie University
FundersNova
KeywordsAdmittanceControl theory (sociology)Terminal sliding modeController (irrigation)Control engineeringRobotObject (grammar)Computer scienceTerminal (telecommunication)TrajectoryMode (computer interface)Control (management)Sliding mode controlEngineeringArtificial intelligenceElectrical impedanceNonlinear system

Abstract

fetched live from OpenAlex

This paper introduces a novel decentralized control framework that integrates admittance control, non-singular terminal sliding mode (NTSM) control, and load distribution to cooperatively manipulate a single object with multiple robotic manipulators. Admittance control grants the system a level of compliance for safe human-robot physical interaction. The admittance controller maintains all communication between the manipulators. The NTSM controller provides accurate tracking of the manipulators in the presence of disturbances and dynamic model uncertainties. A decentralized load distribution method is designed to minimize the internal forces on the object. The proposed framework is applied to numerical simulations of a team of three 3-DOF robotic manipulators to demonstrate its effectiveness in cooperative manipulation tasks.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.005

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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 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

Citations5
Published2023
Admission routes1
Has abstractyes

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