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Record W3216438754

Disturbance Observer-assisted Trajectory Tracking Control for Surgical Robot Manipulator

2019· article· fr· W3216438754 on OpenAlexvenueno aff
Varnita Verma, Piyush Chauhan, Mukul Gupta

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2019
Typearticle
Languagefr
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsControl theory (sociology)TrajectoryTracking (education)Computer scienceRobotObserver (physics)Robot manipulatorDisturbance (geology)Control engineeringSurgical robotManipulator (device)Robot controlControl (management)Artificial intelligenceEngineeringMobile robotPsychologyGeologyPhysics
DOInot available

Abstract

fetched live from OpenAlex

This paper attempts to control the motion of a surgical robot to perform surgeries in an accurate and precise manner.In the target robot, two revolute joints, one prismatic joint and one revolute joint are connected in series, i.e. the 2PRP configuration.The external disturbances on the 2PRP robot were estimated, and the dynamic modelling of the robot was performed using the Newton-Euler formulation.Then, a control system was designed to control the robot motion by the torque computed based on the data collected by the disturbance observer, which compensates for the disturbance-induced error.The surgical robot and control system were simulated in MATLAB/Simulink.The results show that the designed control system can effectively compensate for the position error and control the robot motion in surgeries.

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)0.000
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.030
GPT teacher head0.258
Teacher spread0.228 · 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

Citations0
Published2019
Admission routes1
Has abstractno

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