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Record W1876955589 · doi:10.1109/cdc.1994.411458

Position tracking and force control of robotic manipulators with a descriptor system model. I. Linearization and solution for position and force control

2002· article· en· W1876955589 on OpenAlexaff
Y.Z. Hu, E.J. Davison

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)Nonlinear systemPosition (finance)Feedback linearizationController (irrigation)LinearizationState spaceRobot end effectorNonlinear controlTrajectoryComputer scienceMathematicsRobotArtificial intelligenceControl (management)Physics

Abstract

fetched live from OpenAlex

This paper deals with the position and force control of a robot manipulator in which the end-effector is required to move along a specified trajectory with a specified contact force on a plane or general surface. Such a system is modeled as a nonlinear descriptor system consisting of a set of nonlinear differential equations and algebraic equations. A method of nonlinear state feedback and state space transformation is used to globally, exactly and totally linearize the nonlinear descriptor system into a linear descriptor system in the working space of the manipulator. Based on the resultant linear descriptor system, a necessary and sufficient condition for position and force control of robot manipulator is then obtained, and a synthesis method to design a controller is given. In the special case when the contact force is required to be zero, the controller becomes considerably simpler.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 score0.915
Threshold uncertainty score0.551

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 head0.017
GPT teacher head0.190
Teacher spread0.173 · 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

Citations2
Published2002
Admission routes1
Has abstractyes

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