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Record W2131409324 · doi:10.1109/cdc.1993.325550

On robust saturation control of robot manipulators

2002· article· en· W2131409324 on OpenAlexaff
Guangjun Liu, A.A. Goldenberg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)Robust controlRobotComputer scienceController (irrigation)InertiaSylvester's law of inertiaTracking errorMathematicsControl engineeringControl (management)Control systemArtificial intelligenceEngineeringSymmetric matrixPhysics

Abstract

fetched live from OpenAlex

A new robust control method for robot manipulators is proposed in this paper. The new method possesses two characteristics different from those of known robust control laws: 1) it distinguishes between uncertainty in the inertia matrix, Coriolis and centripetal parameters, and the gravity; and 2) the uncertainty bounds needed to design the control law and to prove uniform ultimate boundedness of the tracking error depend only on the physical parameters and known functions of the generalized co-ordinates, hence they are simple to compute. The proposed method is based on designing a robust controller for each source of uncertainty such that the control parameters can be directly related to properties of the robot dynamics and can be adjusted according to the response to external stimuli. As an illustration, the proposed control method is applied to a robot arm, and the effectiveness of the scheme is confined by simulation.>

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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.184
Teacher spread0.160 · 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

Citations19
Published2002
Admission routes1
Has abstractyes

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