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Record W2099862897 · doi:10.1109/robot.1997.619344

Observer design for flexible joint manipulators with parameter uncertainties

2002· article· en· W2099862897 on OpenAlexaff
N. Léchevin, Pierre Sicard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsControl theory (sociology)Robustness (evolution)Observer (physics)Computer scienceRobot manipulatorState variableLyapunov functionRoboticsControl engineeringState observerStability (learning theory)Flexibility (engineering)RobotArtificial intelligenceMathematicsEngineeringControl (management)Nonlinear system

Abstract

fetched live from OpenAlex

Robotics manipulators may exhibit flexibility that adversely affects the dynamic and even static performance of controllers designed for rigid robots. Full state measurement is costly, so that part of the state must often be reconstructed. We present the design of two observers: 1) a variable structure observer requiring the measurement of link positions to estimate the full state of a flexible joint manipulator; and 2) a reduced adaptive observer that requires the measurement of link and motor positions. Stability and robustness properties are established and we propose a methodology to design the variable structure observer by solving simultaneously a Lyapunov equation and a matching condition. Simulation results are provided to evaluate their performance.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.116
GPT teacher head0.224
Teacher spread0.108 · 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
GenreMethods

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

Citations7
Published2002
Admission routes1
Has abstractyes

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