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Record W2051667398 · doi:10.1109/cdc.2004.1428850

Adaptive control of harmonic drives

2004· article· en· W2051667398 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601) · 2004
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsHarmonic driveControl theory (sociology)TorqueCompensation (psychology)Adaptive controlController (irrigation)HarmonicControl engineeringRobotEngineeringStability (learning theory)Computer scienceControl (management)PhysicsArtificial intelligenceAcousticsMechanical engineering

Abstract

fetched live from OpenAlex

In this paper, an adaptive control algorithm is designed for controlling the harmonic drives used to drive robot manipulators. Direct torque measurement is available by using the flexspline mounted strain-gauges. The torque error is added to the required velocity. Adaptive friction compensation and flexspline dynamics based control are the two main contributions in the paper. The L/sub 2//L/sub /spl infin// stability and the L/sub 2/-gain induced H/sub /spl infin// stability are guaranteed in both joint torque and joint position control modes. Experiments conducted on two typical types of harmonic drives confirm the feasibility of the controller in both time and frequency domains. By using the virtual decomposition control approach, the independently designed joint adaptive controller for harmonic drives can be efficiently incorporated into the motion/force control systems of robot manipulators.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.233
Teacher spread0.217 · 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