MétaCan
Menu
Back to cohort
Record W2158161185 · doi:10.1115/1.2431813

Adaptive Control of Harmonic Drives

2006· article· en· W2158161185 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

VenueJournal of Dynamic Systems Measurement and Control · 2006
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsControl theory (sociology)Harmonic driveTorqueController (irrigation)Adaptive controlHarmonicMotion controlStability (learning theory)Joint (building)RobotControl engineeringPosition (finance)Computer scienceEngineeringControl (management)Artificial intelligencePhysics

Abstract

fetched live from OpenAlex

Aimed at achieving ultrahigh control performance for high-end applications of harmonic drives, an adaptive control algorithm using additional sensing, namely, the joint and motor positions and the joint torque, and their practically available time derivatives, is proposed. The proposed adaptive controller compensates the large friction associated with harmonic drives, while incorporating the dynamics of flexspline. The L2∕L∞ stability and the L2 gain-induced H∞ stability are guaranteed in both joint torque and joint position control modes. Conditions for achieving asymptotic stability are also given. The proposed joint controller can be efficiently incorporated into any robot motion control system based on either its torque control interface or the virtual decomposition control approach. Experimental results demonstrated in both the time and frequency domains confirm the superior control performance achieved not only in individual joint motion, but also in coordinated motion of an entire robot manipulator.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.010
GPT teacher head0.186
Teacher spread0.176 · 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