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Record W2070661298 · doi:10.1115/1.4025154

Switching Gain-Scheduled Control Design for Flexible Ball-Screw Drives

2013· article· en· W2070661298 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Dynamic Systems Measurement and Control · 2013
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsBall screwControl theory (sociology)ServoBall (mathematics)ServomotorMachiningServo controlServomechanismComputer scienceBandwidth (computing)Controller (irrigation)EngineeringControl engineeringMechanical engineeringControl (management)Artificial intelligenceMathematics

Abstract

fetched live from OpenAlex

This paper proposes an application of the switching gain-scheduled control technique to the flexible ball-screw drive servo system with a wide range of operating conditions. The wide operating range is caused by the change of the table position and the workpiece mass during the machining operation, and leads to plant dynamics variations. To achieve high tracking performance of the table position against the dynamics variations and the cutting force disturbance, a set of gain-scheduled controllers is designed so that each controller damps out the resonance of the ball-screw system and increases the closed-loop bandwidth for a local operating range, and tracking performance is guaranteed under the switching between these controllers. Experimental results with a laboratory-scale ball-screw drive setup demonstrate that the switching gain-scheduled controller outperforms the nonswitching one by up to 52% in tracking accuracy.

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)0.003
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: none
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.015
GPT teacher head0.208
Teacher spread0.193 · 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