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Record W2143415934 · doi:10.1109/tcst.2004.839580

Control of hybrid Machines with 2-DOF for trajectory tracking problems

2005· article· en· W2143415934 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

VenueIEEE Transactions on Control Systems Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsControl theory (sociology)ServomotorFlywheelControl engineeringServomechanismController (irrigation)Flexibility (engineering)Hybrid systemComputer scienceServo driveTrajectoryEngineeringControl (management)Automotive engineeringArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

There are two types of drivers in production machine systems: constant velocity (CV) motor and servo-motor. If a system contains two drivers or more, among which some are of the CV motor while the other are the servo-motor, the system has the so-called hybrid driver architecture and is called hybrid machine for short. The hybrid system has the advantage of high payload and application flexibility. In this brief, we propose a control algorithm and show that the controlled hybrid machine is stable. A simulation is performed to verify the proposed controller. The CV motor has the velocity fluctuation due to the change of its workload. The common approach to attenuate the velocity fluctuation is via a flywheel which is attached on the shaft of the CV motor. We show that this can further improve the tracking performance of the hybrid system. A five-bar linkage with two degrees of freedom is used for illustration throughout the brief.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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