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Record W3214757493 · doi:10.1115/detc2000/mech-14158

Trajectory Control of Two DOF Closed-Chain Mechanical Systems

2000· article· en· W3214757493 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsControl theory (sociology)ServomotorTrajectoryTorqueFeed forwardComputer scienceOpen-loop controllerControl engineeringFlywheelEngineeringClosed loopControl (management)Artificial intelligencePhysics

Abstract

fetched live from OpenAlex

Abstract In this study, we develop some control algorithms for trajectory tracking of two DOF closed-chain mechanical systems separately driven by two servomotors (closed-chain robot), as well as by one CV motor and the other servomotor (so-called hybrid machine) based on their dynamic model. For the former, the exponential stability and validation of PD computed-torque control algorithm are verified for trajectory tracking of both joints and the end-effector of the system. For the latter, a complex PD computed-torque control algorithm with a closed-loop feedback for the servomotor and an open-loop feedforward for the CV motor is proposed. Simulation studies show that the complex PD computed-torque control algorithm is effective when the velocity fluctuation of the CV motor is reduced to 5% by attaching a flywheel on the CV motor shaft.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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

Quick stats

Citations2
Published2000
Admission routes1
Has abstractyes

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