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Record W1890865631 · doi:10.1109/ccece.2000.849609

Decomposition-based control of mechanical systems

2002· article· en· W1890865631 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 institutionsToronto Metropolitan University
Fundersnot available
KeywordsDecompositionControl theory (sociology)Parametric statisticsControl engineeringComputer scienceMechanical systemController (irrigation)Robust controlPID controllerRobotControl systemPhysical systemFunctional decompositionNonparametric statisticsControl (management)Artificial intelligenceEngineeringMathematicsMachine learningTemperature control

Abstract

fetched live from OpenAlex

The paper presents a decomposition-based control design framework for mechanical systems with model uncertainties. The fundamental strategy of the proposed decomposition-based system modeling and control approach is to distinguish between uncertain parameters and variables of different physical types, and to design a separate compensator for each of them, while taking into account each specific physical feature. This approach advocates treating each type of model uncertainty with the most suitable and efficient means, including PID, robust, adaptive methods. The overall controller is generated by synergetic integration of these compensators. The procedure of the decomposition-based control design is described in this paper and is illustrated with an example of a two degrees of freedom robot arm, with parametric and nonparametric model uncertainties.

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: none
Teacher disagreement score0.976
Threshold uncertainty score0.336

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)0.0000.000

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 head0.007
GPT teacher head0.202
Teacher spread0.194 · 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

Citations7
Published2002
Admission routes1
Has abstractyes

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