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Record W2885844716 · doi:10.23919/acc.2018.8431035

Novel Controller Design Based on Black Box Systems Approach

2018· article· en· W2885844716 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
TopicAdvanced Control Systems Design
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsTransfer functionBlack boxPadé approximantController (irrigation)Computer scienceControl theory (sociology)Function (biology)Approximation errorFunction approximationApproximation theorySimilarity (geometry)AlgorithmApplied mathematicsMathematicsControl (management)EngineeringArtificial intelligenceArtificial neural network

Abstract

fetched live from OpenAlex

This paper presents a new method to approximate high order systems and their controllers design. This method is an approximation based on systems as black boxes. Therefore, transfer functions are not needed, and the design will be based only on the output response of these systems. The approach is based on the concept of two similar systems but not identical. This similarity is made on significant weighted elements that characterize the black box system. This technique is based on new parameters called weighted elements to approximate the transfer function of the black box system. Based on this reduced transfer function, a PI controller will be developed and applied to the real system. The new approach was compared with the integral square error ISE and the Pade approximation. Theoretical analysis and simulation results are presented.

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 score0.965
Threshold uncertainty score0.953

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)0.0000.001

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 head0.026
GPT teacher head0.210
Teacher spread0.185 · 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

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Citations0
Published2018
Admission routes1
Has abstractyes

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