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Record W2991033378 · doi:10.1115/dscc2019-8945

Periodic Tracking Control Using Gain-Scheduled Fourier Series-Based Internal Models

2019· article· en· W2991033378 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 Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsControl theory (sociology)Internal modelFourier seriesController (irrigation)Computer scienceTracking (education)Stability (learning theory)Tracking errorSeries (stratigraphy)Control engineeringControl (management)EngineeringMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This paper presents a gain-scheduled controller composed of a number of positive real controllers that contain internal models of reference command signals. Using the internal model principle as inspiration, and the Passivity Theorem to assure input-output closed-loop stability, the proposed controller is designed to realize tracking while maintaining input-output stability of the closed-loop system. The gain-scheduled nature of the internal models allows for a number of internal models to be simultaneously implemented. In particular, by expressing a periodic reference command as a Fourier series, the first few Fourier modes can be included as internal models in the controllers to be gain-scheduled, which reduces steady-state tracking error. An example involving tracking the outlet temperature of a heat exchanger is presented, where the first nine Fourier modes of the reference signal are used as internal models to reduce tracking error.

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 score0.882
Threshold uncertainty score0.856

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 communication0.0000.001
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 head0.011
GPT teacher head0.210
Teacher spread0.200 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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