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Record W2145982316 · doi:10.1109/cdc.2001.980986

Control for canceling periodic disturbances with uncertain frequency

2003· article· en· W2145982316 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

VenueProceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228) · 2003
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsWestern University
Fundersnot available
KeywordsControl theory (sociology)Internal modelAutomatic frequency controlController (irrigation)Perturbation (astronomy)Singular perturbationComputer scienceStability (learning theory)LTI system theoryIdeal (ethics)MathematicsControl (management)Linear systemMathematical analysisPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Presents an algorithm to cancel periodic disturbances with uncertain frequency. The disturbances are canceled using a controller with an internal model structure in parallel with a traditional PI controller. It is shown that under ideal circumstances the time varying states of the internal model can be mapped to two time invariant variables, the magnitude or energy of the internal model and the difference between the nominal error frequency and the true error frequency. An additional integral controller then can be used to reduce this error to zero. The stability of the feedback control system including this algorithm is justified by singular perturbation theory. Simulations demonstrate the validity of the analytical results, the ability of this algorithm to identify the frequency of periodic disturbances and the capability of this feedback control system to reject periodic disturbances with uncertainty in frequency.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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