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Record W2108247831 · doi:10.1002/eej.22456

PFC Design via FRIT Approach for Adaptive Output Feedback Control of Discrete‐Time Systems

2014· article· en· W2108247831 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

VenueElectrical Engineering in Japan · 2014
Typearticle
Languageen
FieldEngineering
TopicControl Systems and Identification
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsControl theory (sociology)Feed forwardFritComputer scienceScheme (mathematics)Discrete time and continuous timeAdaptive controlSet (abstract data type)Control engineeringArtificial neural networkControl (management)EngineeringMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

SUMMARY This paper deals with a design problem of an adaptive output feedback control for discrete‐time systems with a parallel feedforward compensator (PFC), which is designed for making the augmented controlled system “Almost Strictly Positive Real” (ASPR). A PFC design scheme by a fictitious reference iterative tuning (FRIT) approach with only using an input/output experimental data set will be proposed for discrete‐time systems in order to design an adaptive output feedback control system. Furthermore, the effectiveness of the proposed PFC design method will be confirmed through numerical simulations by designing an adaptive control system with adaptive NN (neural network) for an uncertain discrete‐time system. © 2014 Wiley Periodicals, Inc. Electr Eng Jpn, 187(1): 24–32, 2014; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.22456

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)0.000
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 score0.991
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.008
GPT teacher head0.173
Teacher spread0.166 · 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