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Record W2319358206 · doi:10.1109/epe.2014.6910980

A Hybrid ACO and Nelder-Mead constrained algorithm for controller and anti-windup tuning

2014· article· en· W2319358206 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
TopicExtremum Seeking Control Systems
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsRobustness (evolution)Ant colony optimization algorithmsComputationComputer scienceControl theory (sociology)Nonlinear systemMathematical optimizationController (irrigation)Key (lock)AlgorithmMathematicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Nonlinear controller tuning with anti-windup is not trivial since the parameters within the structure are interdependent. A global optimization-based tuning approach can handle this interdependence. A hybrid Ant Colony Optimization (ACO) and Nelder-Mead (NM) algorithm for controller and anti-windup tuning efficiently solves this problem. This paper proposes a new way to constrain ACO-NM algorithm to ensure positivity of key parameters in order to avoid searches in instability or low robustness regions and to improve computation time to reach an optimal solution.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.750

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 head0.196
Teacher spread0.188 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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