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Record W4414685148 · doi:10.1016/j.ifacol.2025.09.540

Model-Free Adaptive Control for Three-Dimensional Crane Systems

2025· article· en· W4414685148 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

VenueIFAC-PapersOnLine · 2025
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAdaptabilityControl theory (sociology)Adaptive controlLinearizationControl (management)Control systemAdaptive system

Abstract

fetched live from OpenAlex

This paper proposes the development of and validation of a Model-Free Adaptive Control (MFAC) algorithm for a three-dimensional crane system in a multi input-multi output setting. The three-dimensional cranes are complex systems with various nonlinearities providing a robust environment for testing the adaptability and efficiency of the MFAC algorithms. The paper aims to compare two distinct versions of the algorithm, i.e., the compact form dynamic linearization (CFDL) and the partial form dynamic linearization (PFDL). Both versions are analyzed in terms of their performance in controlling the three-dimensional crane’s movement by controlling the x, y, and z-axes under varying conditions. Experimental validation highlights the strengths and limitations of CFDL and PFDL versions, offering insights into their practical applications and theoretical underpinnings.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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