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Record W1564255144 · doi:10.1080/21693277.2014.892443

Prescribed adaptive control of unknown hysteresis in smart material actuated systems

2014· article· en· W1564255144 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

VenueProduction & Manufacturing Research · 2014
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)HysteresisNonlinear systemStability (learning theory)Transient (computer programming)Computer scienceFuse (electrical)Adaptive controlControl engineeringControl (management)EngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Design of controllers that ensure the closed-loop stability of the system actuated by the smart material which shows hysteresis nonlinearity is a challenging issue in the literature. In this paper, we use the Bouc–Wen model to describe the hysteresis nonlinearity and attempt to fuse the Bouc–Wen model with the available adaptive control techniques without constructing the inverse of the Bouc–Wen model. To realize such a fusion, it is necessary to utilize its solution properties. However, the general solution of the Bouc–Wen model is still not available. Therefore, an approximate solution is applied. By means of the approximate solution, a prescribed adaptive control approach is developed to achieve global stability of the closed-loop system and guarantee the transient and steady-state performance of the tracking error. Simulation results demonstrate the effectiveness of the proposed approach.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.022
GPT teacher head0.244
Teacher spread0.222 · 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