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Synergetic control of micro positioning stage piezoelectric actuator

2022· article· en· W4309682441 on OpenAlex
Amor Ounissi, Azeddine Kaddouri, Rachid Abdessemed

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

VenueInternational Journal of Applied Power Engineering (IJAPE) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsControl theory (sociology)Particle swarm optimizationActuatorPiezoelectricityStability (learning theory)Nonlinear systemTracking errorControl engineeringComputer scienceSliding mode controlEngineeringControl (management)Artificial intelligenceAlgorithmPhysics

Abstract

fetched live from OpenAlex

The work carried out in this article essentially relates to the application of a synergetic control to the piezoelectric positioning mechanism or piezoelectric actuator (PEA). A LuGre model has been followed, capturing the most physical phenomena, in order to be able to follow the most realistic and representative model possible. From this model, which is then identified by particle swarm optimization (PSO), we apply the synergetic control technique, which is a very efficient control method that allows demonstrating the good functioning of the stability of nonlinear system in closed loop. The simulation results have been compared to those obtained when using sliding mode to confer the best performance in terms of tracking error and minimization of oscillations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.683

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 head0.002
GPT teacher head0.185
Teacher spread0.183 · 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