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Hysteresis Modeling, Identification and Fuzzy PID Control of SMA Wire Actuators Using Generalized Prandtl-Ishlinskii Model with Experimental Validation

2019· article· en· W2969400017 on OpenAlexaff
Hamid Basaeri, Mohamadreza Zakerzadeh, Aghil Yousefi‐Koma, Nafise Faridi Rad, Mohammad Mahdavian

Bibliographic record

VenueApplied and Computational Mechanics · 2019
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsControl theory (sociology)PID controllerSMA*ActuatorShape-memory alloyController (irrigation)HysteresisFuzzy logicComputer scienceAirfoilControl engineeringEngineeringPhysicsStructural engineeringControl (management)Temperature controlArtificial intelligenceAlgorithm

Abstract

fetched live from OpenAlex

In this paper, hysteretic behavior modeling, system identification and control of a mechanism that is actuated by shape memory alloy (SMA) wires are presented. The mechanism consists of two airfoil plates and the rotation angle between these plates can be changed by SMA wire actuators. This mechanism is used to identify the unknown parameters of a hysteresis model. Prandtl–Ishlinskii method is employed to model the hysteresis behavior of SMA actuators, and then, a self-tuning fuzzy-PID controller is designed based on the obtained model and implemented experimentally on the mechanism. The process of designing the controller has been implemented based on the model which results in compensating time and price. Self-tuning fuzzy-PID controller is applied to the closed control loop in order to control the position of the morphing wing. The performance of the controller has been investigated under different input signals including square and sinusoidal waves, and the results show the proper effectiveness of the method.

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.

How this classification was reachedexpand

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.506
Threshold uncertainty score0.583

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 head0.008
GPT teacher head0.199
Teacher spread0.190 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2019
Admission routes1
Has abstractyes

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