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Record W2009989932 · doi:10.1109/acc.2013.6580574

A modified generalized Prandtl-Ishlinskii model and its inverse for hysteresis compensation

2013· article· en· W2009989932 on OpenAlexaff
Sining Liu, Chun‐Yi Su

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsConcordia University
Fundersnot available
KeywordsHysteresisPrandtl numberInverseControl theory (sociology)ActuatorFeed forwardCompensation (psychology)Nonlinear systemApplied mathematicsPhysicsComputer scienceMathematicsMathematical analysisConvectionEngineeringMechanicsControl engineeringArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

Hysteresis nonlinearities are inherently exhibited in smart material based actuators. Many hysteresis models have been proposed in the literature to describe such hysteresis nonlinearities. Therein, the Prandtl-Ishlinskii (PI) model is getting more and more popular due to its unique analytical invertibility for the construction of its feedforward compensator. However, the Prandtl-Ishlinskii (PI) model suffers some limits and can only describe a certain class of hystereses. To extend to a more general class, a generalized Prandtl-Ishlinskii (GPI) model was developed. When the smart actuators are cascaded with plants, they usually generate the undesirable oscillations. In order to mitigate the hysteresis effects, its inverse is commonly constructed to compensate such effects. Though, the analytic inverse of the PI is well documented in the literature, the inverse for the GPI has only been listed. As the further development, the GPI is re-defined and a modified generalized Prandtl-Ishlinskii (MGPI) model is proposed which can still describe similar general class of hysteresis shapes. The benefit is that with the linear envelope function an analytical inverse hysteresis model can be derived for the purpose of compensation. The proposed approach is verified in both simulation and experiment.

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: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.357

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 head0.018
GPT teacher head0.203
Teacher spread0.185 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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