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Record W2972987384 · doi:10.1109/icarm.2019.8833817

Adaptive Control of a Class of Nonlinear Systems with Unknown Prandtl-Ishlinskii Hysteresis

2019· article· en· W2972987384 on OpenAlexaff
Wenjun Ye, Farhad Aghili, Jundong Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsCanadian Space AgencyConcordia University
Fundersnot available
KeywordsControl theory (sociology)HysteresisActuatorNonlinear systemController (irrigation)Adaptive controlParameterized complexityStability (learning theory)Prandtl numberInverseComputer scienceControl engineeringEngineeringControl (management)MathematicsPhysicsAlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, taking the advantage of the properties of Prandtl-Ishlinskii (PI) hysteresis model, an adaptive controller is developed for actuators dominated by unknown hysteresis nonlinearities, which are used in the dynamic systems with the uncertain parameters, where the cascaded plant can be expressed as parameterized form. By employing the construction of the inverse PI model and the adaptive control techniques, the new controller can guarantee the stability and good tracking performance with unknown hysteresis actuator, uncertain system dynamics, and the immeasurable actuator outputs. The simulation results validate the proposed controller and illustrate the performance.

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 score0.439
Threshold uncertainty score0.436

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 head0.003
GPT teacher head0.159
Teacher spread0.155 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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