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Record W4393033221 · doi:10.1109/access.2024.3380434

Fuzzy Extended State Observer for Robust Sliding Mode Control of Piezoelectric Actuators

2024· article· en· W4393033221 on OpenAlexfundno aff
Maryam Naghdi, Iman Izadi

Bibliographic record

VenueIEEE Access · 2024
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsnot available
FundersShahrood University of TechnologyIsfahan University of TechnologySharif University of TechnologyUniversity of Alberta
KeywordsControl theory (sociology)State observerActuatorSliding mode controlFuzzy control systemRobust controlComputer scienceObserver (physics)Mode (computer interface)Fuzzy logicPiezoelectricityControl (management)Control systemArtificial intelligenceEngineeringAcousticsPhysicsNonlinear system

Abstract

fetched live from OpenAlex

Piezoelectric actuators are widely used in micro and nano-positioning systems for accurate movement. However, they exhibit some nonlinearities, particularly hysteresis, which makes precise control rather challenging. Many methods are available in the literature to compensate for the hysteresis effect in piezoelectric actuators, but often a model of the actuator is required for this purpose. Identification of such a model is challenging too. In this paper, we propose using a robust observer-based controller for precise motion tracking of piezoelectric actuators without the need for a hysteresis model. The controller consists of a fuzzy extended state observer (FESO) to estimate the hysteresis and other nonlinearities, as well as model uncertainties and external disturbances. Subsequently, a robust sliding-mode controller is designed and added to the framework. Joint stability analysis guarantees the stability and tracking performance of the proposed combined controller. Simulation and experimental results confirm the performance of the proposed controller compared to some other techniques.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.263
Teacher spread0.244 · 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.

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

Citations9
Published2024
Admission routes1
Has abstractyes

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