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Record W2325636143 · doi:10.2514/6.2010-3072

Modulation Control of Bi-State Adaptive Impedance Device for Active Vibration Suppression

2010· article· en· W2325636143 on OpenAlexaff
Anant Grewal, Viresh Wickramasinghe, Yong Chen, D. G. Zimcik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsElectrical impedanceModulation (music)VibrationActive vibration controlState (computer science)Control theory (sociology)Vibration controlFrequency modulationComputer scienceControl (management)AcousticsMaterials sciencePhysicsEngineeringElectrical engineeringTelecommunicationsRadio frequency

Abstract

fetched live from OpenAlex

The piezoceramic actuator based Smart Spring is a bi-state or binary impedance device used for active vibration suppression by means of semi-active control. A modulation control scheme for achieving quasi-continuous variation of stiffness using such a bi-state stiffness device is proposed in this paper. A two degree-of-freedom system model that incorporated a variable stiffness member was used as the demonstration platform to evaluate control strategies. Dynamic simulations of the model using Matlab and Simulink were performed to demonstrate the effectiveness of candidate control laws. The bi-state system subjected to a disturbance force exploits the use of a continuously varying stiffness member by means of the feedback linearization technique applied to design a non-linear control law. The ability to control a discrete stiffness device in a quasi-continuous manner offers the opportunity for the application of a larger selection of control approaches than the state-switched type control laws currently employed with such devices. In addition, the performance of the resulting controller compares favorably to that obtained using a state-switched control law.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.770
Threshold uncertainty score0.356

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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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 designBench or experimental
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

Citations1
Published2010
Admission routes1
Has abstractyes

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