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Record W2215355618 · doi:10.1260/0957-4565.46.4.10

Acceleration-Based Vibration Control for Structural Systems with Actuator Faults and Finite-Time State Constraint

2015· article· en· W2215355618 on OpenAlexaff
Falu Weng, Yuanchun Ding, Liming Liang, Ji Ge

Bibliographic record

VenueNoise & Vibration Worldwide · 2015
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of Toronto
FundersJiangxi University of Science and TechnologyNational Science Foundation
KeywordsControl theory (sociology)AccelerationActuatorVibrationVibration controlFault (geology)Constraint (computer-aided design)Mass matrixState-space representationEngineeringComputer scienceControl (management)AlgorithmPhysics

Abstract

fetched live from OpenAlex

The problem of acceleration-based vibration control for structural systems with actuator faults and finite-time state-constraint is discussed in this paper. The objective of designing controllers is to guarantee the closed-loop systems satisfying a finite-time state-constraint condition while having a prescribed level of acceleration attenuation performance. First, by describing the actuator faults into a fault matrix, the actuator-fault-included state-space model, with the acceleration as its controlled output, is obtained. Then, based on a combination of matrices and rank-1 vectors, the obtained model is extended to its uncertain description which contains parameter uncertainties appearing in all the mass, damping and stiffness matrices. Second, based on the finite-time stability analysis, the sufficient conditions for the existence of acceleration-based vibration controllers are obtained. By solving these conditions, the desired controllers, with considerations of actuator faults and parameters uncertainties, are obtained for the closed-loop system to be stable with finite-time state-constraint and acceleration-based H-infinite performance. In the end, simulation results are given to show the effectiveness of the proposed theorems.

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 score0.829
Threshold uncertainty score0.921

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 communication0.0000.001
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 head0.012
GPT teacher head0.211
Teacher spread0.199 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

Explore more

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