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Record W3042283213 · doi:10.1177/1077546320943794

Determination of optimal positive position feedback parameters by using nonsmooth H∞ synthesis

2020· article· en· W3042283213 on OpenAlexaff
Bin E, Jinjun Shan, Muhammad Atif Khushnood, Xiaogang Wang, Naigang Cui

Bibliographic record

VenueJournal of Vibration and Control · 2020
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsYork University
Fundersnot available
KeywordsControl theory (sociology)Position (finance)Simple (philosophy)Controller (irrigation)Transfer functionOutput feedbackFunction (biology)Optimal controlMathematicsComputer scienceControl (management)EngineeringMathematical optimization

Abstract

fetched live from OpenAlex

This article presents a method for determining optimal parameters of positive position feedback controllers used for suppressing vibration of flexible structures. The method is based on solving the H∞ synthesis problem with additional constraints on the controller structure. The method allows for independent control of damping added to each mode. Moreover, optimal parameters for both simple input simple output and multiple input multiple output control formulations can be obtained. The effectiveness of the method is shown by comparing the results of both multiple input multiple output and simple input simple output positive position feedback controllers designed by the proposed method, with the simple input simple output positive position feedback controller designed by analytically derived optimal parameters. Moreover, results of controllers designed with the fixed-order transfer function control structure are also presented to emphasize the advantages offered by the positive position feedback control structure.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.278

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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
Published2020
Admission routes1
Has abstractyes

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