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Record W2072866329 · doi:10.5539/mas.v5n5p253

Control System Design of Magneto-rheoloical Damper under High-Impact Load

2011· article· en· W2072866329 on OpenAlexvenueno aff
Bucai Liu, Jinjie Chen

Bibliographic record

VenueModern Applied Science · 2011
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsnot available
Fundersnot available
KeywordsDamperControl theory (sociology)Computer sciencePID controllerProcess (computing)Stability (learning theory)Fuzzy control systemElectromagnetic coilControl systemFuzzy logicControl engineeringControl (management)EngineeringTemperature controlElectrical engineering

Abstract

fetched live from OpenAlex

In recent years, the performance requirements of the mechanical and electrical products are increasingly, how to improve those products’ impact resistant ability while in the environment of high impact is become very important. Because of the traditional damper device could not do the real-time adjust about the damping force during the high impact process, in this paper, a magneto-rheological damper is applied to cushion the high-impact load. And the control current, passing into the electromagnetic coil of damper, is changed by designing the delay and fuzzy PID control algorithm in real time, which achieve an effective buffering control of high-impact load. The whole control method is based on TMS320F2812 to achieve. Experimental results show that it can significantly improve the dynamic performance of the damper control system by real-time precise control of the delay and fuzzy PID control algorithm, increase the stability of the damping process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.205
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2011
Admission routes1
Has abstractyes

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