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Vibration Control of Quarter Car Integrated Seat Suspension with Driver Modelusing Type -1 and Type -2 Fuzzy Logic Controllers

2016· article· en· W2469874861 on OpenAlexaboutno aff
Rajendiran, P Lakshmi, Bijender Kumar

Bibliographic record

VenueAsian Journal of Research in Social Sciences and Humanities · 2016
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsVibrationRide qualitySuspension (topology)Control theory (sociology)PID controllerFuzzy logicController (irrigation)EngineeringCar modelAutomotive engineeringActive suspensionQuarter (Canadian coin)Type (biology)Control engineeringControl (management)Computer scienceMathematicsArtificial intelligenceTemperature controlActuatorElectrical engineering

Abstract

fetched live from OpenAlex

While travelling in a car the vibration is one of the disturbance to the system and the passengers. When the vibration of a car is minimum then the travel comfort of the passenger is more and hence the ride quality. To achieve the same various control strategies are used by the researchers for a 2 Degree Of Freedom (DOF) quarter car model. In this paper, ride quality is improved by designing the Type 1 and Type 2 Fuzzy Logic Controller (FLC) for an 8 DOF quarter car with integrated seat suspension and driver model. The performance of the controller is analyzed with the system is subjected to four types of road disturbance. The responses are compared with each other along with the passive system. The results show that Type 2 FLC controls the vibration than the PID, Type 1 FLC and passive system.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.052
GPT teacher head0.297
Teacher spread0.245 · 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
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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueAsian Journal of Research in Social Sciences and HumanitiesSame topicVehicle Dynamics and Control SystemsFrench-language works237,207