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Car Wheel slip Modelling, Simulation, and Control using Quarter Car Model

2015· article· en· W2414486856 on OpenAlexaboutno aff
Abubaker Abasalam A. Emheisen, Abdussalam Ali Ahmed, Nasr Ismael Alhusein, Abdurahim Alfadel Sakeb, Abdulhamid S. Abdulhamid

Bibliographic record

VenueInternational Journal of Engineering Trends and Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCar modelAutomotive engineeringSlip (aerodynamics)Quarter (Canadian coin)Computer scienceEngineeringAerospace engineeringGeography

Abstract

fetched live from OpenAlex

This paper presents a Simulink model of a car wheel slip control loop as might be used in a rudimentary ABS system. Although idealized equations are used, the overall model demonstrates many of the essential features of a Simulink model. Each of the individual components in the loop are discussed separately: a tire model; a quarter car model; a brake actuator; and a (PI) controller. The model for each component demonstrates a different feature of Simulink: the tire model shows how to implement a simple equation; the quarter car model shows how to implement non-linear continuous time equations; the actuator model illustrates how to handle time delays; while the controller shows how to implement discrete time difference equations. Since the quarter car model and actuator are modelled in continuous time, while the controller is implemented in discrete time, the developed model is also excellent example of how to implement hybrid continuous- discrete systems within Simulink.

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: none
Teacher disagreement score0.012
Threshold uncertainty score0.024

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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