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Record W2960243975 · doi:10.21608/amme.2014.35471

CONTROL STRATEGY DEVELOPMENT FOR INDEPENDENT WHEEL TORQUE DISTRIBUTION FOR MULTI-WHEELED COMBAT VEHICLE

2014· article· en· W2960243975 on OpenAlexaff
H. Ragheb, Moustafa El–Gindy, Hossam A. Kishawy

Bibliographic record

VenueThe International Conference on Applied Mechanics and Mechanical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTorqueControl (management)Automotive engineeringDistribution (mathematics)Computer scienceEngineeringControl theory (sociology)Artificial intelligenceMathematicsPhysics

Abstract

fetched live from OpenAlex

Multi-wheeled combat vehicles behavior depends not only on the available totaldriving torque but also on its distribution among the drive axles/wheels. In turn, thisdistribution is largely regulated by the drivetrain layout and its torque distributiondevices.In this paper, a multi-wheeled (8x4) combat vehicle bicycle model has beendeveloped and used to obtain the desired yaw rate and lateral acceleration tobecome reference for the design of the controllers. PID controllers were designed asupper and lower layers of the controllers. The upper controller develops thecorrective yaw moment, which is the input to the lower controller to manage theindependent torque distribution (torque vectoring) among the driving wheels. Severalsimulation maneuvers have been performed at different vehicle speeds usingMatlab/ Simulink-TruckSim to investigate the proposed torque vectoring controlstrategy. The simulation results with the proposed controller showed a significantimprovement over conventional driveline, especially at severe maneuvers.

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 score0.987
Threshold uncertainty score0.746

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 head0.021
GPT teacher head0.227
Teacher spread0.205 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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