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Record W2316187989 · doi:10.1109/icma.2014.6885706

Vehicle yaw dynamics control using Pulsed Active Rear Steering

2014· article· en· W2316187989 on OpenAlexaff
Yubiao Zhang, Amir Khajepour, Baozhen Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsUniversity of Waterloo
FundersState Key Laboratory of Advanced Design and Manufacturing for Vehicle BodyNational Natural Science Foundation of China
KeywordsYawActive steeringVehicle dynamicsMATLABControl theory (sociology)Dynamics (music)Automotive engineeringComputer scienceAutomobile handlingControl (management)EngineeringPhysicsAcousticsArtificial intelligence

Abstract

fetched live from OpenAlex

Active steering control is an efficient approach used to improve the vehicle's dynamics. This paper introduces a novel technique called Pulsed Active Rear Steering (PARS) for vehicle yaw control. With the concept of pulsed active steering, the optimum frequency and amplitude of a pulse and the effect of individual actuation of each rear wheel on the vehicle yaw dynamics and control is investigated. Using the co-simulation of Adams/Car and Matlab/Simulink, results show that the proposed active steering system has a good yaw dynamics improvement on the vehicle.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.732

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 head0.004
GPT teacher head0.172
Teacher spread0.169 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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