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Record W2061736103 · doi:10.1115/detc2010-28252

Design of an Integrated AFS/ACD Control System to Enhance 4WD Vehicles Dynamics and Stability

2010· article· en· W2061736103 on OpenAlexaff
Avesta Goodarzi, Samaneh Arabi, Ebrahim Esmailzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsControl theory (sociology)Traction control systemYawVehicle dynamicsTorqueFuzzy control systemElectronic stability controlAutomobile handlingControl systemEngineeringTraction (geology)Fuzzy logicController (irrigation)Control engineeringComputer scienceAutomotive engineeringControl (management)

Abstract

fetched live from OpenAlex

An integrated vehicle dynamic control system for a four-wheel drive vehicle with an active front steering (AFS) and active centre differential (ACD), based on fuzzy logic control, is developed to improve the vehicle stability and its handling performance. The control system has a hierarchical structure consisting of two layers. A fuzzy logic controller is used in the upper layer to keep the yaw rate in its desired value. The yaw rate error and the side slip angle are applied to the upper controlling layer as the inputs, where the desired traction torque transfer ratio and the steering angle correction of the front wheels are the outputs. However, the ideal control effectors could not directly be the control inputs for the centre differential. Therefore, in the lower control loop, one should map the ideal control effectors to the physical control inputs for the centre differential by optimum dynamic traction force distribution. A nonlinear eight degree-of-freedom (DOF) vehicle model with the traction force distribution being utilized by a PI controller is considered. The simulation results illustrate considerable improvements have been achieved for the vehicle stability and handling performance through the integrated AFS/ACD control 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 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)0.001
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: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.194
Teacher spread0.190 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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