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Record W1974660927 · doi:10.1115/esda2006-95577

Design of an Optimal Control Strategy for an Active Front Steering System

2006· article· en· W1974660927 on OpenAlexaff
Avesta Goodarzi, Ebrahim Esmailzadeh, Babak Nadarkhani

Bibliographic record

VenueVolume 2: Automotive Systems, Bioengineering and Biomedical Technology, Fluids Engineering, Maintenance Engineering and Non-Destructive Evaluation, and Nanotechnology · 2006
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAccelerationVehicle dynamicsActive steeringControl theory (sociology)MinificationNonlinear systemOptimal controlComputer scienceControl (management)Steering systemFront (military)Range (aeronautics)Control systemControl engineeringEngineeringAutomotive engineeringMathematicsMathematical optimizationMechanical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

The concept of active steering control (ASC) has been considered by several researchers as well as auto manufacturing companies during recent years. This innovative system permits any correction of the driver’s steering angle in order to achieve the desired vehicle dynamic behavior. An optimal control law to evaluate the steering angle’s correction of the front wheels, being part of an active front steering system (AFS), has been developed. For this purpose a specific lateral vehicle dynamics index is defined in which way that the minimization of the performance index lead to improved vehicle dynamics. The optimal values of the control law’s gains are determined analytically. The performance of the proposed control system has been verified using 8-DOF nonlinear vehicle dynamic model. The simulation results illustrate that considerable improvement in vehicle handling is achieved particularly for the cases of the low and mid-range lateral acceleration 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.524
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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.

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
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueVolume 2: Automotive Systems, Bioengineering and Biomedical Technology, Fluids Engineering, Maintenance Engineering and Non-Destructive Evaluation, and NanotechnologySame topicVehicle Dynamics and Control SystemsFrench-language works237,207