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Record W4233297469 · doi:10.1155/2009/343048

Design of an Active Anti-Roll Bar for Off-Road Vehicles

2009· article· en· W4233297469 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueShock and Vibration · 2009
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsUniversité LavalUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaCentre québécois de recherche et de développement de l’aluminium
KeywordsControl theory (sociology)Active suspensionEngineeringBar (unit)Controller (irrigation)Automobile handlingSuspension (topology)AccelerationComputer scienceAutomotive engineeringControl (management)MathematicsActuatorArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a comparison of performance between a passive and an active anti-roll bar. Off-road vehicles are subject to large input road motion and appreciable lateral forces, making anti-roll bars desirable. A four DOF linear model is used to represent an independent suspension and to design the controller. For every case the performance is evaluated for severe road input perturbation and lateral acceleration. A method is presented to illustrate the compromise between stability and comfort inherent in passive anti-roll bar selection. This method was used to select a realistic anti-roll bar stiffness. The active anti-roll bar was designed using full state feedback optimal controller. A simplification of the active system is proposed to reduce the number of measurements and eliminate the need for an optimal observer. The results show a superior performance in ride and handling for the active controller in the frequency range of interest. The addition of filters is proposed to maximize controller efficiency and to reduce associated problems.

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.

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: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.249

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 head0.009
GPT teacher head0.214
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