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Record W2163166241 · doi:10.1109/acc.2009.5160098

H∞ robust control of active suspensions: A practical point of view

2009· article· en· W2163166241 on OpenAlexaff
Mohammad Saber Fallah, Rama Bhat, Wenfang Xie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsConcordia University
Fundersnot available
KeywordsKinematicsSuspension (topology)Ride qualityPoint (geometry)Control theory (sociology)Active suspensionWork (physics)Stability (learning theory)Computer scienceAutomobile handlingControl (management)Robust controlVehicle dynamicsControl engineeringEngineeringControl systemAutomotive engineeringMathematicsArtificial intelligenceMechanical engineeringPhysicsMachine learning

Abstract

fetched live from OpenAlex

Control of vehicle suspension systems has been the focus of extensive work in the past two decades. Many control strategies have been developed to improve the overall vehicle performance, including both ride quality and stability conditions. However, the concerns regarding the wheel motions affecting significantly on the handling and steering are ignored by researchers in the control formulation. A H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> control methodology is employed to design an active suspension system so that ride quality and wheel motions are improved simultaneously. In addition, a three-dimensional kinematic model of a specific suspension system, namely Macpherson suspension, is developed to study the alteration of those of kinematic suspension parameters which represent the wheel motions. The results show that the proposed robust design provides superior kinematic and dynamic performances compared to those of the passive 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.970
Threshold uncertainty score1.000

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)0.0010.000

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 head0.026
GPT teacher head0.257
Teacher spread0.232 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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