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Record W2112337219 · doi:10.1109/icma.2009.5246067

Integrated vehicle dynamics control through coordinating electronic stability program and active suspension system

2009· article· en· W2112337219 on OpenAlexaff
H. Xia, Wuwei Chen, Huihui Zhou, Jean W. Zu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChassisActive suspensionElectronic stability controlControl systemVehicle dynamicsStability (learning theory)Hierarchical control systemComputer scienceController (irrigation)Control (management)Suspension (topology)Layer (electronics)Control engineeringControl theory (sociology)EngineeringAutomotive engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper investigates integrated vehicle dynamics control through coordinating active suspension system (ASS) and electronic stability program (ESP) in order to improve the overall vehicle performance including handling, stability, and comfort. A two-layer hierarchical control architecture is proposed to integrated control of the two chassis control systems. The upper layer controller is designed to coordinate the interactions between the ASS and the ESP. While in the lower layer, the two controllers including the ASS and the ESP, are developed independently to achieve their local control objectives. Simulation investigation is performed to demonstrate the effectiveness of the proposed hierarchical control system. Results show that the proposed hierarchical control system is able to improve the multiple performance indices of the vehicle including both the ride comfort and the lateral stability, compared to the non-integrated 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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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