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Record W4237723600 · doi:10.1504/ijhvs.2019.101463

Rollover prevention for a heavy vehicle using optimised slide mode steering control

2019· article· en· W4237723600 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.

Bibliographic record

VenueInternational Journal of Heavy Vehicle Systems · 2019
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRollover (web design)Sprung massAutomotive engineeringEngineeringVehicle dynamicsMode (computer interface)Automobile handlingControl theory (sociology)Displacement (psychology)Electronic stability controlTrack (disk drive)Stability (learning theory)ToeControl (management)Computer scienceControl engineeringDamper

Abstract

fetched live from OpenAlex

For a heavy vehicle, active steering can effectively prevent rollover, however, it also changes the operation of the vehicle away from the driver's intentions. To improve rollover stability and reduce tracking error, an optimised slide mode steering control strategy is proposed. Four degrees of freedom linear model is used, which includes lateral, yaw, and roll motions of the sprung mass and unsprung mass. This model is also validated to describe heavy vehicle rollover dynamics. From the model, slide mode control based roll angle and lateral displacement strategies are developed, respectively, and the optimised slide mode steering control strategy is presented to track the set routes and prevent vehicle rollover. Furthermore, some typical numerical cases are simulated to demonstrate the effectiveness of the control strategies. The results show that the optimised strategy can effectively prevent vehicle rollover and significantly improve the performance of tracking the driver's intention for a heavy vehicle.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.257
Teacher spread0.247 · 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