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Record W1975940613 · doi:10.1177/1464419314558741

An integrated vehicle dynamic control strategy for three-wheeled vehicles

2014· article· en· W1975940613 on OpenAlexaff
Avesta Goodarzi, Amir Soltani, ‬Mohammad Hassan Shojaeefard, Amir Khajepour

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part K Journal of Multi-body Dynamics · 2014
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVehicle dynamicsControl theory (sociology)Active steeringControl engineeringYawControl (management)Active safetyControl systemEngineeringMoment (physics)Computer scienceAutomotive engineering

Abstract

fetched live from OpenAlex

Three-wheeled vehicles can address many congestion, parking and pollution issues associated with urban transportation. Previous studies about control of dynamics of three-wheeled vehicles mostly focus on roll dynamics control. This study considers simultaneous control of roll and yaw dynamics. For this purpose, an integrated system has been developed to coordinate active front steering, direct yaw moment control and active tilt systems. The challenges in the control system design arise in finding a compromise between improving vehicle dynamic behaviour and minimizing the actuators’ torque requirements. This study first introduces the vehicle model and then, using optimal control theory, develops an integrated control strategy that works based on the feedback signals from the vehicle’s state variables and the steering input feed-forward. Using a comprehensive nonlinear model, the simulation results illustrate considerable improvements in vehicle handling through the integrated control system in comparison with the pure active front steering, direct yaw moment control or active tilt systems.

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: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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
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

Citations16
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part K Journal of Multi-body DynamicsSame topicVehicle Dynamics and Control SystemsFrench-language works237,207