MétaCan
Menu
Back to cohort

Handling performance of an 8x8 combat vehicle

2020· article· en· W3110606441 on OpenAlexaff
Moataz Ahmed, Moustafa El–Gindy, Haoxiang Lang

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAxleChassisLinear-quadratic regulatorControl theory (sociology)MATLABController (irrigation)YawComputer scienceEngineeringControl engineeringAutomotive engineeringControl (management)

Abstract

fetched live from OpenAlex

Abstract In this research paper, the handling stability of an 8x8 combat vehicle will be assessed using two different control systems. The first technique utilizes a Torque Vectoring Controller (TVC) to control the vehicle yaw rate to meet the desired value. The second technique utilizes an Active Rear-axles Steering (ARS) to minimize the vehicle sideslip. TVC will be designed as a Single Input Single Output (SISO) control problem using a Sliding Mode Control (SMC) technique, while an Optimal Linear Quadratic Regulator (LQR) will be utilized to develop the ARS controller. The two controllers will be evaluated against a conventional vehicle with fixed rear axles. TruckSim software is used in corporation with Matlab/Simulink to implement and assess the controllers using Double Lane Change (DLC) over high and low coefficient of friction road surfaces at various speeds. The results give an insight into the driving conditions at which each controller is utilized and introduce a novel method to coordinate the integration between both controllers for integrated chassis applications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.583

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 communication0.0000.001
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 head0.010
GPT teacher head0.177
Teacher spread0.167 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueIOP Conference Series Materials Science and EngineeringSame topicVehicle Dynamics and Control SystemsFrench-language works237,207