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Record W2938981613 · doi:10.4271/2019-01-1516

A Control Strategy to Reduce Torque Oscillation of the Electric Power Steering System

2019· article· en· W2938981613 on OpenAlexaff
Duo Fu, Subhash Rakheja, Wenjun Yan, Wen‐Bin Shangguan

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2019
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsTorqueOscillation (cell signaling)Control theory (sociology)Power steeringControl (management)Torque steeringPower (physics)Control systemAutomotive engineeringComputer scienceEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">This paper proposes a new evaluation method of analyzing stability and design of a controller for an electric power steering (EPS) system. The main purpose of the EPS system’s control design is to ensure a comfortable driving experience of drivers, which mainly depends on the assist torque map. However, the high level of assist gain and its nonlinearity may cause oscillation, divergence and instability to the steering systems. Therefore, an EPS system needs to have an extra stability controller to eliminate the side effect of assist gain on system stability and attenuate the unpleasant vibration. In this paper, an accurate theoretical model is built and the method for evaluating system quality are suggested. The bench tests and vehicle experiments are carried out to verify the theoretical analysis. The evaluation method proposed in this paper can not only guide the design of controller parameters, but also evaluate the control effect while the performance of several controllers are all excellent.</div></div>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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 designObservational
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

Citations2
Published2019
Admission routes1
Has abstractyes

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