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Comparative Analysis of Reaching Laws in Sliding Mode Control for Electrohydraulic Active Suspension Systems under Complex Road Trajectories

2024· article· en· W4405846121 on OpenAlexaff
Rachid Fattah, Jean‐Pierre Kenné, Khalid Benjelloun, Ahmed Chebak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsControl theory (sociology)Sliding mode controlActive suspensionSuspension (topology)Computer scienceMode (computer interface)Control engineeringControl systemControl (management)EngineeringPhysicsNonlinear systemMathematicsActuatorArtificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a comparative study on various reaching laws within the sliding mode control approach for electrohydraulic active suspension systems under complex road conditions. Unlike traditional single bump profiles, this study evaluates the performance of constant, exponential, and power reaching laws with scenarios involving a speed bump, a pothole, and a sidewalk. The objective is to enhance vehicle handling and passenger comfort.To address chattering in sliding mode controllers, we explore exponential and power reaching laws. Performance metrics for road holding and passenger comfort are compared with those of a traditional PID control. Simulation results indicate that the exponential reaching law excels in both position and force control, potentially negating the need for hybrid control when used alone. However, hybrid control is necessary for constant rate or power rate reaching laws.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.040
GPT teacher head0.300
Teacher spread0.260 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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