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Record W4400012311 · doi:10.18280/jesa.570310

Design of an Adaptive Integral Sliding Mode Controller for Position Control of Electronic Throttle Valve

2024· article· en· W4400012311 on OpenAlexvenueno aff
Ahmed Khalaf Hamoudi, Luay Thamir Rasheed

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2024
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsnot available
Fundersnot available
KeywordsThrottleControl theory (sociology)Controller (irrigation)Integral sliding modePosition (finance)Sliding mode controlControl engineeringMode (computer interface)Computer scienceEngineeringAutomotive engineeringControl (management)PhysicsNonlinear systemArtificial intelligence

Abstract

fetched live from OpenAlex

This study examines the effectiveness of an adaptive integral sliding mode controller (AISMC) for adjusting an electronic throttle valve's (ETV) angular position.An electronic throttle is a DC motor-driven valve that controls the flow of air into the engine in modern cars.Due to the nonlinear dynamical properties of electronic throttle systems, they are challenging to control using the conventional PID control technique.In the present investigation, an adaptive integral sliding mode control approach is recommended for controlling the ETV.First, the dynamical model of the electronic throttle is utilized to create an integral sliding mode controller (ISMC).The switching control term and the equivalent control term makes up the ISMC.The permissible minimum switching control term's gain is determined using the adaptive sliding mode control.The controller's chattering is reduced as a result.A computer simulation of the suggested control technique is then conducted, and the simulation results validate that the suggested control method has a sufficient degree of control performance.Regarding the transient features of the system, an analysis has been carried out to compare the effectiveness of the AISMC with alternative controllers.The MATLAB package's environment has been employed to implement the simulation.

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 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 score0.959
Threshold uncertainty score0.785

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 head0.016
GPT teacher head0.258
Teacher spread0.242 · 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

Explore more

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