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Record W2018257068 · doi:10.1109/tcst.2006.886440

Flatness-Based Feedback Control of an Automotive Solenoid Valve

2007· article· en· W2018257068 on OpenAlexaff
Soon K. Chung, Charles Robert Koch, Alan F. Lynch

Bibliographic record

VenueIEEE Transactions on Control Systems Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsControl theory (sociology)Flatness (cosmology)CamshaftSolenoid valveActuatorNonlinear systemControl engineeringEngineeringRepetitive controlTestbedCrankshaftNonlinear controlControl systemFeedback linearizationControl valvesTracking errorMotion controlSpline (mechanical)SolenoidComputer scienceMechanical engineeringControl (management)

Abstract

fetched live from OpenAlex

This brief considers the control of solenoid valve actuators used for gas exchange in internal combustion engines. Although solenoid valves offer performance benefits over traditional camshaft-based valve systems, maintaining low impact velocity is a critical performance requirement. Flatness provides a convenient framework for meeting a number of performance specifications on the valve's end motion. The proposed control design incorporates voltage constraints, nonlinear magnetic effects, and various motion planning requirements. A flat output acts as a design parameter and is parameterized with a spline basis. A nonlinear feasibility problem is solved to obtain optimal spline coefficients such that performance requirements are met. The resulting flat output provides an open-loop control which is augmented with feedback so that a linear stable tracking error system results. The proposed control scheme is demonstrated in simulation and on an experimental testbed. The performance of a proportional-integral controller is compared experimentally to the flatness-based method

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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

Citations68
Published2007
Admission routes1
Has abstractyes

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