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Record W2067018100 · doi:10.1109/isie.2006.295607

Sliding Mode Nonlinear Switching Functions for Control Input Transient Constraints Reduction

2006· article· en· W2067018100 on OpenAlexaff
Charles Fallaha, Maarouf Saad, Hadi Y. Kanaan, Wen-Hong Zhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsCanadian Space AgencyÉcole de Technologie Supérieure
Fundersnot available
KeywordsControl theory (sociology)Sliding mode controlNonlinear systemTransient (computer programming)Reduction (mathematics)Controller (irrigation)Transient responseComputer scienceNonlinear controlVariable structure controlControl engineeringControl (management)EngineeringMathematicsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper introduces a new approach based on the use of nonlinear switching functions in sliding mode control. These nonlinear functions are used to eliminate transient constraints on the control input and therefore to increase the speed performance of the controller without having a negative effect on the control input. This approach is brought up in a general perspective to nth order single input systems. In order to validate the superiority of this novel approach over the conventional sliding mode control, simulation results of a current-controlled magnetic levitation system are compared for both the conventional and the new techniques

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: none
Teacher disagreement score0.861
Threshold uncertainty score0.418

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 communication0.0000.000
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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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

Citations5
Published2006
Admission routes1
Has abstractyes

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