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Record W2065722371 · doi:10.1109/syscon.2014.6819232

Model-free tuning solution for sliding mode control of servo systems

2014· article· en· W2065722371 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaAutoritatea Natională pentru Cercetare Stiintifică
KeywordsControl theory (sociology)Nonlinear systemSmoothingSliding mode controlServo controlServomechanismComputer scienceTrajectoryServomotorControl systemSIGNAL (programming language)ServoVariable structure controlControl engineeringEngineeringControl (management)Physics

Abstract

fetched live from OpenAlex

This paper suggests a model-free tuning solution for a sliding mode control system (SMCS) structure dedicated to servo systems. The new SMCS structure is viewed in the framework of reference trajectory tracking using a first-order nonlinear dynamic system as a local approximation of the process model. The sliding mode control signal augments the control signal specific to a model-free PI control system (CS) structure in order to compensate for the estimation errors which affect the systematic design and performance. The derivatives in the local approximation of the process model are estimated numerically using a Savitzky-Golay filter to carry out both differentiation and smoothing. A simple design approach is proposed for the SMCS structure. The real-time experimental results concerning the speed control of a laboratory nonlinear DC servo system prove the performance improvement of the SMCS structure against a model-free PI CS structure.

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.

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 score0.983
Threshold uncertainty score0.581

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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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

Quick stats

Citations23
Published2014
Admission routes2
Has abstractyes

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