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Record W1990479381 · doi:10.1109/acc.2010.5531585

Adaptive fuzzy sliding mode control design for laser metal deposition

2010· article· en· W1990479381 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsControl theory (sociology)Sliding mode controlRobustness (evolution)Fuzzy logicAdaptive controlComputer scienceFuzzy control systemControl engineeringRobust controlControl systemEngineeringNonlinear systemControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

This paper presents design and implementation of a novel adaptive fuzzy sliding mode control (AFSMC) scheme and its application to the height control of laser metal deposition process (LMD). Because of the uncertainties in modelling, and imperfections in switching devices or delays, the standard sliding mode control (SMC) cannot be used due to chattering. In order to eliminate the chattering, the control scheme proposed in this paper uses an adaptive fuzzy inverse dynamic model of the LMD process constructed from the input-output data, and an auxiliary continuous proportional-integral type control law constructed using the algebraic distance of trajectories from the switching surface. The simulation and experimental studies are presented to verify the performance and robustness of the controller and its ability in eliminating the chattering, and tracking the different trajectories. The overall performance, on-line learning capability and the disturbance attenuation property of the proposed control scheme makes it suitable for industrial applications.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.943
Threshold uncertainty score0.545

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 head0.013
GPT teacher head0.224
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

Quick stats

Citations5
Published2010
Admission routes1
Has abstractyes

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