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Record W4221063097 · doi:10.1177/10775463211070901

Event-based fast terminal sliding mode control design for a class of uncertain nonlinear systems with input delay: A quantized feedback control

2022· article· en· W4221063097 on OpenAlexaff
Mobin Saeedi, Jafar Zarei, Roozbeh Razavi‐Far, Mehrdad Saif

Bibliographic record

VenueJournal of Vibration and Control · 2022
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsControl theory (sociology)Nonlinear systemComputer scienceInverted pendulumRobustness (evolution)Terminal sliding modeSliding mode controlControl (management)

Abstract

fetched live from OpenAlex

This paper deals with the problem of regulation and tracking tasks for nonlinear dynamic systems that use a network medium to transmit state measurements. A novel quantized event-triggered fast terminal sliding mode controller (SMC) is proposed to reduce communication resources and computation loads while increase robustness against packet dropout, uncertainties, and disturbances. Then, new criteria are defined for the dynamic quantizer based on the event-triggering error, which increases the accuracy and facilitates the implementation procedures. In practice, delay-free systems are not realistic considerations, thus, their stability is analyzed in the presence of a dynamic quantizer and input delay under the proposed controller scheme. Then, the minimum inter-sampling time is derived, which guarantees the Zeno-free behavior and provides information about the cyber layer bandwidth in cyber-physical systems. Finally, three simulations on an unstable numerical model, the model of an inertia wheel inverted pendulum, and the mass-spring-damper model validate the effectiveness of the proposed methodology.

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)0.002
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 score0.941
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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