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Record W3008607699 · doi:10.1049/iet-cta.2019.0349

Non‐fragile sliding mode control of discrete switched singular systems with time‐varying delays

2019· article· en· W3008607699 on OpenAlexaff
Yueqiao Han, Chun‐Yi Su, Yonggui Kao, Cunchen Gao

Bibliographic record

VenueIET Control Theory and Applications · 2019
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsConcordia University
FundersNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsControl theory (sociology)Discrete time and continuous timeSliding mode controlMode (computer interface)Computer scienceControl (management)MathematicsPhysicsNonlinear systemArtificial intelligence

Abstract

fetched live from OpenAlex

This study tends to solve the finite‐time boundedness (FTB) problems for discrete switched singular systems with time‐varying delays via a non‐fragile sliding mode approach. The remarkable feature of the provided method is that a new sliding surface function is constructed such that a full‐order dynamic system is acquired. Thus, a non‐fragile sliding mode controller can be proposed to guarantee the FTB of the dynamic system. Therein, by employing the multiple Lyapunov‐like functions and dwell time (DT) method, sufficient conditions and the DT of switching signal are given to ensure the FTB of the sliding mode dynamics. These results are then applied to the FTB issue, being expressed as a solvable optimisation problem. Furthermore, by employing a discrete reaching condition, the proposed sliding mode controller can guarantee the reachability of the quasi‐sliding mode. The validity of the proposed theorems is verified through a numerical example.

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)0.001
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.003
GPT teacher head0.194
Teacher spread0.191 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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