MétaCan
Menu
Back to cohort
Record W4382998669 · doi:10.1109/tcsii.2023.3291360

Adaptive Fuzzy Finite-Time Command Filtering Control for Flexible-Joint Robot Systems Against Multiple Actuator Constraints

2023· article· en· W4382998669 on OpenAlexaff
Shijia Kang, Peter Liu, Huanqing Wang

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2023
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsCarleton University
FundersDepartment of Education of Liaoning ProvinceNational Natural Science Foundation of China
KeywordsControl theory (sociology)Computer scienceActuatorFuzzy logicController (irrigation)Filter (signal processing)Compensation (psychology)Position (finance)Tracking errorNonlinear systemFuzzy control systemControl engineeringJoint (building)Tracking (education)Control (management)EngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This brief focuses on the issue of fuzzy finite-time position tracking control for single-link flexible-joint robotic systems subject to multiple actuator constraints. At first, fuzzy logic systems are invoked to estimate completely unknown nonlinear functions, which can appropriately overcome heavy calculations. Next, the inherent computational complexity problem is eliminated via adopting command filter technology and the correlative error compensation mechanism is exploited to mitigate the influence of the errors brought by the filter. Further, the developed controller not only assures the semi-global finite-time stable of the controlled system, but also makes the tracking error enter a small region around the origin within fast finite time. The significance and potential of the presented control technique can be testified through simulation results.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.230
Teacher spread0.192 · 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
GenreMethods

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

Citations14
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Circuits & Systems II Express BriefsSame topicAdaptive Control of Nonlinear SystemsFrench-language works237,207