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Record W2972211881 · doi:10.1109/taes.2020.2975525

Unified Backstepping Sliding Mode Framework for Airship Control Design

2020· preprint· en· W2972211881 on OpenAlexfundno aff
Henrique S. Vieira, Ely C. de Paiva, Sergio K. Moriguchi, José Reginaldo H. Carvalho

Bibliographic record

VenueIEEE Transactions on Aerospace and Electronic Systems · 2020
Typepreprint
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado do AmazonasConcordia UniversityFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsBacksteppingRobustness (evolution)Control theory (sociology)Sliding mode controlControl engineeringNonlinear systemComputer scienceMode (computer interface)TrajectoryBlock (permutation group theory)EngineeringControl (management)Adaptive controlArtificial intelligenceMathematicsPhysics

Abstract

fetched live from OpenAlex

This article presents a new kind of vectorial backstepping sliding mode control (BSMC) for the positioning and trajectory tracking of an autonomous robotic airship. Also, a unified framework basis for the design/analysis of vectorial BSMC, as well as sliding mode control and backstepping control for a system in lower triangular block form is derived. The design framework makes the theoretical-based comparative analysis of performances/robustness easier between the three nonlinear control approaches. Simulation results for the positioning and tracking of the autonomous airship illustrate the proposal.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.239
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

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

Citations0
Published2020
Admission routes1
Has abstractyes

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