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Record W2877731124 · doi:10.1109/yac.2018.8406409

3D gliding guidance for an unpowered RLV in the TAEM phase

2018· article· en· W2877731124 on OpenAlexaff
Lingxia Mu, Xiang Yu, Ban Wang, Youmin Zhang, Xinmin Wang, Ping Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGuidance and Control Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsAerospace engineeringTerminal (telecommunication)Launch vehicleMode (computer interface)Phase (matter)Low altitudeEngineeringComputer scienceSimulationControl theory (sociology)Altitude (triangle)TelecommunicationsPhysics

Abstract

fetched live from OpenAlex

A sliding mode guidance strategy is developed for an unpowered reusable launch vehicle (RLV) during the terminal area energy management (TAEM) phase. The 3-DOF guidance model of a RLV in the domain of altitude is firstly provided, based on which a new sliding surface is constructed to account for multiple constraints on terminal conditions and dynamic pressure profile during flight. A second-order sliding mode guidance law is then developed to ensure the reaching of the sliding surface at a specific altitude. The simulation results confirm the effectiveness of the proposed method.

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)0.000
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

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 science0.0000.001
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 head0.029
GPT teacher head0.297
Teacher spread0.268 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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