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Record W2143089809 · doi:10.5539/mas.v7n1p28

Moving Mass and Reaction Compound Control System Design for Reentry Aircrafts

2012· article· en· W2143089809 on OpenAlexvenueno aff
Jun Zhou, Peng Lin

Bibliographic record

VenueModern Applied Science · 2012
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsnot available
Fundersnot available
KeywordsReentryControl theory (sociology)ThrustAerodynamicsTorqueImpulse (physics)Computer scienceActuatorController (irrigation)Attitude controlSimulationControl (management)EngineeringControl engineeringAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

Moving mass control (MMC) method is of benefit to reentry aircrafts in shape maintenance, energy consume reduction and load capability increase. However, as an aerodynamic manipulating method, MMC partially lacks ability to perform effectively during a whole airspace flight. To achieve reentry aircrafts control system design goal of high performance and low energy cost, we propose a moving mass and reaction compound control system design method in this paper. Firstly, dynamic models are established for a reentry aircraft actuated by moving mass and reaction compound control system, including MMC actuator models. Secondly, moving mass control periodic equivalent torque (MMCPET) is defined, and an optimal torque distribution compound control method based on MMCPET prediction is presented. The optimal object of torque distribution is minimizing difference from MMCPET and control torque command given by virtual controller. Finally, comparison simulations are performed to demonstrate validity of the proposed method. Simulation results show that the proposed compound control is more effective than moving mass control. Also, even if performance of compound control is nearly the same as that of reaction control, thrust impulse requirement of the former is close to 1/5 of the latter.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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 designBench or experimental
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

Citations0
Published2012
Admission routes1
Has abstractyes

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