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Record W2765803143 · doi:10.1080/00207179.2017.1381346

Adaptive fault-tolerant control of air-breathing hypersonic vehicles robust to input nonlinearities

2017· article· en· W2765803143 on OpenAlexafffund
Hao An, Barış Fi̇dan, Jianxing Liu, Changhong Wang, Ligang Wu

Bibliographic record

VenueInternational Journal of Control · 2017
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooHarbin Institute of TechnologyChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsControl theory (sociology)Fault toleranceAerodynamicsTransient (computer programming)Hypersonic speedActuatorAdaptive controlController (irrigation)Control engineeringBacklashComputer scienceEngineeringControl (management)Aerospace engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper designs a fault-tolerant adaptive controller for air-breathing hypersonic vehicles (AHVs) subject to modelling parameter uncertainties, external disturbances, and actuator nonlinearities of saturation and backlash. The proposed adaptive control scheme is able to compensate the effects of actuator saturation by utilising the states of five auxiliary dynamic systems, which are driven by the differences between the nominal and saturated input signals. Additionally, the effects of control surfaces on the aerodynamic force and moment, which are commonly neglected by the existing adaptive control designs, can be well handled. Transient tracking performance is explicitly derived in terms of L2 norms of the tracking errors. The final control scheme is obtained in a direct form, which makes its implementation more practical compared with other adaptive controllers for AHVs. The effectiveness of the proposed control scheme is demonstrated by numerical simulations.

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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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

Citations42
Published2017
Admission routes2
Has abstractyes

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