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Record W4389446572 · doi:10.1201/9781032678146

Refined Safety Control of Unmanned Flight Vehicles via Fractional-Order Calculus

2023· book· en· W4389446572 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEngineering
TopicGuidance and Control Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsOrder (exchange)AeronauticsControl (management)Fractional calculusComputer scienceCalculus (dental)Aerospace engineeringEngineeringMathematicsApplied mathematicsArtificial intelligenceMedicineBusiness

Abstract

fetched live from OpenAlex

The monograph explores the safety of unmanned flight vehicles via the corresponding fault-tolerant control design methods. The authors analyse the safety control issues of unmanned flight vehicles, which include finite-time recovery against faults, concurrence of actuator faults and sensor faults, concurrence of actuator faults and wind effects, and faults encountered by a portion of unmanned flight vehicles in a distributed communication network. In addition, the commonly used simple but effective proportional-integral-derivative structure is also incorporated into the safety control design for unmanned flight vehicles. By using the fractional-order calculus, the developed safety control results are able to ensure flight safety and achieve the refined performance adjustments against faults and wind effects. The book will be of interest to 3rd/4th year undergraduate students, postgraduate and graduate students, researchers, academic staff, engineers of aircraft and unmanned flight vehicles.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.001

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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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