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Record W2578823554 · doi:10.2514/1.g002222

Gain-Scheduling Control of Flexible Aircraft with Actuator Saturation and Stuck Faults

2017· article· en· W2578823554 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Guidance Control and Dynamics · 2017
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsControl theory (sociology)AirspeedActuatorGain schedulingFlight control surfacesFault toleranceAeroelasticityEngineeringScheduling (production processes)Computer scienceControl systemControl engineeringAerodynamicsControl (management)Aerospace engineering

Abstract

fetched live from OpenAlex

In this paper, a fault-tolerant control design that can gain schedule with the airspeed is developed for a flexible aircraft with actuator saturation and stuck control surface faults. First, a flexible aircraft model that captures the coupling between rigid-body motions and flexible modes is presented. Then, a linear parameter-varying fault-tolerant controller featuring a proportional and integral structure is designed for a stuck control surface scenario, with control gains solved from a set of set-invariant conditions represented by linear matrix inequalities. The controller, which gain schedules with the airspeed, is able to eliminate the effects of stuck faults on the system output while minimizing the effects on other system states and guaranteeing no closed-loop performance degradation caused by actuator saturation. Finally, the effectiveness of the proposed design is demonstrated on an aircraft model with high-aspect-ratio wings in 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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.591

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)0.0000.000

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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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