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Record W2794585060 · doi:10.1016/j.cja.2018.03.018

Robust adaptive fault-tolerant control of a tandem coaxial ducted fan aircraft with actuator saturation

2018· article· en· W2794585060 on OpenAlexaff
Xiaoliang Wang, Changle Xiang, Homayoun Najjaran, Bin Xu

Bibliographic record

VenueChinese Journal of Aeronautics · 2018
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsControl theory (sociology)ControllabilityActuatorController (irrigation)EngineeringAdaptive controlRobust controlCoaxialControl engineeringControl systemComputer scienceControl (management)Mathematics

Abstract

fetched live from OpenAlex

This paper is concerned with the robust adaptive fault-tolerant control of a tandem coaxial ducted fan aircraft under system uncertainty, mismatched disturbance, and actuator saturation. For the proposed aircraft, comprehensive controllability analysis is performed to evaluate the controllability of each state as well as the margin to reject mismatched disturbance without any knowledge of the controller. Mismatched disturbance attenuation is ensured through a structured H-infinity controller tuned by a non-smooth optimization algorithm. Embedded with the H-infinity controller, an adaptive control law is proposed in order to mitigate matched system uncertainty and actuator fault. Input saturation is also considered by the modified reference model. Numerical simulation of the novel ducted fan aircraft is provided to illustrate the effectiveness of the proposed method. The simulation results reveal that the proposed adaptive controller achieves better transient response and more robust performance than classic Model Reference Adaptive Control (MRAC) method, even with serious actuator saturation.

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 score0.006
Threshold uncertainty score0.013

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 science0.0000.000
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 head0.215
Teacher spread0.202 · 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

Citations27
Published2018
Admission routes1
Has abstractyes

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