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Record W4398250458 · doi:10.1109/tie.2024.3393153

Finite-Time Control Design for a Coaxial Tilt-Rotor UAV

2024· article· en· W4398250458 on OpenAlexaff
Zongyang Lv, Qing Zhao, Xi‐Ming Sun, Yuhu Wu

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Control Systems
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsCoaxialTilt (camera)Rotor (electric)Control theory (sociology)Computer scienceControl (management)EngineeringPhysicsAutomotive engineeringElectrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Coaxial tilt-rotor (CTR) unmanned aerial vehicle (UAV) is a system with the specific configuration of two CTR modules and a rear-rotor module. The CTRUAV is a typical strongly coupled, nonlinear, and underactuated system whose control performance is always adversely affected by various disturbances. To realize robust, stable, and efficient flight control for the CTRUAV is a challenging task. A multivariable cascaded finite-time (FT) controller and an improved nonlinear control allocation law with variable parameters are proposed based on the particular configuration of the CTRUAV. Finally, the superiority and the relationship between the control performance of the proposed control scheme and the control parameters are demonstrated through simulations and real flight experiments. The results show that the proposed control system significantly improves the robustness, transient performance, stability, and efficiency of the CTRUAV. In the flight tests, the root mean square error of the velocity is reduced by 28% in the presence of external disturbance, and the settling time of the attitude is shortened up to 55.8% in tracking the step signal.

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 score0.011

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 studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.213
Teacher spread0.193 · 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

Citations40
Published2024
Admission routes1
Has abstractyes

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