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Pitch Tracking for an Airship with Moving Gondola Using Backstepping Control

2020· article· en· W3017019958 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
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBacksteppingTracking (education)Computer scienceAerospace engineeringAeronauticsControl (management)Control theory (sociology)Control engineeringEngineeringAdaptive controlArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presented a backstepping approach for trajectory tracking control of an airship using a moving gondola as a control input. The gondola and thrusters can travel from the mid-rear of helium envelop to the front via a curved rail thereby altering the location of the center of gravity (CG) with respect to the center of volume (CV). The dynamic equations of airship is derived using the Newton- Euler method, and the model was implemented and simulated in Matlab/Simulink to illustrate the effectiveness of the designed controller for tracking pitch trajectories in the presence of uncertainty and wind disturbances. The global asymptotically stability is proved by Lyapunov stability analysis. The results show the effectiveness of the CG control for pitch and altitude tracking.

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: none
Teacher disagreement score0.751
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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 head0.039
GPT teacher head0.216
Teacher spread0.176 · 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
Published2020
Admission routes1
Has abstractyes

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