MétaCan
Menu
Back to cohort
Record W1912478428

Simulation of non-linear flight control using backstepping method

2015· article· en· W1912478428 on OpenAlexaff
Édouard Finoki, Vahé Nerguizian, Maarouf Saad

Bibliographic record

VenueEspace ÉTS (ETS) · 2015
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsBacksteppingControl theory (sociology)ElevatorLinearizationNonlinear systemPID controllerComputer scienceStability derivativesAerodynamicsLyapunov functionControl engineeringEngineeringAdaptive controlControl (management)Artificial intelligenceAerospace engineering
DOInot available

Abstract

fetched live from OpenAlex

This paper describes the design and the simulation of a non-linear controller for an aircraft using the backstepping method. The aim is to find the expressions of the elevator deflection in order to control the flight path angle. Backstepping controller uses the non-linear equations of motion of an aircraft, the Lyapunov analysis and the errors between the real and the desired values. The advantage of the backstepping method is to work with cascaded structures. Compared to the PID method, there is no need of tuning gains to ensure the stability. Furthermore, compared to the dynamic inversion there is no linearization and no approximations of the system; it works with the true non-linear system using virtual controls. Compared to other works, this paper deals with very accurate equations of motion and a very detailed non-linear coefficient aerodynamic model. This technique does not control only the angle of attack or the pitch Euler angle but particularly the flight path angle allowing a steady, climb or descent flight. The controller has been implemented in Matlab/Simulink and FlightGear.

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 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)0.001
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 score0.898
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.036
GPT teacher head0.309
Teacher spread0.273 · 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 teacher head, 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueEspace ÉTS (ETS)Same topicAdaptive Control of Nonlinear SystemsFrench-language works237,207