Simulation of non-linear flight control using backstepping method
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".