Nonlinear Controller Design for Non-minimum Phase Flight System Enhanced by Adaptive Elevator Algorithm
Bibliographic record
Abstract
– Aircraft’s longitudinal dynamics is nonlinear and has a non-minimum phase characteristic when acceleration is used as feedback. Non-minimum phase system presents tracking and stability challenges in control design and performance. This paper shows how to convert the non-minimum phase longitudinal dynamics into a minimum phase system by using a new control equation referenced at the aircraft’s instantaneous center-of-rotation. The difference between the non-minimum phase and minimum phase systems is studied by using linear analysis. To compensate for the nonlinear aircraft dynamics, the nonlinear dynamic inversion is commonly applied to the minimum phase system. In this paper, the nonlinear dynamic inversion uses an elevator prediction algorithm for the compensation of the nonlinear aircraft dynamics. Two types of elevator prediction algorithms are designed and discussed. (1) Model-based prediction algorithm using nominal aircraft dynamics, (2) Data-based prediction algorithm using Sigma-Pi Neural Network (SPNN). Digital six-degree-of-freedom (6DOF) simulation validates the performance of the two elevator prediction algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".