Aircraft Speed/Altitude Control Using a Sigma-Pi Neural Network
Bibliographic record
Abstract
– Performance and stability of small aircraft are sensitive to the onboard aerodynamic model of the flight controller. Due to aerodynamic uncertainty with excessive bias, scale factor, or noise affecting the plant, a small aircraft typically lacks the modeling accuracy required for designing a robust flight control system. These deficiencies can be overcome by implementing a traditional linear control law augmented with an adaptive nonlinear control law. A nonlinear dynamic inversion control law with feedback linearization is stable but lacks in performance when modeling uncertainty is introduced to the model. Compensating the traditional approach with a neural network to estimate the imperfect and unmodeled dynamics provides a robust control design, even for in-flight regimes not previously seen by the aircraft. In this research, we investigate using the Sigma-Pi Neural Network (SPNN) to adapt to both the engine speed and elevator commands in the aircraft speed/altitude control. Training the neural network model is performed using a recursive least square estimator (RLSE), and the SPNN control designs are validated on a six-degree-of-freedom (6DOF) digital simulation.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".