Reinforcement Learning Solution with Costate Approximation for a Flexible Wing Aircraft
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Bibliographic record
Abstract
An online adaptive learning approach based on costate function approximation is developed to solve an optimal control problem in real time. The proposed approach tackles the main concerns associated with the classical Dual Heuristic Dynamic Programming techniques in uncertain dynamical environments. It employs a policy iteration paradigm along with adaptive critics to implement the adaptive learning solution. The resultant framework does not need or require prior knowledge of the system dynamics, which makes it suitable for systems with high modeling uncertainties. As a proof of concept, the suggested structure is applied for the auto-pilot control of a flexible wing aircraft with unknown dynamics which are continuously varying at each trim speed condition. Numerical simulations showed that the adaptive control technique was able to learn the system's dynamics and regulate its states as desired in a relatively short time.
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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 | 0.000 | 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 | 0.001 |
| 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 it