Fractional-Order Integral Neural-Adaptive Update and Feedback Laws
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Bibliographic record
Abstract
A neural-adaptive controller with fractional-order integral (FOI) feedback and update laws is developed to enhance tracking performance and response to uncertainties. Feedback control law with an FOIs react quickly to uncer-tainties, such as biases, which complement slow-responding artificial neural networks (ANN). Adaptive ANNs are further enhanced by including FOI in network training, improving convergence speed and depth. Lyapunov stability methods enabled the creation of the proposed method generalized on a nonlinear direct-input system. Simulated quad copter and serial manipulator systems utilize the proposed method to follow trajectories in their respective spaces. The proposed controller significantly enhances performance and adaptive capabilities while remaining stable over multiple execution cycles relative to baseline non-FOI adaptive methods.
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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 | 0.000 |
| 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 it