Fractional-Order Integral Neural-Adaptive Control of Nonlinear Input-Affine Systems
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Bibliographic record
Abstract
Long decaying memory is a trademark of fractional calculus operations. These can be incorporated in the feedback and training laws of neural-adaptive controllers; the adaptive laws for feedforward and transform matrix artificial neural networks (ANNs) inherit historical errors. Thus, requiring an analysis of such a scheme on different control problems over prolonged executions; this enables the ability to observe the interactions between ANNs (feedforward and transform matrices) and fractional-order integral (FOI), as both are adaptive memory functions. Moreover, Lyapunov stability methods paved the way to incorporate FOI in feedback and adaptive laws for nonlinear input-affine dynamical systems. A planar 2-degree-of-freedom serial manipulator executes two control problems: task-space trajectory tracking and hybrid force-position control, in separate simulations. The proposed FOI-based method provides significantly better results than a non-FOI baseline method while remaining stable over prolonged cycles.
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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