Fractional-Order Integral Neural-Adaptive Control of Nonlinear Input-Affine Systems
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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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".