Finite-Iteration Learning Tracking Control of Magnetic Shape Memory Alloy Actuator Based on Neural Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hysteresis is the key factor affecting the positioning accuracy of the magnetic shape memory alloy-based actuator (M-BA). In this paper, we investigate the finite-iteration tracking control problem of M-BA using neural network technology. Firstly, a neural network-based iterative learning control strategy is developed for finite-iteration tracking of a discrete non-affine system, where the contraction mapping principle is employed to establish the relation between tracking error and the iteration bound, thereby determining the settling iteration. Then, incorporating the mathematical induction and the data-driven methods, a sufficient condition for system convergence is provided. Finally, experiments are conducted to validate the effectiveness of the proposed method and the correctness of the theory. This study contributes to improving the positioning accuracy of the M-BA, providing insights into its potential applications in electromagnetic drive and precision electromechanical systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.000 | 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