Iterative Learning Control Based on Neural Network and Its Application to Ni-Mn-Ga Alloy Actuator With Local Lipschitz Nonlinearity
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Bibliographic record
Abstract
The inherent hysteresis property of Ni-Mn-Ga alloy material is the main reason that affects the positioning accuracy of Ni-Mn-Ga alloy-based actuator. This study proposes an iterative learning control based on feedforward neural network (ILCBFNN) to eliminate the effect of hysteresis on actuator positioning accuracy. In addition, the convergence analysis problem of the system that is subject to system irreversibility, local Lipschitz nonlinearity, and iteration-dependent uncertainty, is investigated. Specifically, ILC is combined with the FNN to improve the adaptability and performance of the ILC. The global Lipschitz-like condition is established using the principles of mathematical induction and contraction mapping. Then, the convergence of the ILC process is analyzed by studying the variation of tracking error along the iteration axis. The obtained convergence condition ensures that the tracking error converges to a small region proportional to the initial state error. Experimental results verify the feasibility of proposed ILCBFNN method.
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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 | 0.001 |
| 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