Neural Network Based Iterative Learning Control for Dynamic Hysteresis and Uncertainties in Magnetic Shape Memory Alloy Actuator
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Bibliographic record
Abstract
Magnetic shape memory alloy-based actuator (MSMA-BA) is constructed based on the strain mechanism of MSMA material and the magnetic effect of electric current. It can generate macroscopic deformation with micro-nano scale resolution. However, the rate-dependent and load-dependent hysteresis characteristics in MSMA-BA will reduce the positioning accuracy and hinder its applications. In this study, a long short-term memory (LSTM)-based U model with exogenous inputs is proposed to describe the complex dynamic hysteresis characteristics. Then, an LSTM-based iterative learning control (ILC) scheme is proposed to realize the reference trajectory tracking control of the MSMA-BA. Additionally, a dynamic expansion compression factor (DECF) is introduced in the controller to accelerate the convergence speed of system. The convergence of the proposed LSTM-based ILC scheme is analyzed with the consideration of state uncertainty, output disturbance, and the initial state error. It will promote the further applications of ILC in practical situations. Experiments are carried out on MSMA-BA to validate the effectiveness of the proposed method. The experimental results indicate that the proposed modeling and control methods exhibit excellent performance.
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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 | 0.001 | 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