Adaptive neural network control for magnetic shape memory alloy actuator via improved Volterra model considering input saturation
Bibliographic record
Abstract
Abstract The typical characteristics of magnetic shape memory alloy (MSMA)-based actuator are rate-dependent and load-dependent hysteresis. This study first proposes an improved Volterra model to describe the hysteresis of the MSMA-based actuator. By combining the play operator with the hyperbolic tangent function as the exogenous input to the Volterra model, hysteresis is transformed from a multi-valued to a one-to-one mapping, while also improving the model’s ability to describe asymmetric hysteresis. Then, an adaptive control strategy based on a radial basis function neural network and the proposed model is employed to eliminate the effect of hysteresis on the positioning accuracy of the MSMA-based actuator. In the controller design, the impact of input saturation in the actual physical system on the controller performance is considered, and the Lyapunov theory is employed to demonstrate that the tracking error is asymptotically convergent. Finally, experimental studies verify the effectiveness of the proposed modeling and control schemes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".