Dynamic modeling for soft dielectric elastomer actuator considering different input frequencies and external loads
Bibliographic record
Abstract
A dynamic model for the soft dielectric elastomer actuator (SDEA) is developed in this paper to describe its intricately nonlinear behaviors considering different input frequencies and external loads. Firstly, the characteristics of the SDEA are observed by several groups of experiments. A phenomenological model is proposed to describe the asymmetric hysteresis behavior of the SDEA, which consists of a Prandtl-Ishlinskii model with one-side play operator and a dead-zone model with one-side dead-zone operator. Meanwhile, a mathematical model is built to depict the creep behavior of the SDEA. The dynamic model including a module and a linear system is proposed to further handle the rate-dependent and the stress-dependent hysteresis behaviors of the SDEA, in which the module is the superposition of the asymmetric hysteresis model and the creep model. To ensure that the inverse solution of the module is existing, as well as the linear system is controllable and observable, the constraint conditions of parameter values of the dynamic model are constructed. Next, the parameter identification is divided into two steps, and the differential evolution algorithm is employed in each step. Finally, the generalization of the proposed dynamics model is demonstrated by comparing the model output with the experimental data.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".