Motion Control of Dielectric Viscoelastomer Actuator With Variable Load Based on Cerebellar Model Articulation Neural Network
Bibliographic record
Abstract
Dielectric viscoelastomer actuators (DVAs) possess humanlike muscle softness and large stretch, which have demonstrated great application potential in the field of soft biomimetic robots. At present, the high-precision motion control of the DVA is still challenging due to its complicated dynamic characteristics, especially when its load varies. To provide a feasible solution to this issue, this article presents a hybrid control architecture, which includes a cerebellar model articulation neural network (CMANN) and a proportional integral differential controller (PIDC). The CMANN is used as an inverse compensator to mitigate the complicated dynamic characteristics of the DVA and the PIDC is employed to enhance the control system's robustness. The proposed control architecture is validated experimentally via a DVA-based motion control platform. The experimental results demonstrate that the DVA can precisely track various reference trajectories even though its load varies during the control process, expanding applications of the DVA in emerging fields.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".