Towards Developing a Simple Lumped Parameter-based State Estimator for PneuNets
Bibliographic record
Abstract
Soft actuators (SAs) are used for gripping and manipulating activities in various industrial applications and different designs of SAs are developed to improve their efficacy. In these designs, compliance materials are used to control the morphology of SAs to generate different movements. Specifically, PneuNets design is one of the popular designs and it uses the pressure difference in their internally networked chambers to control the movements. These chambers act as energy storing and releasing elements during pressurizing and de-pressurizing stages and play an important role in controlling motion output. These discrete chambers are usually networked and continuum therefore, they are difficult to model and integrate into control systems. The present paper attempts to use a hypothetical disc model to estimate the real-time states of PneuNets. The proposed second-order model has a single stiffness element (K) and a single damping element (B) that is assumed to be approximately equal to the change of bending angle of the PneuNet. These K and B values are obtained experimentally and the results were compared against finite element simulations. The model was tested for step/sine inputs and resulted in an average root-mean-square-error approximately 4%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".