A Dynamic Pole Motion Approach for Control of Nonlinear Hybrid Soft Legs: A Preliminary Study
Bibliographic record
Abstract
Hybrid soft leg systems have been studied for advanced gaits of soft robots. However, it is challenging to analyze and control hybrid soft legs due to their nonlinearity. In this study, we adopted dynamic pole motion (DPM) to analyze stability of a nonlinear hybrid soft leg system with dynamic Routh’s stability criterion and to design a proper controller for the nonlinear system with an error-based adaptive controller (E-BAC). A typical hybrid soft leg system was taken as an example, as such a system can easily become unstable and needs a controller to get the system back to a stable state. Specifically, E-BAC was designed to control the unstable hybrid soft leg fast with a minimal overshoot. As a nonlinear controller, the implanted E-BAC in a feedback control system includes two dominant dynamic parameters: the dynamic position feedback Kpe,t and the dynamic velocity feedback Kve,t. These parameters were properly selected, and the feedback was continuously varying as a function of system error et, exhibiting an adaptive control behavior. The simulation shows that this approach for constructing an adaptive controller can yield a very fast response with no overshoot.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".