Position control of a bulk liquid metal droplet based on a two-harmonic oscillator model
Bibliographic record
Abstract
Gallium-based liquid metal, as an emerging intelligent material, possesses excellent properties such as large surface tension and high electrical conductivity, making it promising for a wide range of applications in robotics. However, previous studies have primarily focussed on small spherical liquid metal droplets, leaving a gap in research on bulk liquid metal droplets (BLMDs). BLMDs exhibit more complex dynamic behaviours than smaller droplets. During their movement, they may experience oscillation, elongation until fracture, and even recoalesce after fracture. Consequently, the modelling and control of BLMDs pose certain challenges. To address these challenges, this paper proposes a position control method for BLMDs based on a two-harmonic oscillator model. This model is developed to capture the two main motion characteristics of BLMDs: translational motion and shape oscillation, which occur simultaneously due to their large volume. These characteristics lead to the compression and elongation of BLMDs like springs. The model parameters are identified using the nonlinear least squares algorithm. Based on this model, a position controller is designed, and the stability of the control system is proved via the Lyapunov method. The experimental results show the effectiveness of the proposed modelling and control approach.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".