Dynamic Modeling and Focus Control of a Piezo-Actuated Liquid Tunable Lens
Bibliographic record
Abstract
The piezo-actuated liquid tunable lens exhibits complex characteristics, including electromechanical coupling, fluid dynamics, membrane and plate vibration, and hysteresis nonlinearities, which pose significant challenges to achieving precise focus control. To address these issues, this article proposes a dynamic model that integrates these factors into a unified framework for the piezo-actuated liquid tunable lens. The model employs a spring-damper-mass particle chain system to equivalently represent the coupled dynamics of the membrane-fluid-plate system in the liquid tunable lens. An equivalent circuit is used to capture the electromechanical coupling effect of the piezoelectric actuator (PEA). Furthermore, a discrete-form Duhem equation is introduced to model the hysteresis nonlinearities in the PEA. Using this dynamic model, a model-based parameter-tuned PID controller is implemented to achieve precise focus control of the lens. Experimental results demonstrate that the proposed theoretical model aligns well with experimental data, and the controller successfully achieves precise focus control of the piezo-actuated liquid tunable lens.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".