Self-sensing tracking control for piezoelectric actuators based on sensor fusion
Bibliographic record
Abstract
Piezoelectric actuators are a popular choice in micro- and nano-positioning devices. Traditional sensorless position control approaches use a hysteresis mapping between voltage and position in a voltage feedforward control scheme. However, this mapping is affected by frequency, temperature, aging etc. Recently, charge control for positioning is also attracting interest among researchers due to the linear relationship between position and charge. Conversely, a sophisticated hardware design is required to minimize charge drift. This limits charge based controllers for practical applications. In this study, a new self-sensing control technique is proposed which requires neither an accurate inverse mapping nor a sophisticated charge controller. This technique uses a position estimate that is obtained by fusing a traditional charge based position measurement with a novel capacitance based position measurement. Upon achieving a reliable position estimate, it is shown that a traditional PI control scheme is sufficient for tracking applications. Different wave forms having multiple lifts and rates were tested. Error is reduced upto 75% using a self-sensing feedback control when compared to open loop actuation. These results compare well with traditional self-sensing control techniques found in literature.
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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) | 0.001 | 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".