Modeling and control of piezoelectric inertia–friction actuators: review and future research directions
Bibliographic record
Abstract
Abstract. This paper provides a comprehensive review of the literature regarding the modeling and control of piezoelectric inertia–friction actuators (PIFAs). Examples of PIFAs are impact drive mechanisms (IDMs) and friction-driving actuators (FDAs). In this paper, the critical challenges are first identified in modeling and control of PIFAs. Second, a general architecture of PIFAs is proposed to facilitate the analysis and classification of the literature regarding modeling and control of PIFAs. This general architecture covers all types of PIFAs (e.g., FDAs, IDMs) and thus serves as a general conceptual model of PIFAs. There is an additional benefit with this general architecture of PIFAs, namely that it is conducive to innovation in PIFAs, as new specific PIFAs may be designed in order to tailor to a specific application (for example, both FDAs and IDMs are viewed as specific PIFAs). Finally, the paper presents future directions in modeling and control for further improvement of the performance of PIFAs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".