Hysteresis-Estimator-Based Adaptive Fuzzy Control for Piezoelectric Micro-Positioning Stage With Time-Varying Output Constraints
Bibliographic record
Abstract
This brief addresses the high-precision motion control of piezoelectric micro-positioning stages (PMPSs) subject to time-varying output constraints, input hysteresis nonlinearity, and system uncertainties. The key features of the developed hysteresis-estimator-based adaptive fuzzy control (HEAFC) method are as follows. First, an asymmetric rate-dependent hysteresis operator is used to construct an extended fuzzy input space, enabling a fuzzy dynamic hysteresis estimator (FDHE) for real-time hysteresis estimation via adaptive fuzzy logic. Second, auxiliary functions are embedded into a backstepping-like control framework to explicitly handle time-varying output constraints. Moreover, by leveraging the structural characteristics of fuzzy systems, the HEAFC scheme avoids repeated differentiation or filtering of virtual control laws. This feature can substantially simplify the control structure. The HEAFC method guarantees prespecified constraint satisfaction and high-accuracy trajectory tracking. Lyapunov-based analysis ensures closed-loop stability, and experimental results on a PMPS demonstrate the effectiveness of the HEAFC strategy.
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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 | 0.001 | 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".