Compound Estimation-Based Output-Feedback Hysteresis Compensation and Sensor Fault-Tolerant Control Strategy: Application to Piezoelectric Micropositioning Stage
Bibliographic record
Abstract
A piezoelectric micropositioning stage (PMPS), which is a type of piezoelectric-driven system, exhibits hysteresis characteristics that affect its positioning precision. In addition, the system is sensitive to disturbances, such as environmental noise, system uncertainties, and sensor uncertainties or faults. This study investigated a new hysteresis-compensation fault-tolerant control strategy for a PMPS. To compensate for the hysteresis caused by the piezoelectric actuator, an observer-based hysteresis compensator (HC) was proposed to avoid the need to model the hysteresis and calculate the hysteresis inverse. Subsequently, an extended state observer (ESO) was constructed to simultaneously estimate the unmeasurable states and generalized disturbances, including the HC compensation error, external disturbances, and uncertainties. The HC and ESO constitute a new compound estimation method to further enhance estimation performance. Moreover, to address sensor faults, a reconstruction sensor was developed to reconstruct the feedback loop and mask faults. Furthermore, a Nussbaum function was incorporated into the fault-tolerant controller to address the unknown actuator gain caused by hysteresis and hence avoid singularities that could occur in the controller design because of the adaptive estimation of the unknown gain. Finally, comparative experiments were conducted on the PMPS to demonstrate the effectiveness of the developed control method.
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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 | 0.001 |
| 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".