High-precision control of piezoelectric nanopositioning stages using hysteresis compensator and disturbance observer
Bibliographic record
Abstract
This paper proposes a novel high-performance control scheme with hysteresis compensator and disturbance observer for high-precision motion control of a nanopositioning stage driven by a piezoelectric stack actuator (PSA). In the developed control scheme, a real-time inverse hysteresis compensator (IHC) with the modified Prandtl-Ishlinskii model is firstly designed to compensate for the asymmetric hysteresis nonlinearity of the PSA. Due to the imperfect compensation, the dynamics behaviors of the PSA-actuated stage with the IHC can be treated as a linear dynamic system plus a lumped disturbance term. Owing to the unknown nature of this lumped disturbance term, a disturbance observer (DOB) is used as a means for disturbance rejection. With the DOB, a tracking controller is finally designed and implemented to stabilize the position error. To verify the proposed control scheme, a real-time experimental platform with a PSA-actuated nanopositioning stage is built, and extensive experimental tests are performed. The comparative experimental results demonstrate the effectiveness and improved performance of the developed control approach in terms of the maximum-value errors, root-mean-square-value errors and hysteresis compensation.
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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) | 0.000 | 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".