A modified generalized Prandtl-Ishlinskii model and its inverse for hysteresis compensation
Bibliographic record
Abstract
Hysteresis nonlinearities are inherently exhibited in smart material based actuators. Many hysteresis models have been proposed in the literature to describe such hysteresis nonlinearities. Therein, the Prandtl-Ishlinskii (PI) model is getting more and more popular due to its unique analytical invertibility for the construction of its feedforward compensator. However, the Prandtl-Ishlinskii (PI) model suffers some limits and can only describe a certain class of hystereses. To extend to a more general class, a generalized Prandtl-Ishlinskii (GPI) model was developed. When the smart actuators are cascaded with plants, they usually generate the undesirable oscillations. In order to mitigate the hysteresis effects, its inverse is commonly constructed to compensate such effects. Though, the analytic inverse of the PI is well documented in the literature, the inverse for the GPI has only been listed. As the further development, the GPI is re-defined and a modified generalized Prandtl-Ishlinskii (MGPI) model is proposed which can still describe similar general class of hysteresis shapes. The benefit is that with the linear envelope function an analytical inverse hysteresis model can be derived for the purpose of compensation. The proposed approach is verified in both simulation and experiment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".