Model Reference Control including Adaptive Inverse Hysteresis for Systems with Unknown Input Hysteresis
Bibliographic record
Abstract
Control of linear systems with unknown input hysteresis is a challenging task and is receiving increased attention in recent years. Many hysteresis models have been proposed in the literature, but the challenge is to determine how to integrate these models with available control techniques to ensure system stability. Such a possibility is by using the Krasnosel'skii-Pokrovkii (KP) hysteresis model. After establishing an off-line KP approximate model of the unknown hysteresis, an inverse KP hysteresis model can be constructed to partially eliminate the hysteresis effects. To combine the model reference control methodology with the inverse hysteresis model, the relationship between system tracking error and parameter errors of the modeled hysteresis is derived, and then an adaptive control algorithm is developed to update the model parameters to ensure that the tracking error asymptotically converges to zero. The approach is illustrated and verified through simulations performed on a linear plant.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".