Inverse control of a class of nonlinear systems with modified generalized Prandtl-Ishlinskii hysteresis
Bibliographic record
Abstract
The exhibition of hysteresis effects in smart actuators highly affects the accuracy and stability of the control systems. In this work, a modified generalized PI (MGPI) model is proposed to describe a more general class of hysteresis shapes. Comparing to the previous works, the MGPI model not only enlarges the application of the PI model, but also makes it possible to derive the analytical inverse model. The inverse MGPI model can be used as a compensator to mitigate the hysteresis effect in the control systems. Furthermore, in order to minimize the inverse compensation error due to the modeling inaccuracy and to achieve the closed-loop stability and tracking precision, an adaptive variable structure controller is designed. The simulation results show that the proposed controller consisting of both the inverse compensator and adaptive controller has superior control performance comparing with the adaptive controller itself.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".