Practically adaptive output tracking control of inherently nonlinear systems preceded by unknown hysteresis
Bibliographic record
Abstract
Control of nonlinear systems preceded by unknown hysteresis nonlinearities is usually difficult and challenging due to the nonsmooth and memory characteristics of hysteresis. Focusing on a class of inherently nonlinear systems and with use of available mathematical models of hysteresis nonlinearities, this paper addresses the challenge on how to fuse available hysteresis models with those results for the inherently nonlinear systems to achieve practically adaptive output tracking control. We will show such a possibility by combining the recently developed framework of Immersion and Invariance (I&I) tools, adding a power integrator technique, and Prandtl-Ishlinshii hysteresis model. The proposed approach has the following two features. First, in order to mitigate the effects of the unknown hysteresis, the proposed approach does not necessarily need to construct a hysteresis inverse; secondly, the adaptive mechanism does not have to satisfy the certainty equivalence principle. It is shown that the developed controller ensures all signals of closed-loop systems are bounded while practically keeping the output tracking error to an arbitrary small neighborhood of the origin.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".