Robust inverse compensation and control of a class of non‐linear systems with unknown asymmetric backlash non‐linearity
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Bibliographic record
Abstract
A robust control approach with the inverse backlash compensation is presented for a class of non‐linear systems preceded by unknown asymmetric backlash non‐linearity. Firstly, the analytical expressions of the inverse compensation error for an asymmetric backlash are obtained by introducing new indicator functions, which make it possible to design a corresponding controller for the asymmetric input backlash. With the developed compensation error expression, conventional robust control approaches can be utilised to deal with such a non‐smooth non‐linear system. As an illustration, a robust adaptive control strategy is applied to demonstrate the approach. The developed control laws ensure the robust inverse compensation and achieve tracking within a desired accuracy. Finally, simulations performed on an unstable and uncertain non‐linear system illustrate and clarify the effectiveness of the developed approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it