Discrete-Time Elasto-Plastic Friction Estimation
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Bibliographic record
Abstract
For control applications involving small displacements and velocities, friction modeling and compensation can be very important, especially around velocity reversal. We previously described single-state friction models that are based on elasto-plastic presliding, something that reduces drift while preserving the favorable properties of existing models (e.g., dissipativity) and that provide a comparable match to experimental data. In this paper, for this class of models, discrete estimation for friction force compensation is derived. The estimator uses only position and velocity (not force) measurements and integrates over space rather than time, yielding a discrete-time implementation that is robust to issues of sample size and sensor noise, reliably renders static friction and is computationally efficient for real-time implementation. Boundedness with respect to all inputs, convergence during steady sliding and dissipativity are established for the discrete-time formulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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 it