Practical, Model-Free, Completely Robust Learning Control using Reversed Time Input Runs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Iterative learning control can be used to produce high precision tracking of a desired trajectory, learning to do so by iteratively adjusting the command based on errors observed in previous runs. There are applications in spacecraft to produce high precision scanning motions. Learning control can be very sensitive to having an accurate system model all the way to Nyquist frequency. Reversed time runs are used here to have the hardware produce a compensator that stabilizes the learning, instead of relying on a model. This means that there is no such thing as model error, and the learning control is completely robust. This work builds on previous work by two of the authors, and develops methods to deal with finite time effects, repeating disturbances, methods to include a compensator to adjust the learning rate as a function of frequency, and methods to introduce a cutoff filter to prevent requiring large control actions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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