Observations on Causal Iterative-Learning-Control & Transients
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 (ILC) is a technique for adaptive feed-forward control of electro-mechanical plant. This paper and companion [7] deals with ILC behaviours encountered before the widespread adoption of Q-ILC, the quadratic optimization. This paper explains, for the first time, the structural causes of “bad learning transients” for causal learning in terms of the cumulant iteration matrix - which can only be constructed by the method of forward-substitution. This paper underscores the importance of the linear weighted-sums of the column elements of the iteration matrix, and their relation to the convergence of sum of squares and Parseval's theorem. These criteria have the advantage that no model is required; the measured impulse response is sufficient information. Finally, the paper reminds readers that there are also wave-like (soliton) solutions of the ILC equations that may occur even when all convergence criteria are satisfied.
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) | 0.000 | 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 | 0.001 |
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