An Iterative Learning Feedforward Controller for the TRIUMF e-linac
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
In the TRIUMF e-linac design, beam stability to within 0.1% within 10 μs in pulse mode is a design requirement. Traditional feedback control systems cannot respond within this time frame, so some form of feedforward control is needed. Even conventional feedforward may not be sufficient due to differences between the required feedforward signal and the actual beam-loading current. For this reason, an adaptive feedforward system using an iterative learning controller was developed for the e-linac LLRF. It can anticipate repetitive beam disturbance patterns by learning from previous iterations. The design and implementation of such a control algorithm is outlined, some simulation results are presented, and some preliminary test results with an actual cavity are illustrated.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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