A Virtual Cycle-based Iterative learning Control Framework for Repetitive System with Randomly Varying Initial State
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) has been considered a powerful strategy for repetitive process control. However, a fundamental assumption of conventional ILC is that each cycle must start from a predetermined fixed initial state. This assumption can be strict and challenging to achieve in real-world industrial applications. To address the issues arising from varying initial states, we propose an ILC framework that learns from a virtual cycle generated using historical data. We establish three conditions for generating the virtual cycle, and theoretical results demonstrate guaranteed convergence. To ensure the practicality of our framework, we relax one of the conditions, enabling the virtual cycle to be generated by solving a convex optimization problem. The effectiveness of our framework in improving control performance is verified through an injection molding example.
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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) | 0.001 | 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 | 0.001 |
| 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