Iterative learning strategy for a class of nonlinear controllers applied to constrained batch processes
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Bibliographic record
Abstract
In this paper, we apply an iterative learning strategy to improve the performance of a class of nonlinear controllers, when they are applied to constrained batch processes. The idea is to exploit the control error information from the previous batches so that the corrected control inputs will iteratively improve the control performance. In this iterative learning scheme, we provide the convergence proof of the feed-forward input correction strategy as the batch cycle progresses. Furthermore, we extend the proposed strategy for handling input constraints, which in some cases the constraints may result in an accumulated error during the iteration process. To deal with this problem, we propose a segmented reference trajectory, where the learning strategy is applied for each segment with the assumption that a smooth transition between segments is established. Throughout the paper, a batch reactor control problem is used to illustrate how the proposed methods work in practice.
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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) | 0.001 | 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 | 0.000 |
| 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