High‐order data‐driven optimal TILC approach for fed‐batch processes
Why this work is in the frame
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Bibliographic record
Abstract
A high‐order data‐driven optimal terminal iterative learning control (H‐DDOTILC) is proposed for fed‐batch processes, which are considered a general class of nonlinear and non‐affine systems. A new dynamical linearization is introduced to the iteration domain to reveal the relationship of system terminal output and control input among batches. The proposed H‐DDOTILC consists of a high‐order learning control law, an iterative parameter estimator, and a rest algorithm, together. The learning control law with a high‐order form is capable of utilizing more control knowledge of the previous l batches to improve control performance. The parameter updating law is used to estimate the unknown derivatives of the nonlinear system to control input, which is the main part of the nonlinear learning gain function of the control law. Essentially, the proposed approach is a data‐driven control strategy, and the controller design and analysis only depend on the I/O data of the plant, which is a distinct feature for the control problems of practical nonlinear and non‐affine systems. Both the rigorous analysis and the simulation results illustrate the applicability and effectiveness of the proposed approach.
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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 | 0.001 |
| 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 | 0.001 | 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